diff --git a/004-NCBI/01-NCBI_overview.Rmd b/004-NCBI/01-NCBI_overview.Rmd index ec6fc6c..d93e130 100755 --- a/004-NCBI/01-NCBI_overview.Rmd +++ b/004-NCBI/01-NCBI_overview.Rmd @@ -2,6 +2,15 @@ **NOTE:** The following material was partially adapted by N. Brouwer from [Wikipedia](https://en.wikipedia.org/wiki/National_Center_for_Biotechnology_Information). See the underlying .Rmd file for information on specific paragraphs from Wikipedia. +## Key concepts + +* NCBI +* Rentrez +* accession numbers +* BLAST + +## NCBI + The **National Center for Biotechnology Information (NCBI)** is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It was founded in 1988 and is approved and funded by the government of the United States. The NCBI houses a series of **databases** relevant to the basic and applied life sciences and is an important resource for **bioinformatics** tools and services. Major databases include **GenBank** for DNA sequences and **PubMed**, a **bibliographic** database for biomedical literature. All these databases are available online through the **Entrez** search engine. @@ -12,7 +21,7 @@ In this chapter we'll briefly discuss the major databases, following up with spe The GenBank sequence database is an open access collection of publicly available DNA and protein sequences. If you've work with sequence data, you'll work with GenBank. GenBank is the actual database, and it can be searched several ways. For example, you can search for a sequence by its ID number (**accession number**) if you know it, or do a **BLAST search** using an actual sequence to look for similar sequences. -A key component of GenBank are the **GeneBank Records**, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene [pallysin](https://www.ncbi.nlm.nih.gov/protein/AAC65720) from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides **metadata**, such as the scientific name of the organism (*Treponema pallidum*), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene. +A key component of GenBank are the **GeneBank Records**, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene [pallysin](https://www.ncbi.nlm.nih.gov/protein/AAC65720) https://www.ncbi.nlm.nih.gov/protein/AAC65720 from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides **metadata**, such as the scientific name of the organism (*Treponema pallidum*), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene. A key feature of PubMed records is that they are **hyperlinked** to other NCBI databases. For example, you can click link under the name of the paper which reported the sequence of the gene and it will take you to the PubMed record for that paper (see below). You can also click the "Run BLAST" link and you can search the database for similar sequences. This protein coded for by this particular gene has had its structured solved using x-ray crystallography, and you can see these results under "Protein 3D Structure." In a later chapter we'll get to know these records in further detail. @@ -29,20 +38,18 @@ knitr::include_graphics("images/genbank_record.png") ## Entrez -https://en.wikipedia.org/wiki/Entrez - + -"Entrez is a federated search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name "Entrez" (a greeting meaning "Come in" in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI." +Entrez is a search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name "Entrez" (a greeting meaning "Come in" in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI. -"Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system." +Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system. ## BLAST -https://en.wikipedia.org/wiki/BLAST_(biotechnology) - + -"BLAST (basic local alignment search tool)is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence." +BLAST (Basic Local Alignment Search Tool) is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. diff --git a/004-NCBI/02-NCBI_genebank_fasta.Rmd b/004-NCBI/02-NCBI_genebank_fasta.Rmd index 0fa568f..175af0b 100755 --- a/004-NCBI/02-NCBI_genebank_fasta.Rmd +++ b/004-NCBI/02-NCBI_genebank_fasta.Rmd @@ -31,7 +31,7 @@ library(compbio4all) ## Introduction -**NCBI** is the National Center for Biotechnology Information. The [NCBI Webiste](www.ncbi.nlm.nih.gov/) is the entry point to a large number of databases giving access to **biological sequences** (DNA, RNA, protein) and biology-related publications. +**NCBI** is the National Center for Biotechnology Information. The [NCBI Webiste](https://www.ncbi.nlm.nih.gov/) www.ncbi.nlm.nih.gov/ is the entry point to a large number of databases giving access to **biological sequences** (DNA, RNA, protein) and biology-related publications. When scientists sequence DNA, RNA and proteins they typically publish their data via databases with the NCBI. Each is given a unique identification number known as an **accession number**. For example, each time a unique human genome sequence is produced it is uploaded to the relevant databases, assigned a unique **accession**, and a website created to access it. Sequence are also cross-referenced to related papers, so you can start with a sequence and find out what scientific paper it was used in, or start with a paper and see if any sequences are associated with it. @@ -47,7 +47,7 @@ In this chapter we'll typically refer generically to "NCBI data" and "NCBI datab Almost published biological sequences are available online, as it is a requirement of every scientific journal that any published DNA or RNA or protein sequence must be deposited in a public database. The main resources for storing and distributing sequence data are three large databases: -1. USA: **[NCBI database](www.ncbi.nlm.nih.gov/)** (www.ncbi.nlm.nih.gov/) +1. USA: **[NCBI database](https://www.ncbi.nlm.nih.gov/)** (www.ncbi.nlm.nih.gov/) 1. Europe: **European Molecular Biology Laboratory (EMBL)** database (https://www.ebi.ac.uk/ena) 1. Japan: **DNA Database of Japan (DDBJ)** database (www.ddbj.nig.ac.jp/). These databases collect all publicly available DNA, RNA and protein sequence data and make it available for free. They exchange data nightly, so contain essentially the same data. The redundancy among the databases allows them to serve different communities (e.g. native languages), provide different additional services such as tutorials, and assure that the world's scientists have their data backed up in different physical locations -- a key component of good data management! @@ -87,7 +87,7 @@ As mentioned above, for each sequence the NCBI database stores some extra inform To view the GenBank entry for the DEN-1 Dengue virus, follow these steps: -1. Go to the [NCBI website](www.ncbi.nlm.nih.gov) (www.ncbi.nlm.nih.gov). +1. Go to the [NCBI website](https://www.ncbi.nlm.nih.gov) (www.ncbi.nlm.nih.gov). 1. Search for the accession number NC_001477. 1. Since we searched for a particular accession we are only returned a single main result which is titled "NUCLEOTIDE SEQUENCE: Dengue virus 1, complete genome." 1. Click on "Dengue virus 1, complete genome" to go to the GenBank entry. diff --git a/004-NCBI/03-NCBI_seqdata_by_GUI1.Rmd b/004-NCBI/03-NCBI_seqdata_by_GUI1.Rmd index 5171afa..1084849 100755 --- a/004-NCBI/03-NCBI_seqdata_by_GUI1.Rmd +++ b/004-NCBI/03-NCBI_seqdata_by_GUI1.Rmd @@ -10,7 +10,7 @@ The following chapter was originally written by Avril Coghlan. It provides brie ## Retrieving genome sequence data via the NCBI website -You can easily retrieve DNA or protein sequence data by hand from the [NCBI](www.ncbi.nlm.nih.gov) Sequence Database via its website www.ncbi.nlm.nih.gov. +You can easily retrieve DNA or protein sequence data by hand from the [NCBI](https://www.ncbi.nlm.nih.gov/) Sequence Database via its website www.ncbi.nlm.nih.gov. Dengue DEN-1 DNA is a viral DNA sequence and its NCBI **accession number** is NC_001477. To retrieve the DNA sequence for the Dengue DEN-1 virus from NCBI, go to the NCBI website, type “NC_001477” in the Search box at the top of the webpage, and press the “Search” button beside the Search box. diff --git a/004-NCBI/04-uniprot_by_GUI-AC07-01.Rmd b/004-NCBI/04-uniprot_by_GUI-AC07-01.Rmd index f1edc56..c5eed0a 100755 --- a/004-NCBI/04-uniprot_by_GUI-AC07-01.Rmd +++ b/004-NCBI/04-uniprot_by_GUI-AC07-01.Rmd @@ -22,7 +22,7 @@ In a previous vignette you learned how to retrieve sequences from the NCBI datab As mentioned previously, a subsection of the NCBI database called **RefSeq** consists of high quality DNA and protein sequence data. Furthermore, the NCBI entries for the RefSeq sequences have been **manually curated**, which means that expert biologists employed by NCBI have added additional information to the NCBI entries for those sequences, such as details of scientific papers that describe the sequences. -Another extremely important manually curated database is [**UniProt**](www.uniprot.org), which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. +Another extremely important manually curated database is [UniProt](https://www.uniprot.org/) www.uniprot.org, which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. For each protein in UniProt, the UniProt curators read all the scientific papers that they can find about that protein, and add information from those papers to the protein’s UniProt entry. For example, for a human protein, the UniProt entry for the protein usually includes information about the biological function of the protein, in what human tissues it is expressed, whether it interacts with other human proteins, and much more. All this information has been manually gathered by the UniProt curators from scientific papers, and the papers in which the found the information are always listed in the UniProt entry for the protein. @@ -42,7 +42,7 @@ This tells us that *Mycobacterium* is a species of bacteria, which belongs to a Back up at the top under "organism" is says "Status", which tells us the **annotation score** is 2 out of 5, that it is a "Protein inferred from homology", which means what we know about it is derived from bioinformatics and computational tools, not lab work. -Beside the heading “Function”, it says that the function of this protein is that it “Removes the pyruvyl group from chorismate to provide 4-hydroxybenzoate (4HB)”. This tells us this protein is an enzyme (a protein that increases the rate of a specific biochemical reaction), and tells us what is the particular biochemical reaction that this enzyme is involved in. At the end of this info it says "By similarity", which again indicates that what we know about this protein comes from bioinformatics, not lab work. +Beside the heading “Function”, it says that the function of this protein is that it “Removes the pyruvyl group from chorismate to provide 4-hydroxybenzoate (4HB)”. This tells us this protein is an enzyme (a protein that increases the rate of a specific biochemical reaction), and tells us what is the particular biochemical reaction that this enzyme is involved in. At the end of this info it says "By similarity", which again indicates that what we know about this protein comes from bioinformatics, not lab work. ### Protein sequence and size @@ -88,6 +88,13 @@ We can confirm str(lepraeseq) ``` +```{r eval = F} + # 'SeqFastadna' chr [1:210] "m" "t" "n" "r" "t" "l" "s" "r" "e" "e" "i" ... + # - attr(*, "name")= chr "sp|Q9CD83|PHBS_MYCLE" + # - attr(*, "Annot")= chr ">sp|Q9CD83|PHBS_MYCLE Chorismate pyruvate-lyase + # OS=Mycobacterium leprae (strain TN) OX=272631 GN=ML0133 PE=3 SV=1" +``` + For the other sequence @@ -99,6 +106,16 @@ file.2 <- system.file("./extdata/A0PQ23.fasta", package = "compbio4all") # load fasta ulcerans <- read.fasta(file = file.2) ulceransseq <- ulcerans[[1]] + +str(ulceransseq)[[1]] +``` + + +```{r} + # 'SeqFastadna' chr [1:212] "m" "l" "a" "v" "l" "p" "e" "k" "r" "e" "m" ... + # - attr(*, "name")= chr "tr|A0PQ23|A0PQ23_MYCUA" + # - attr(*, "Annot")= chr ">tr|A0PQ23|A0PQ23_MYCUA Chorismate pyruvate-lyase +# OS=Mycobacterium ulcerans (strain Agy99) OX=362242 GN=MUL_2003 PE=4 SV=1" ``` diff --git a/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-159-1.png b/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-159-1.png old mode 100755 new mode 100644 index aa7a334..a695096 Binary files a/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-159-1.png and b/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-159-1.png differ diff --git a/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-166-1.png b/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-166-1.png index 5248f4e..651404e 100644 Binary files a/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-166-1.png and b/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-166-1.png differ diff --git a/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-167-1.png b/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-167-1.png index 784aa53..066b75d 100644 Binary files a/_bookdown_files/lbrb_files/figure-html/unnamed-chunk-167-1.png and 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  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases

    A FASTQ file containing a single sequence might look like this:”

    -
    cat("@SEQ_ID
-GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
-+
-!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65")
    +
    cat("@SEQ_ID
+GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
++
+!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65")
    ## @SEQ_ID
 ## GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
 ## +
 ## !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

    Here are the quality value characters in left-to-right increasing order of quality (ASCII):”

    -
     !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
    +
     !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~

    FASTQ files typically do not include line breaks and do not wrap around when they reach the width of a normal page or file.

    diff --git a/docs/introduction-to-biological-sequences-databases.html b/docs/introduction-to-biological-sequences-databases.html index 7da4a24..a4f9ebc 100644 --- a/docs/introduction-to-biological-sequences-databases.html +++ b/docs/introduction-to-biological-sequences-databases.html @@ -265,10 +265,12 @@
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      @@ -414,7 +416,7 @@

      13.1 Topics

      13.2 Introduction

      -

      NCBI is the National Center for Biotechnology Information. The NCBI Webiste is the entry point to a large number of databases giving access to biological sequences (DNA, RNA, protein) and biology-related publications.

      +

      NCBI is the National Center for Biotechnology Information. The NCBI Webiste www.ncbi.nlm.nih.gov/ is the entry point to a large number of databases giving access to biological sequences (DNA, RNA, protein) and biology-related publications.

      When scientists sequence DNA, RNA and proteins they typically publish their data via databases with the NCBI. Each is given a unique identification number known as an accession number. For example, each time a unique human genome sequence is produced it is uploaded to the relevant databases, assigned a unique accession, and a website created to access it. Sequence are also cross-referenced to related papers, so you can start with a sequence and find out what scientific paper it was used in, or start with a paper and see if any sequences are associated with it.

      This chapter provides an introduction to the general search features of the NCBI databases via the interface on the website, including how to locate sequences using accession numbers and other search parameters, such as specific authors or papers. Subsequent chapters will introduce advanced search features, and how to carry out searches using R commands.

      One consequence of the explosion of biological sequences used in publications is that the system of databases has become fairly complex. There are databases for different types of data, different types of molecules, data from individual experiments showing genetic variation and also the consensus sequence for a given molecule. Luckily, if you know the accession number of the sequence you are looking for – which will be our starting point throughout this book – its fairly straight forward. There are numerous other books on bioinformatics and genomics that provide all details if you need to do more complex searches.

      @@ -424,7 +426,7 @@

      13.2 Introduction

      13.3 Biological sequence databases

      Almost published biological sequences are available online, as it is a requirement of every scientific journal that any published DNA or RNA or protein sequence must be deposited in a public database. The main resources for storing and distributing sequence data are three large databases:

        -
      1. USA: NCBI database (www.ncbi.nlm.nih.gov/)
        2. +
      2. USA: NCBI database (www.ncbi.nlm.nih.gov/)
      3. Europe: European Molecular Biology Laboratory (EMBL) database (https://www.ebi.ac.uk/ena)
      4. Japan: DNA Database of Japan (DDBJ) database (www.ddbj.nig.ac.jp/). These databases collect all publicly available DNA, RNA and protein sequence data and make it available for free. They exchange data nightly, so contain essentially the same data. The redundancy among the databases allows them to serve different communities (e.g. native languages), provide different additional services such as tutorials, and assure that the world’s scientists have their data backed up in different physical locations – a key component of good data management!
        5. @@ -456,7 +458,7 @@

        13.6 NCBI GenBank Record FormatAs mentioned above, for each sequence the NCBI database stores some extra information such as the species that it came from, publications describing the sequence, etc. This information is stored in the GenBank entry (aka GenBank Record) for the sequence. The GenBank entry for a sequence can be viewed by searching the NCBI database for the accession number for that sequence.

        To view the GenBank entry for the DEN-1 Dengue virus, follow these steps:

          -
        1. Go to the NCBI website (www.ncbi.nlm.nih.gov).
          2. +
        2. Go to the NCBI website (www.ncbi.nlm.nih.gov).
        3. Search for the accession number NC_001477.
        4. Since we searched for a particular accession we are only returned a single main result which is titled “NUCLEOTIDE SEQUENCE: Dengue virus 1, complete genome.”
        5. Click on “Dengue virus 1, complete genome” to go to the GenBank entry.
          6. diff --git a/docs/lbrb.pdf b/docs/lbrb.pdf index e7e1a2e..4248944 100644 Binary files a/docs/lbrb.pdf and b/docs/lbrb.pdf differ diff --git a/docs/lbrb.tex b/docs/lbrb.tex index ee0e392..0ec235e 100644 --- a/docs/lbrb.tex +++ b/docs/lbrb.tex @@ -2343,6 +2343,24 @@ \chapter{NCBI: The National Center for Biotechnology Information}\label{ncbi-the \textbf{NOTE:} The following material was partially adapted by N. Brouwer from \href{https://en.wikipedia.org/wiki/National_Center_for_Biotechnology_Information}{Wikipedia}. See the underlying .Rmd file for information on specific paragraphs from Wikipedia. +\hypertarget{key-concepts}{% +\section{Key concepts}\label{key-concepts}} + +\begin{itemize} +\tightlist +\item + NCBI +\item + Rentrez +\item + accession numbers +\item + BLAST +\end{itemize} + +\hypertarget{ncbi}{% +\section{NCBI}\label{ncbi}} + The \textbf{National Center for Biotechnology Information (NCBI)} is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It was founded in 1988 and is approved and funded by the government of the United States. The NCBI houses a series of \textbf{databases} relevant to the basic and applied life sciences and is an important resource for \textbf{bioinformatics} tools and services. Major databases include \textbf{GenBank} for DNA sequences and \textbf{PubMed}, a \textbf{bibliographic} database for biomedical literature. All these databases are available online through the \textbf{Entrez} search engine. In this chapter we'll briefly discuss the major databases, following up with specific details in subsequent chapters. One thing to note is that in practice people do no always adhere to the specific names of databases and other tools. For example, someone may say ``I searched NCBI for sequences.'' It would be more accurate to say ``I used Entrez to search GenBank for sequences.'' For in-depth details on all the features see \href{https://academic.oup.com/nar/article/41/D1/D8/1067646}{Database resources of the National Center for Biotechnology Information} @@ -2352,7 +2370,7 @@ \section{GenBank sequence database}\label{genbank-sequence-database}} The GenBank sequence database is an open access collection of publicly available DNA and protein sequences. If you've work with sequence data, you'll work with GenBank. GenBank is the actual database, and it can be searched several ways. For example, you can search for a sequence by its ID number (\textbf{accession number}) if you know it, or do a \textbf{BLAST search} using an actual sequence to look for similar sequences. -A key component of GenBank are the \textbf{GeneBank Records}, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene \href{https://www.ncbi.nlm.nih.gov/protein/AAC65720}{pallysin} from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides \textbf{metadata}, such as the scientific name of the organism (\emph{Treponema pallidum}), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene.\\ +A key component of GenBank are the \textbf{GeneBank Records}, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene \href{https://www.ncbi.nlm.nih.gov/protein/AAC65720}{pallysin} \url{https://www.ncbi.nlm.nih.gov/protein/AAC65720} from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides \textbf{metadata}, such as the scientific name of the organism (\emph{Treponema pallidum}), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene.\\ A key feature of PubMed records is that they are \textbf{hyperlinked} to other NCBI databases. For example, you can click link under the name of the paper which reported the sequence of the gene and it will take you to the PubMed record for that paper (see below). You can also click the ``Run BLAST'' link and you can search the database for similar sequences. This protein coded for by this particular gene has had its structured solved using x-ray crystallography, and you can see these results under ``Protein 3D Structure.'' In a later chapter we'll get to know these records in further detail. @@ -2366,18 +2384,14 @@ \section{PubMed and PubMed Central article database}\label{pubmed-and-pubmed-cen \hypertarget{entrez}{% \section{Entrez}\label{entrez}} -\url{https://en.wikipedia.org/wiki/Entrez} - -``Entrez is a federated search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name''Entrez'' (a greeting meaning ``Come in'' in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI.'' +Entrez is a search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name ``Entrez'' (a greeting meaning ``Come in'' in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI. -``Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system.'' +Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system. \hypertarget{blast}{% \section{BLAST}\label{blast}} -\url{https://en.wikipedia.org/wiki/BLAST_(biotechnology)} - -``BLAST (basic local alignment search tool)is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence.'' +BLAST (Basic Local Alignment Search Tool) is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. \hypertarget{introduction-to-biological-sequences-databases}{% \chapter{Introduction to biological sequences databases}\label{introduction-to-biological-sequences-databases}} @@ -2418,7 +2432,7 @@ \section{Topics}\label{topics}} \hypertarget{introduction}{% \section{Introduction}\label{introduction}} -\textbf{NCBI} is the National Center for Biotechnology Information. The \href{www.ncbi.nlm.nih.gov/}{NCBI Webiste} is the entry point to a large number of databases giving access to \textbf{biological sequences} (DNA, RNA, protein) and biology-related publications. +\textbf{NCBI} is the National Center for Biotechnology Information. The \href{https://www.ncbi.nlm.nih.gov/}{NCBI Webiste} www.ncbi.nlm.nih.gov/ is the entry point to a large number of databases giving access to \textbf{biological sequences} (DNA, RNA, protein) and biology-related publications. When scientists sequence DNA, RNA and proteins they typically publish their data via databases with the NCBI. Each is given a unique identification number known as an \textbf{accession number}. For example, each time a unique human genome sequence is produced it is uploaded to the relevant databases, assigned a unique \textbf{accession}, and a website created to access it. Sequence are also cross-referenced to related papers, so you can start with a sequence and find out what scientific paper it was used in, or start with a paper and see if any sequences are associated with it. @@ -2437,7 +2451,7 @@ \section{Biological sequence databases}\label{biological-sequence-databases}} \def\labelenumi{\arabic{enumi}.} \tightlist \item - USA: \textbf{\href{www.ncbi.nlm.nih.gov/}{NCBI database}} (www.ncbi.nlm.nih.gov/) + USA: \textbf{\href{https://www.ncbi.nlm.nih.gov/}{NCBI database}} (www.ncbi.nlm.nih.gov/) \item Europe: \textbf{European Molecular Biology Laboratory (EMBL)} database (\url{https://www.ebi.ac.uk/ena}) \item @@ -2484,7 +2498,7 @@ \section{NCBI GenBank Record Format}\label{ncbi-genbank-record-format}} \def\labelenumi{\arabic{enumi}.} \tightlist \item - Go to the \href{www.ncbi.nlm.nih.gov}{NCBI website} (www.ncbi.nlm.nih.gov). + Go to the \href{https://www.ncbi.nlm.nih.gov}{NCBI website} (www.ncbi.nlm.nih.gov). \item Search for the accession number NC\_001477. \item @@ -2672,7 +2686,7 @@ \section{Preface}\label{preface-3}} \hypertarget{retrieving-genome-sequence-data-via-the-ncbi-website}{% \section{Retrieving genome sequence data via the NCBI website}\label{retrieving-genome-sequence-data-via-the-ncbi-website}} -You can easily retrieve DNA or protein sequence data by hand from the \href{www.ncbi.nlm.nih.gov}{NCBI} Sequence Database via its website www.ncbi.nlm.nih.gov. +You can easily retrieve DNA or protein sequence data by hand from the \href{https://www.ncbi.nlm.nih.gov/}{NCBI} Sequence Database via its website www.ncbi.nlm.nih.gov. Dengue DEN-1 DNA is a viral DNA sequence and its NCBI \textbf{accession number} is NC\_001477. To retrieve the DNA sequence for the Dengue DEN-1 virus from NCBI, go to the NCBI website, type ``NC\_001477'' in the Search box at the top of the webpage, and press the ``Search'' button beside the Search box. @@ -2721,7 +2735,7 @@ \section{Downloading Protein data from UniProt}\label{downloading-protein-data-f As mentioned previously, a subsection of the NCBI database called \textbf{RefSeq} consists of high quality DNA and protein sequence data. Furthermore, the NCBI entries for the RefSeq sequences have been \textbf{manually curated}, which means that expert biologists employed by NCBI have added additional information to the NCBI entries for those sequences, such as details of scientific papers that describe the sequences. -Another extremely important manually curated database is \href{www.uniprot.org}{\textbf{UniProt}}, which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. +Another extremely important manually curated database is \href{https://www.uniprot.org/}{UniProt} www.uniprot.org, which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. For each protein in UniProt, the UniProt curators read all the scientific papers that they can find about that protein, and add information from those papers to the protein's UniProt entry. For example, for a human protein, the UniProt entry for the protein usually includes information about the biological function of the protein, in what human tissues it is expressed, whether it interacts with other human proteins, and much more. All this information has been manually gathered by the UniProt curators from scientific papers, and the papers in which the found the information are always listed in the UniProt entry for the protein. @@ -2791,6 +2805,13 @@ \section{Retrieving a UniProt protein sequence via the UniProt website}\label{re str(lepraeseq) \end{lstlisting} +\begin{lstlisting}[language=R] + # 'SeqFastadna' chr [1:210] "m" "t" "n" "r" "t" "l" "s" "r" "e" "e" "i" ... + # - attr(*, "name")= chr "sp|Q9CD83|PHBS_MYCLE" + # - attr(*, "Annot")= chr ">sp|Q9CD83|PHBS_MYCLE Chorismate pyruvate-lyase + # OS=Mycobacterium leprae (strain TN) OX=272631 GN=ML0133 PE=3 SV=1" +\end{lstlisting} + For the other sequence \begin{lstlisting}[language=R] @@ -2801,6 +2822,15 @@ \section{Retrieving a UniProt protein sequence via the UniProt website}\label{re # load fasta ulcerans <- read.fasta(file = file.2) ulceransseq <- ulcerans[[1]] + +str(ulceransseq)[[1]] +\end{lstlisting} + +\begin{lstlisting}[language=R] + # 'SeqFastadna' chr [1:212] "m" "l" "a" "v" "l" "p" "e" "k" "r" "e" "m" ... + # - attr(*, "name")= chr "tr|A0PQ23|A0PQ23_MYCUA" + # - attr(*, "Annot")= chr ">tr|A0PQ23|A0PQ23_MYCUA Chorismate pyruvate-lyase +# OS=Mycobacterium ulcerans (strain Agy99) OX=362242 GN=MUL_2003 PE=4 SV=1" \end{lstlisting} \hypertarget{introducingFASTA}{% @@ -3403,7 +3433,7 @@ \section{Prepping macromolecular sequences}\label{prepping-macromolecular-sequen ## ## return(fasta_object[[1]]) ## } -## +## ## \end{lstlisting} @@ -3740,7 +3770,7 @@ \subsubsection{Displaying an MSA as an R plot}\label{displaying-an-msa-as-an-r-p ## grobY.absoluteGrob ggplot2 \end{lstlisting} -\includegraphics{lbrb_files/figure-latex/unnamed-chunk-157-1.pdf} +\includegraphics{lbrb_files/figure-latex/unnamed-chunk-159-1.pdf} \hypertarget{saving-an-msa-as-pdf}{% \subsubsection{Saving an MSA as PDF}\label{saving-an-msa-as-pdf}} @@ -3848,7 +3878,7 @@ \subsection{Plotting phylogenetic trees}\label{plotting-phylogenetic-trees}} mtext(text = "Shroom family gene tree - unrooted, no branch lengths") \end{lstlisting} -\includegraphics{lbrb_files/figure-latex/unnamed-chunk-164-1.pdf} +\includegraphics{lbrb_files/figure-latex/unnamed-chunk-166-1.pdf} This is an **unrooted tree*. For the sake of plotting we've also ignored the evolutionary distance between the sequences. @@ -3863,7 +3893,7 @@ \subsection{Plotting phylogenetic trees}\label{plotting-phylogenetic-trees}} mtext(text = "Shroom family gene tree - rooted, no branch lenths") \end{lstlisting} -\includegraphics{lbrb_files/figure-latex/unnamed-chunk-165-1.pdf} +\includegraphics{lbrb_files/figure-latex/unnamed-chunk-167-1.pdf} We can include information about branch length by setting \passthrough{\lstinline!use.edge.length = ...!} to \passthrough{\lstinline!T!}. @@ -3876,7 +3906,7 @@ \subsection{Plotting phylogenetic trees}\label{plotting-phylogenetic-trees}} mtext(text = "Shroom family gene tree - rooted, with branch lenths") \end{lstlisting} -\includegraphics{lbrb_files/figure-latex/unnamed-chunk-166-1.pdf} +\includegraphics{lbrb_files/figure-latex/unnamed-chunk-168-1.pdf} Some of the branches are now very short, but most are very long, indicating that these genes have been evolving independently for many millions of years. @@ -3930,7 +3960,7 @@ \subsection{Plotting phylogenetic trees}\label{plotting-phylogenetic-trees}} lwd=2) \end{lstlisting} -\includegraphics{lbrb_files/figure-latex/unnamed-chunk-167-1.pdf} +\includegraphics{lbrb_files/figure-latex/unnamed-chunk-169-1.pdf} \hypertarget{logarithms-in-r}{% \chapter{Logarithms in R}\label{logarithms-in-r}} @@ -4401,7 +4431,7 @@ \section{Practice (OPTIONAL)}\label{practice-optional}} \end{lstlisting} \begin{lstlisting} -## [1] "Sat Sep 18 00:32:06 2021" +## [1] "Sat Sep 18 01:23:51 2021" \end{lstlisting} \textbf{Exponents} diff --git a/docs/lbrb_files/figure-html/unnamed-chunk-159-1.png b/docs/lbrb_files/figure-html/unnamed-chunk-159-1.png new file mode 100644 index 0000000..a695096 Binary files /dev/null and b/docs/lbrb_files/figure-html/unnamed-chunk-159-1.png differ diff --git a/docs/lbrb_files/figure-html/unnamed-chunk-166-1.png b/docs/lbrb_files/figure-html/unnamed-chunk-166-1.png index 5248f4e..651404e 100644 Binary files a/docs/lbrb_files/figure-html/unnamed-chunk-166-1.png and b/docs/lbrb_files/figure-html/unnamed-chunk-166-1.png differ diff --git a/docs/lbrb_files/figure-html/unnamed-chunk-167-1.png b/docs/lbrb_files/figure-html/unnamed-chunk-167-1.png index 784aa53..066b75d 100644 Binary files a/docs/lbrb_files/figure-html/unnamed-chunk-167-1.png and b/docs/lbrb_files/figure-html/unnamed-chunk-167-1.png differ diff --git a/docs/lbrb_files/figure-html/unnamed-chunk-168-1.png b/docs/lbrb_files/figure-html/unnamed-chunk-168-1.png new file mode 100644 index 0000000..5248f4e Binary files /dev/null and b/docs/lbrb_files/figure-html/unnamed-chunk-168-1.png differ diff --git a/docs/lbrb_files/figure-html/unnamed-chunk-169-1.png b/docs/lbrb_files/figure-html/unnamed-chunk-169-1.png new file mode 100644 index 0000000..784aa53 Binary files /dev/null and b/docs/lbrb_files/figure-html/unnamed-chunk-169-1.png differ diff --git a/docs/logarithms-in-r.html b/docs/logarithms-in-r.html index 8bd6d3a..e265dbf 100644 --- a/docs/logarithms-in-r.html +++ b/docs/logarithms-in-r.html @@ -265,10 +265,12 @@

  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      @@ -394,34 +396,34 @@

      Chapter 19 Logarithms in R

      Logging splits up multiplication into addition. So, log(m*n) is the same as log(m) + log(n)

      You can check this

      -
      m<-10
-n<-11
-log(m*n)
      +
      m<-10
+n<-11
+log(m*n)
      ## [1] 4.70048
      -
      log(m)+log(n)
      +
      log(m)+log(n)
      ## [1] 4.70048
      -
      log(m*n) == log(m)+log(n)
      +
      log(m*n) == log(m)+log(n)
      ## [1] TRUE

      Exponentiation undos logs

      -
      exp(log(m*n))
      +
      exp(log(m*n))
      ## [1] 110
      -
      m*n
      +
      m*n
      ## [1] 110

      The key equation in BLAST’s E values is

      u = ln(Kmn)/lambda

      This can be changed to

      [ln(K) + ln(mn)]/lambda

      We can check this

      -
      K <- 1
-m <- 10
-n <- 11
-lambda <- 110
-
-log(K*m*n)/lambda
      +
      K <- 1
+m <- 10
+n <- 11
+lambda <- 110
+
+log(K*m*n)/lambda
      ## [1] 0.04273164
      -
      (log(K) + log(m*n))/lambda
      +
      (log(K) + log(m*n))/lambda
      ## [1] 0.04273164
      -
      log(K*m*n)/lambda == (log(K) + log(m*n))/lambda
      +
      log(K*m*n)/lambda == (log(K) + log(m*n))/lambda
      ## [1] TRUE
      diff --git a/docs/ncbi-the-national-center-for-biotechnology-information.html b/docs/ncbi-the-national-center-for-biotechnology-information.html index f8ee6ef..0a176bd 100644 --- a/docs/ncbi-the-national-center-for-biotechnology-information.html +++ b/docs/ncbi-the-national-center-for-biotechnology-information.html @@ -265,10 +265,12 @@
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      @@ -391,35 +393,47 @@

      Chapter 12 NCBI: The National Center for Biotechnology Information

      NOTE: The following material was partially adapted by N. Brouwer from Wikipedia. See the underlying .Rmd file for information on specific paragraphs from Wikipedia.

      +
      +

      12.1 Key concepts

      +
        +
      • NCBI
        • +
      • Rentrez
        • +
      • accession numbers
        • +
      • BLAST
        • +
      +
      +
      +

      12.2 NCBI

      The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It was founded in 1988 and is approved and funded by the government of the United States. The NCBI houses a series of databases relevant to the basic and applied life sciences and is an important resource for bioinformatics tools and services. Major databases include GenBank for DNA sequences and PubMed, a bibliographic database for biomedical literature. All these databases are available online through the Entrez search engine.

      In this chapter we’ll briefly discuss the major databases, following up with specific details in subsequent chapters. One thing to note is that in practice people do no always adhere to the specific names of databases and other tools. For example, someone may say “I searched NCBI for sequences.” It would be more accurate to say “I used Entrez to search GenBank for sequences.” For in-depth details on all the features see Database resources of the National Center for Biotechnology Information

      -
      -

      12.1 GenBank sequence database

      +
      +
      +

      12.3 GenBank sequence database

      The GenBank sequence database is an open access collection of publicly available DNA and protein sequences. If you’ve work with sequence data, you’ll work with GenBank. GenBank is the actual database, and it can be searched several ways. For example, you can search for a sequence by its ID number (accession number) if you know it, or do a BLAST search using an actual sequence to look for similar sequences.

      -

      A key component of GenBank are the GeneBank Records, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene pallysin from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides metadata, such as the scientific name of the organism (Treponema pallidum), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene.
      +

      A key component of GenBank are the GeneBank Records, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene pallysin https://www.ncbi.nlm.nih.gov/protein/AAC65720 from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides metadata, such as the scientific name of the organism (Treponema pallidum), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene.

      A key feature of PubMed records is that they are hyperlinked to other NCBI databases. For example, you can click link under the name of the paper which reported the sequence of the gene and it will take you to the PubMed record for that paper (see below). You can also click the “Run BLAST” link and you can search the database for similar sequences. This protein coded for by this particular gene has had its structured solved using x-ray crystallography, and you can see these results under “Protein 3D Structure.” In a later chapter we’ll get to know these records in further detail.

      -
      -

      12.2 PubMed and PubMed Central article database

      +
      +

      12.4 PubMed and PubMed Central article database

      PubMed and PubMed Central (PMC) are databases of scientific articles related to data contained in NCBI databases. PubMed contains basic bibliographic information, the abstract, relevant links to sequences, and to the websites of the actual publishers of the papers. If the text of an article is open-access, PMC should have a copy of it. Articles in PMC contain relevant hyperlinks, such as to any sequences that are mentioned. For example, the image below show where the sequence of Tp0751 is mentioned in a paper and linked to the GenBank record we looked at above.

      -
      -

      12.3 Entrez

      -

      https://en.wikipedia.org/wiki/Entrez

      +
      +

      12.5 Entrez

      + -

      “Entrez is a federated search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name”Entrez” (a greeting meaning “Come in” in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI.”

      +

      Entrez is a search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name “Entrez” (a greeting meaning “Come in” in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI.

      -

      “Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system.”

      +

      Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system.

      -
      -

      12.4 BLAST

      -

      https://en.wikipedia.org/wiki/BLAST_(biotechnology)

      +
      +

      12.6 BLAST

      + -

      “BLAST (basic local alignment search tool)is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence.”

      +

      BLAST (Basic Local Alignment Search Tool) is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence.

      diff --git a/docs/nucleic-acids-review.html b/docs/nucleic-acids-review.html index 92b0368..5fc503e 100644 --- a/docs/nucleic-acids-review.html +++ b/docs/nucleic-acids-review.html @@ -265,10 +265,12 @@

  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      diff --git a/docs/plotting-vectors.html b/docs/plotting-vectors.html index 3568462..ae18002 100644 --- a/docs/plotting-vectors.html +++ b/docs/plotting-vectors.html @@ -265,10 +265,12 @@
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      diff --git a/docs/proteins-review.html b/docs/proteins-review.html index e4b6c41..d867f18 100644 --- a/docs/proteins-review.html +++ b/docs/proteins-review.html @@ -265,10 +265,12 @@
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      diff --git a/docs/search_index.json b/docs/search_index.json index 16368d9..f859698 100644 --- a/docs/search_index.json +++ b/docs/search_index.json @@ -1 +1 @@ -[["index.html", "A Little Book of R for Bioinformatics 2.0 Preface to version 2.0", " A Little Book of R for Bioinformatics 2.0 Avirl Coghlan, with contributions by Nathan L. Brouwer 2021-09-18 Preface to version 2.0 Welcome to A Little Book of R for Bioinformatics 2.0!. This book is based on the original A Little Book of R for Bioinformatics by Dr. Avril Coghlan (Hereafter “ALBRB 1.0”). Dr. Coghlan’s book was one of the first and most thorough introductions to using R for bioinformatics and computational biology, and was generously published under the Creative Commons 3.0 Attribution License (CC BY 3.0). In addition to describing how to do bioinformatics in R, Coghlan provided numerous functions to facilitate important tasks, practice questions, and references to further reading. ALBRB 1.0 was extremely useful to me when I was learning bioinformatics and computational biology. In this version of the book, which I’ll refer to as ALBRB 2.0, I have adapted Dr. Coghlan’s original book to suit my own teaching needs, updated it with current packages now available in R, added background materials from other open-access sources, and added in my original materials. Below I’ve outlined the general types of changes I’ve made to the original book. I have tried to link back to the original content that these updates are derived from and note how changes were made. Any errors or inconsistencies should be ascribed to me, not Dr. Coghlan. If you have any feedback, please email me at brouwern@gmail.com ~Nathan Brouwer, June 2021 Changes implemented in ALBRB 2.0 by Nathan Brouwer Converted the entire book to RMarkdown and published it via bookdown. Added instructions for using RStudio and RStudio Cloud. Updated instructions to reflect any changes in software, including changes to how the bioinformatics repository Bioconductor now works. Split up chapters into smaller units. Reorganized the order of some material. Added biological background information by integrating information from the Open Access textbook LibreText General Biology Added links to the books I am developing, Get R Done! and Computational Biology for All. Moved functions written by Coghlan and datasets to my teaching package compbio4all. Functions names changed from camelCase to snake_case Functions re-written so as not to use Bioconductor to reduce/eliminate dependency of compbio4all on Bioconductor. Changed some plotting to ggplot2 or ggpubr. Added additional subheadings Added vocab and function lists to the beginning of many chapters At times replaced non-biological examples with biological ones. Change from British to American English (Sorry! Couldn’t help myself - the spellchecker made me do it!) Provided additional links to external resources. Added use of rentrez for querying NCBI databases "],["downloadR.html", "Chapter 1 Downloading R 1.1 Preface 1.2 Introduction to R 1.3 Installing R 1.4 Starting R", " Chapter 1 Downloading R By: Avril Coghlan Adapted, edited and expanded: Nathan Brouwer (brouwern@gmail.com) under the Creative Commons 3.0 Attribution License (CC BY 3.0). 1.1 Preface The following introduction to R is based on the first part of “How to install R and a Brief Introduction to R” by Avril Coghlan, which was released under the Creative Commons 3.0 Attribution License (CC BY 3.0). For additional information see the Appendices and “Getting R onto your computer”. 1.2 Introduction to R R (www.r-project.org) is a commonly used free statistics software. R allows you to carry out statistical analyses in an interactive mode, as well as allowing programming. 1.3 Installing R To use R, you first need to install the R program on your computer. 1.3.1 Installing R on a Windows PC These instructions will focus on installing R on a Windows PC. However, I will also briefly mention how to install R on a Macintosh or Linux computer (see below). These steps have not been checked as of 8/13/2019 so there may be small variations in what the prompts are. Installing R, however, is basically that same as any other program. Clicking “Yes” etc on everything should work. PROTIP: Even if you have used R before its good to regularly update it to avoid conflicts with recently produced software. Minor updates of R are made very regularly (approximately every 6 months), as R is actively being improved all the time. It is worthwhile installing new versions of R a couple times a year, to make sure that you have a recent version of R (to ensure compatibility with all the latest versions of the R packages that you have downloaded). To install R on your Windows computer, follow these steps: Go to https://cran.r-project.org/ Under “Download and Install R”, click on the “Windows” link. Under “Subdirectories”, click on the “base” link. On the next page, you should see a link saying something like “Download R 4.1.0 for Windows” (or R X.X.X, where X.X.X gives the version of the program). Click on this link. You may be asked if you want to save or run a file “R-x.x.x-win32.exe”. Choose “Save” and save the file. Then double-click on the icon for the file to run it. You will be asked what language to install it in. The R Setup Wizard will appear in a window. Click “Next” at the bottom of the R Setup wizard window. The next page says “Information” at the top. Click “Next” again. The next page says “Select Destination Location” at the top. By default, it will suggest to install R on the C drive in the “Program Files” directory on your computer. Click “Next” at the bottom of the R Setup wizard window. The next page says “Select components” at the top. Click “Next” again. The next page says “Startup options” at the top. Click “Next” again. The next page says “Select start menu folder” at the top. Click “Next” again. The next page says “Select additional tasks” at the top. Click “Next” again. R should now be installing. This will take about a minute. When R has finished, you will see “Completing the R for Windows Setup Wizard” appear. Click “Finish”. To start R, you can do one of the following steps: Check if there is an “R” icon on the desktop of the computer that you are using. If so, double-click on the “R” icon to start R. If you cannot find an “R” icon, try the next step instead. Click on the “Start” button at the bottom left of your computer screen, and then choose “All programs”, and start R by selecting “R” (or R X.X.X, where X.X.X gives the version of R) from the menu of programs. The R console (a rectangle) should pop up: 1.3.2 How to install R on non-Windows computers (eg. Macintosh or Linux computers) These steps have not been checked as of 8/13/2019 so there may be small variations in what the prompts are. Installing R, however, is basically that same as any other program. Clicking “Yes” etc on everything should work. The instructions above are for installing R on a Windows PC. If you want to install R on a computer that has a non-Windows operating system (for example, a Macintosh or computer running Linux, you should download the appropriate R installer for that operating system at https://cran.r-project.org/ and follow the R installation instructions for the appropriate operating system at https://cran.r-project.org/doc/FAQ/R-FAQ.html#How-can-R-be-installed_003f . 1.4 Starting R To start R, Check if there is an R icon on the desktop of the computer that you are using. If so, double-click on the R icon to start R. If you cannot find an R icon, try the next step instead. You can also start R from the Start menu in Windows. Click on the “Start” button at the bottom left of your computer screen, and then choose “All programs”, and start R by selecting “R” (or R X.X.X, where X.X.X gives the version of R, e.g.. R 2.10.0) from the menu of programs. Say “Hi” to R and take a quick look at how it looks. Now say “Goodbye”, because we will never actually do any work in this version of R; instead, we’ll use the RStudio IDE (integrated development environment). "],["installing-the-rstudio-ide-installrstudio.html", "Chapter 2 Installing the RStudio IDE {$installRStudio} 2.1 Getting to know RStudio 2.2 RStudio versus RStudio Cloud", " Chapter 2 Installing the RStudio IDE {$installRStudio} By: Nathan Brouwer The name “R” refers both to the programming language and the program that runs that language. When you download itR* there is also a basic GUI (graphical user interface) that you can access via the R icon. Other GUIs are available, and the most popular currently is RStudio. RStudio a for-profit company that is a main driver of development of R. Much of what they produce has free basic versions or is entirely free. They produce software (RStudio), cloud-based applications (RStudio Cloud), and web server infrastructure for business applications of R. A brief overview of installing RStudio can be found here “Getting RStudio on to your computer” 2.1 Getting to know RStudio For a brief overview of RStudio see “Getting started with RStudio” A good overview of what the different parts of RStudio can be seen in the image in this tweet: https://twitter.com/RLadiesNCL/status/1138812826917724160?s=20 2.2 RStudio versus RStudio Cloud RStudio and RStudio cloud work almost identically, so anything you read about RStudio will apply to RStudio Cloud. RStudio is easy to download an use, but RStudio Cloud eliminates even the minor hiccups that occur. Free accounts with RStudio Cloud allow up to 15 hours per month, which is enough for you to get a taste for using R. "],["installing-packages.html", "Chapter 3 Installing R packages 3.1 Downloading packages with the RStudio IDE 3.2 Downloading packages with the function install.packages() 3.3 Using packages after they are downloaded", " Chapter 3 Installing R packages By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). R is a programming language, and packages (aka libraries) are bundles of software built using R. Most sessions using R involve using additional R packages. This is especially true for bioinformatics and computational biology. NOTE: If you are working in an RStudio Cloud environment organized by someone else (e.g. a course instructor), they likely are taking care of many of the package management issues. The following information is still useful to be familiar with. 3.1 Downloading packages with the RStudio IDE There is a point-and-click interface for installing R packages in RStudio. There is a brief introduction to downloading packages on this site: http://web.cs.ucla.edu/~gulzar/rstudio/ I’ve summarized it here: “Click on the”Packages” tab in the bottom-right section and then click on “Install”. The following dialog box will appear. In the “Install Packages” dialog, write the package name you want to install under the Packages field and then click install. This will install the package you searched for or give you a list of matching package based on your package text. 3.2 Downloading packages with the function install.packages() The easiest way to install a package if you know its name is to use the R function install.packages()`. Note that it might be better to call this “download.packages” since after you install it, you also have to load it! Frequently I will include install.packages(...) at the beginning of a chapter the first time we use a package to make sure the package is downloaded. Note, however, that if you already have downloaded the package, running install.packages(...) will download a new copy. While packages do get updated from time to time, but its best to re-run install.packages(...) only occassionaly. We’ll download a package used for plotting called ggplot2, which stands for “Grammar of Graphics.” ggplot2 was developed by Dr. Hadley Wickham, who is now the Chief Scientists for RStudio. To download ggplot2, run the following command: install.packages("ggplot2") # note the " " Often when you download a package you’ll see a fair bit of angry-looking red text, and sometime other things will pop up. Usually there’s nothing of interest here, but sometimes you need to read things carefully over it for hints about why something didn’t work. 3.3 Using packages after they are downloaded To actually make the functions in package accessible you need to use the library() command. Note that this is not in quotes. library(ggplot2) # note: NO " " "],["installing-bioconductor.html", "Chapter 4 Installing Bioconductor 4.1 Bioconductor 4.2 Installing BiocManager 4.3 The ins and outs of package installation 4.4 Actually loading a package", " Chapter 4 Installing Bioconductor By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0), including details on install Bioconductor and common prompts and error messages that appear during installation. 4.1 Bioconductor R packages (aka “libraries”) can live in many places. Most are accessed via CRAN, the Comprehensive R Archive Network. The bioinformatics and computational biology community also has its own package hosting system called Bioconductor. R has played an important part in the development and application of bioinformatics techniques in the 21th century. Bioconductor 1.0 was released in 2002 with 15 packages. As of winter 2021, there are almost 2000 packages in the current release! NOTE: If you are working in an RStudio Cloud environment organized by someone else (eg a course instructor), they likely are taking care of most of package management issues, inlcuding setting up Bioconductor. The following information is still useful to be familiar with. To interface with Bioconductor you need the BiocManager package. The Bioconductor people have put BiocManager on CRAN to allow you to set up interactions with Bioconductor. See the BiocManager documentation for more information (https://cran.r-project.org/web/packages/BiocManager/vignettes/BiocManager.html). Note that if you have an old version of R you will need to update it to interact with Bioconductor. 4.2 Installing BiocManager BiocManager can be installed using the install.packages() packages command. install.packages("BiocManager") # Remember the " "; don't worry about the red text Once downloaded, BioManager needs to be explicitly loaded into your active R session using library() library(BiocManager) # no quotes; again, ignore the red text Individual Bioconductor packages can then be downloaded using the install() command. An essential packages is Biostrings. To do this , BiocManager::install("Biostrings") 4.3 The ins and outs of package installation IMPORANT Bioconductor has many dependencies - other packages which is relies on. When you install Bioconductor packages you may need to update these packages. If something seems to not be working during this process, restart R and begin the Bioconductor installation process until things seem to work. Below I discuss the series of prompts I had to deal with while re-installing Biostrings while editing this chapter. 4.3.1 Updating other packages when downloading a package When I re-installed Biostrings while writing this I was given a HUGE blog of red test that contained something like what’s shown below (this only aout 1/3 of the actual output!): 'getOption("repos")' replaces Bioconductor standard repositories, see '?repositories' for details replacement repositories: CRAN: https://cran.rstudio.com/ Bioconductor version 3.11 (BiocManager 1.30.16), R 4.0.5 (2021-03-31) Old packages: 'ade4', 'ape', 'aster', 'bayestestR', 'bio3d', 'bitops', 'blogdown', 'bookdown', 'brio', 'broom', 'broom.mixed', 'broomExtra', 'bslib', 'cachem', 'callr', 'car', 'circlize', 'class', 'cli', 'cluster', 'colorspace', 'corrplot', 'cpp11', 'curl', 'devtools', 'DHARMa', 'doBy', 'dplyr', 'DT', 'e1071', 'ellipsis', 'emmeans', 'emojifont', 'extRemes', 'fansi', 'flextable', 'forecast', 'formatR', 'gap', 'gargle', 'gert', 'GGally' Hidden at the bottom was a prompt: \"Update all/some/none? [a/s/n]:\" Its a little vague, but what it wants me to do is type in a, s or n and press enter to tell it what to do. I almost always chose a, though this may take a while to update everything. 4.3.2 Packages “from source” You are likely to get lots of random-looking feedback from R when doing Bioconductor-related installations. Look carefully for any prompts as the very last line. While updating Biostrings I was told: “There are binary versions available but the source versions are later:” and given a table of packages. I was then asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)” I almost always chose “no”. 4.3.3 More on angry red text After the prompt about packages from source, R proceeded to download a lot of updates to packages, which took a few minutes. Lots of red text scrolled by, but this is normal. 4.4 Actually loading a package Again, to actually load the Biostrings package into your active R sessions requires the libary() command: library(Biostrings) As you might expect, there’s more red text scrolling up my screen! I can tell that is actually worked because at the end of all the red stuff is the R prompt of “>” and my cursor. "],["basic-R.html", "Chapter 5 A Brief introduction to R 5.1 Vocabulary 5.2 R functions 5.3 Interacting with R 5.4 Variables in R 5.5 Arguments 5.6 Help files with help() and ? 5.7 Searching for functions with help.search() and RSiteSearch() 5.8 More on functions 5.9 Quiting R 5.10 Links and Further Reading", " Chapter 5 A Brief introduction to R By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). This chapter provides a brief introduction to R. At the end of are links to additional resources for getting started with R. 5.1 Vocabulary scalar vector list class numeric character assignment elements of an object indices attributes of an object argument of a function 5.2 R functions <- [ ] $ table() function c() log10() help(), ? help.search() RSiteSearch() mean() return() q() 5.3 Interacting with R You will type R commands into the RStudio console in order to carry out analyses in R. In the RStudio console you will see the R prompt starting with the symbol “>”. “>” will always be there at the beginning of each new command - don’t try to delete it! Moreover, you never need to type it. We type the commands needed for a particular task after this prompt. The command is carried out by R after you hit the Return key. Once you have started R, you can start typing commands into the RStudio console, and the results will be calculated immediately, for example: 2*3 ## [1] 6 Note that prior to the output of “6” it shows “[1]”. Now subtraction: 10-3 ## [1] 7 Again, prior to the output of “7” it shows “[1]”. R can act like a basic calculator that you type commands in to. You can also use it like a more advanced scientific calculator and create variables that store information. All variables created by R are called objects. In R, we assign values to variables using an arrow-looking function <- the assignment operator. For example, we can assign the value 2*3 to the variable x using the command: x <- 2*3 To view the contents of any R object, just type its name, press enter, and the contents of that R object will be displayed: x ## [1] 6 5.4 Variables in R There are several different types of objects in R with fancy math names, including scalars, vectors, matrices (singular: matrix), arrays, dataframes, tables, and lists. The scalar** variable x above is one example of an R object. While a scalar variable such as x has just one element, a vector consists of several elements. The elements in a vector are all of the same type (e.g.. numbers or alphabetic characters), while lists may include elements such as characters as well as numeric quantities. Vectors and dataframes are the most common variables you’ll use. You’ll also encounter matrices often, and lists are ubiquitous in R but beginning users often don’t encounter them because they remain behind the scenes. 5.4.1 Vectors To create a vector, we can use the c() (combine) function. For example, to create a vector called myvector that has elements with values 8, 6, 9, 10, and 5, we type: myvector <- c(8, 6, 9, 10, 5) # note: commas between each number! To see the contents of the variable myvector, we can just type its name and press enter: myvector ## [1] 8 6 9 10 5 5.4.2 Vector indexing The [1] is the index of the first element in the vector. We can extract any element of the vector by typing the vector name with the index of that element given in square brackets [...]. For example, to get the value of the 4th element in the vector myvector, we type: myvector[4] ## [1] 10 5.4.3 Character vectors Vectors can contain letters, such as those designating nucleic acids my.seq <- c("A","T","C","G") They can also contain multi-letter strings: my.oligos <- c("ATCGC","TTTCGC","CCCGCG","GGGCGC") 5.4.4 Lists NOTE: below is a discussion of lists in R. This is excellent information, but not necessary if this is your very very first time using R. In contrast to a vector, a list can contain elements of different types, for example, both numbers and letters. A list can even include other variables such as a vector. The list() function is used to create a list. For example, we could create a list mylist by typing: mylist <- list(name="Charles Darwin", wife="Emma Darwin", myvector) We can then print out the contents of the list mylist by typing its name: mylist ## $name ## [1] "Charles Darwin" ## ## $wife ## [1] "Emma Darwin" ## ## [[3]] ## [1] 8 6 9 10 5 The elements in a list are numbered, and can be referred to using indices. We can extract an element of a list by typing the list name with the index of the element given in double square brackets (in contrast to a vector, where we only use single square brackets). We can extract the second element from mylist by typing: mylist[[2]] # note the double square brackets [[...]] ## [1] "Emma Darwin" As a baby step towards our next task, we can wrap index values as in the c() command like this: mylist[[c(2)]] # note the double square brackets [[...]] ## [1] "Emma Darwin" The number 2 and c(2) mean the same thing. Now, we can extract the second AND third elements from mylist. First, we put the indices 2 and 3 into a vector c(2,3), then wrap that vector in double square brackets: [c(2,3)]. All together it looks like this. mylist[c(2,3)] # note the double brackets ## $wife ## [1] "Emma Darwin" ## ## [[2]] ## [1] 8 6 9 10 5 Elements of lists may also be named, resulting in a named lists. The elements may then be referred to by giving the list name, followed by “$”, followed by the element name. For example, mylist$name is the same as mylist[[1]] and mylist$wife is the same as mylist[[2]]: mylist$wife ## [1] "Emma Darwin" We can find out the names of the named elements in a list by using the attributes() function, for example: attributes(mylist) ## $names ## [1] "name" "wife" "" When you use the attributes() function to find the named elements of a list variable, the named elements are always listed under a heading “$names”. Therefore, we see that the named elements of the list variable mylist are called “name” and “wife”, and we can retrieve their values by typing mylist$name and mylist$wife, respectively. 5.4.5 Tables Another type of object that you will encounter in R is a table. The table() function allows you to total up or tabulate the number of times a value occurs within a vector. Tables are typically used on vectors containing character data, such as letters, words, or names, but can work on numeric data data. 5.4.5.1 Tables - The basics If we made a vector variable “nucleotides” containing the of a DNA molecule, we can use the table() function to produce a table variable that contains the number of bases with each possible nucleotides: bases <- c("A", "T", "A", "A", "T", "C", "G", "C", "G") Now make the table table(bases) ## bases ## A C G T ## 3 2 2 2 We can store the table variable produced by the function table(), and call the stored table “bases.table”, by typing: bases.table <- table(bases) Tables also work on vectors containing numbers. First, a vector of numbers. numeric.vecter <- c(1,1,1,1,3,4,4,4,4) Second, a table, showing how many times each number occurs. table(numeric.vecter) ## numeric.vecter ## 1 3 4 ## 4 1 4 5.4.5.2 Tables - further details To access elements in a table variable, you need to use double square brackets, just like accessing elements in a list. For example, to access the fourth element in the table bases.table (the number of Ts in the sequence), we type: bases.table[[4]] # double brackets! ## [1] 2 Alternatively, you can use the name of the fourth element in the table (“John”) to find the value of that table element: bases.table[["T"]] ## [1] 2 5.5 Arguments Functions in R usually require arguments, which are input variables (i.e.. objects) that are passed to them, which they then carry out some operation on. For example, the log10() function is passed a number, and it then calculates the log to the base 10 of that number: log10(100) ## [1] 2 There’s a more generic function, log(), where we pass it not only a number to take the log of, but also the specific base of the logarithm. To take the log base 10 with the log() function we do this log(100, base = 10) ## [1] 2 We can also take logs with other bases, such as 2: log(100, base = 2) ## [1] 6.643856 5.6 Help files with help() and ? In R, you can get help about a particular function by using the help() function. For example, if you want help about the log10() function, you can type: help("log10") When you use the help() function, a box or web page will show up in one of the panes of RStudio with information about the function that you asked for help with. You can also use the ? next to the function like this ?log10 Help files are a mixed bag in R, and it can take some getting used to them. An excellent overview of this is Kieran Healy’s “How to read an R help page.” 5.7 Searching for functions with help.search() and RSiteSearch() If you are not sure of the name of a function, but think you know part of its name, you can search for the function name using the help.search() and RSiteSearch() functions. The help.search() function searches to see if you already have a function installed (from one of the R packages that you have installed) that may be related to some topic you’re interested in. RSiteSearch() searches all R functions (including those in packages that you haven’t yet installed) for functions related to the topic you are interested in. For example, if you want to know if there is a function to calculate the standard deviation (SD) of a set of numbers, you can search for the names of all installed functions containing the word “deviation” in their description by typing: help.search("deviation") Among the functions that were found, is the function sd() in the stats package (an R package that comes with the base R installation), which is used for calculating the standard deviation. Now, instead of searching just the packages we’ve have on our computer let’s search all R packages on CRAN. Let’s look for things related to DNA. Note that RSiteSearch() doesn’t provide output within RStudio, but rather opens up your web browser for you to display the results. RSiteSearch("DNA") The results of the RSiteSearch() function will be hits to descriptions of R functions, as well as to R mailing list discussions of those functions. 5.8 More on functions We can perform computations with R using objects such as scalars and vectors. For example, to calculate the average of the values in the vector myvector (i.e.. the average of 8, 6, 9, 10 and 5), we can use the mean() function: mean(myvector) # note: no " " ## [1] 7.6 We have been using built-in R functions such as mean(), length(), print(), plot(), etc. 5.8.1 Writing your own functions NOTE: *Writing your own functions is an advanced skills. New users can skip this section. We can also create our own functions in R to do calculations that you want to carry out very often on different input data sets. For example, we can create a function to calculate the value of 20 plus square of some input number: myfunction <- function(x) { return(20 + (x*x)) } This function will calculate the square of a number (x), and then add 20 to that value. The return() statement returns the calculated value. Once you have typed in this function, the function is then available for use. For example, we can use the function for different input numbers (e.g.. 10, 25): myfunction(10) ## [1] 120 5.9 Quiting R To quit R either close the program, or type: q() 5.10 Links and Further Reading Some links are included here for further reading. For a more in-depth introduction to R, a good online tutorial is available on the “Kickstarting R” website, cran.r-project.org/doc/contrib/Lemon-kickstart. There is another nice (slightly more in-depth) tutorial to R available on the “Introduction to R” website, cran.r-project.org/doc/manuals/R-intro.html. Chapter 3 of Danielle Navarro’s book is an excellent intro to the basics of R. "],["vectors-in-R.html", "Chapter 6 A primer for working with vectors 6.1 Preface 6.2 Vocab", " Chapter 6 A primer for working with vectors By: Avril Coghlan Adapted, edited and expanded: Nathan Brouwer (brouwern@gmail.com) under the Creative Commons 3.0 Attribution License (CC BY 3.0). 6.1 Preface This is a modification of part of“DNA Sequence Statistics (2)” from Avril Coghlan’s A little book of R for bioinformatics.. Most of text and code was originally written by Dr. Coghlan and distributed under the Creative Commons 3.0 license. 6.2 Vocab base R scalar, vector, matrix vectorized operation regular expressions "],["functions.html", "Chapter 7 Functions 7.1 Vectors in R 7.2 Math on vectors 7.3 Functions on vectors 7.4 Operations with two vectors 7.5 Subsetting vectors 7.6 Sequences of numbers 7.7 Vectors can hold numeric or character data 7.8 Regular expressions can modify character data", " Chapter 7 Functions seq() is(), is.vector(), is.matrix() gsub() 7.1 Vectors in R Variables in R include scalars, vectors, and lists. Functions in R carry out operations on variables, for example, using the log10() function to calculate the log to the base 10 of a scalar variable x, or using the mean() function to calculate the average of the values in a vector variable myvector. For example, we can use log10() on a scalar object like this: # store value in object x <- 100 # take log base 10 of object log10(x) ## [1] 2 Note that while mathematically x is a single number, or a scalar, R considers it to be a vector: is.vector(x) ## [1] TRUE There are many “is” commands. What is returned when you run is.matrix() on a vector? is.matrix(x) ## [1] FALSE Mathematically this is a bit odd, since often a vector is defined as a one-dimensional matrix, e.g., a single column or single row of a matrix. But in R land, a vector is a vector, and matrix is a matrix, and there are no explicit scalars. 7.2 Math on vectors Vectors can serve as the input for mathematical operations. When this is done R does the mathematical operation separately on each element of the vector. This is a unique feature of R that can be hard to get used to even for people with previous programming experience. Let’s make a vector of numbers: myvector <- c(30,16,303,99,11,111) What happens when we multiply myvector by 10? myvector*10 ## [1] 300 160 3030 990 110 1110 R has taken each of the 6 values, 30 through 111, of myvector and multiplied each one by 10, giving us 6 results. That is, what R did was ## 30*10 # first value of myvector ## 16*10 # second value of myvector ## 303*10 # .... ## 99*10 ## 111*10 # last value of myvector The normal order of operations rules apply to vectors as they do to operations we’re more used to. So multiplying myvector by 10 is the same whether you put he 10 before or after vector. That is myvector\\*10 is the same as 10\\*myvector. myvector*10 ## [1] 300 160 3030 990 110 1110 10*myvector ## [1] 300 160 3030 990 110 1110 What happen when you subtract 30 from myvector? Write the code below. myvector-30 ## [1] 0 -14 273 69 -19 81 So, what R did was ## 30-30 # first value of myvector ## 16-30 # second value of myvector ## 303-30 # .... ## 99-30 ## 111-30 # last value of myvector Again, myvector-30 is vectorized operation. You can also square a vector myvector^2 ## [1] 900 256 91809 9801 121 12321 Which is the same as ## 30^2 # first value of myvector ## 16^2 # second value of myvector ## 303^2 # .... ## 99^2 ## 111^2 # last value of myvector Also you can take the square root of a vector using the functions sqrt()… sqrt(myvector) ## [1] 5.477226 4.000000 17.406895 9.949874 3.316625 10.535654 …and take the log of a vector with log()… log(myvector) ## [1] 3.401197 2.772589 5.713733 4.595120 2.397895 4.709530 …and just about any other mathematical operation. Here we are working on a separate vector object; all of these rules apply to a column in a matrix or a dataframe. This attribute of R is called vectorization. When you run the code myvector*10 or log(myvector) you are doing a vectorized operation - its like normal math with special vector-based super power to get more done faster than you normally could. 7.3 Functions on vectors As we just saw, we can use functions on vectors. Typically these use the vectors as an input and all the numbers are processed into an output. Call the mean() function on the vector we made called myvector. mean(myvector) ## [1] 95 Note how we get a single value back - the mean of all the values in the vector. R saw that we had a vector of multiple and knew that the mean is a function that doesn’t get applied to single number, but sets of numbers. The function sd() calculates the standard deviation. Apply the sd() to myvector: sd(myvector) ## [1] 110.5061 7.4 Operations with two vectors You can also subtract one vector from another vector. This can be a little weird when you first see it. Make another vector with the numbers 5, 10, 15, 20, 25, 30. Call this myvector2: myvector2 <- c(5, 10, 15, 20, 25, 30) Now subtract myvector2 from myvector. What happens? myvector-myvector2 ## [1] 25 6 288 79 -14 81 7.5 Subsetting vectors You can extract an element of a vector by typing the vector name with the index of that element given in square brackets. For example, to get the value of the 3rd element in the vector myvector, we type: myvector[3] ## [1] 303 Extract the 4th element of the vector: myvector[4] ## [1] 99 You can extract more than one element by using a vector in the brackets: First, say I want to extract the 3rd and the 4th element. I can make a vector with 3 and 4 in it: nums <- c(3,4) Then put that vector in the brackets: myvector[nums] ## [1] 303 99 We can also do it directly like this, skipping the vector-creation step: myvector[c(3,4)] ## [1] 303 99 In the chunk below extract the 1st and 2nd elements: myvector[c(1,2)] ## [1] 30 16 7.6 Sequences of numbers Often we want a vector of numbers in sequential order. That is, a vector with the numbers 1, 2, 3, 4, … or 5, 10, 15, 20, … The easiest way to do this is using a colon 1:10 ## [1] 1 2 3 4 5 6 7 8 9 10 Note that in R 1:10 is equivalent to c(1:10) c(1:10) ## [1] 1 2 3 4 5 6 7 8 9 10 Usually to emphasize that a vector is being created I will use c(1:10) We can do any number to any numbers c(20:30) ## [1] 20 21 22 23 24 25 26 27 28 29 30 We can also do it in reverse. In the code below put 30 before 20: c(30:20) ## [1] 30 29 28 27 26 25 24 23 22 21 20 A useful function in R is the seq() function, which is an explicit function that can be used to create a vector containing a sequence of numbers that run from a particular number to another particular number. seq(1, 10) ## [1] 1 2 3 4 5 6 7 8 9 10 Using seq() instead of a : can be useful for readability to make it explicit what is going on. More importantly, seq has an argument by = ... so you can make a sequence of number with any interval between For example, if we want to create the sequence of numbers from 1 to 10 in steps of 1 (i.e.. 1, 2, 3, 4, … 10), we can type: seq(1, 10, by = 1) ## [1] 1 2 3 4 5 6 7 8 9 10 We can change the step size by altering the value of the by argument given to the function seq(). For example, if we want to create a sequence of numbers from 1-100 in steps of 20 (i.e.. 1, 21, 41, … 101), we can type: seq(1, 101, by = 20) ## [1] 1 21 41 61 81 101 7.7 Vectors can hold numeric or character data The vector we created above holds numeric data, as indicated by class() class(myvector) ## [1] "numeric" Vectors can also holder character data, like the genetic code: # vector of character data myvector <- c("A","T","G") # how it looks myvector ## [1] "A" "T" "G" # what is "is" class(myvector) ## [1] "character" 7.8 Regular expressions can modify character data We can use regular expressions to modify character data. For example, change the Ts to Us myvector <- gsub("T", "U", myvector) Now check it out myvector ## [1] "A" "U" "G" Regular expressions are a deep subject in computing. You can find some more information about them here. "],["plotting-vectors.html", "Chapter 8 Plotting vectors in base R 8.1 Preface 8.2 Plotting numeric data 8.3 Other plotting packages", " Chapter 8 Plotting vectors in base R By: Avril Coghlan Adapted, edited and expanded: Nathan Brouwer (brouwern@gmail.com) under the Creative Commons 3.0 Attribution License (CC BY 3.0). 8.1 Preface This is a modification of part of“DNA Sequence Statistics (2)” from Avril Coghlan’s A little book of R for bioinformatics.. Most of text and code was originally written by Dr. Coghlan and distributed under the Creative Commons 3.0 license. 8.2 Plotting numeric data R allows the production of a variety of plots, including scatterplots, histograms, piecharts, and boxplots. Usually we make plots from dataframes with 2 or more columns, but we can also make them from two separate vectors. This flexibility is useful, but also can cause some confusion. For example, if you have two equal-length vectors of numbers numeric.vect1 and numeric.vect1, you can plot a scatterplot of the values in myvector1 against the values in myvector2 using the base R plot()function. First, let’s make up some data in put it in vectors: numeric.vect1 <- c(10, 15, 22, 35, 43) numeric.vect2 <- c(3, 3.2, 3.9, 4.1, 5.2) Not plot with the base R plot() function: plot(numeric.vect1, numeric.vect2) Note that there is a comma between the two vector names. When building plots from dataframes you usually see a tilde (~), but when you have two vectors you can use just a comma. Also note the order of the vectors within the plot() command and which axes they appear on. The first vector is numeric.vect1 and it appears on the horizontal x-axis. If you want to label the axes on the plot, you can do this by giving the plot() function values for its optional arguments xlab = and ylab =: plot(numeric.vect1, # note again the comma, not a ~ numeric.vect2, xlab="vector1", ylab="vector2") We can store character data in vectors so if we want we could do this to set up our labels: mylabels <- c("numeric.vect1","numeric.vect2") Then use bracket notation to call the labels from the vector plot(numeric.vect1, numeric.vect2, xlab=mylabels[1], ylab=mylabels[2]) If we want we can use a tilde to make our plot like this: plot(numeric.vect2 ~ numeric.vect1) Note that now, numeric.vect2 is on the left and numeric.vect1 is on the right. This flexibility can be tricky to keep track of. We can also combine these vectors into a dataframe and plot the data by referencing the data frame. First, we combine the two separate vectors into a dataframe using the cbind() command. df <- cbind(numeric.vect1, numeric.vect2) Then we plot it like this, referencing the dataframe df via the data = ... argument. plot(numeric.vect2 ~ numeric.vect1, data = df) 8.3 Other plotting packages Base R has lots of plotting functions; additionally, people have written packages to implement new plotting capabilities. The package ggplot2 is currently the most popular plotting package, and ggpubr is a package which makes ggplot2 easier to use. For quick plots we’ll use base R functions, and when we get to more important things we’ll use ggplot2 and ggpubr. "],["R-objects.html", "Chapter 9 Intro to R objects 9.1 Commands used 9.2 R Objects 9.3 Differences between objects 9.4 The Data 9.5 The assignment operator “<-” makes object 9.6 Debrief", " Chapter 9 Intro to R objects By: Nathan Brouwer 9.1 Commands used <- c() length() dim() is() 9.2 R Objects Everything in R is an object, works with an object, tells you about an object, etc We’ll do a simple data analysis with a t.test and then look at properties of R objects There are several types of objects: vectors, matrices, lists, dataframes R objects can hold numbers, text, or both A typical dataframe has columns of numeric data and columns of text that represent factor variables (aka “categorical variables”) 9.3 Differences between objects Different objects are used and show up in different contexts. Most practical stats work in R is done with dataframes . A dataframe is kind of like a spreadsheet, loaded into R. For the sake of simplicity, we often load data in as a vector. This just makes things smoother when we are starting out. vectors pop up in many places, usually in a support role until you start doing more programming. matrices are occasionally used for applied stats stuff but show up more for programming. A matrix is like a stripped-down dataframe. lists show up everywhere, but you often don’t know it; many R functions make lists Understanding lists will help you efficiently work with stats output and make plots. 9.4 The Data We’ll use the following data to explore R objects. Motulsky 2nd Ed, Chapter 30, page 220, Table 30.1. Maximal relaxation of muscle strips of old and young rat bladders stimulated w/ high concentrations of nonrepinephrine (Frazier et al 2006). Response variable is %E.max 9.5 The assignment operator “<-” makes object The code above has made two objects. We can use several commands to learn about these objects. is(): what an object is, i.e., vector, matrix, list, dataframe length():how long an object is; only works with vectors and lists, not dataframes! dim(): how long AND how wide and object is; doesn’t work with vectors, only dataframes and matrices :( 9.5.1 is() What is our “old.E.max” object? is(old.E.max) ## [1] "numeric" "vector" is(young.E.max) ## [1] "numeric" "vector" Its a vector, containing numeric data. What if we made a vector like this? cat.variables <- c("old","old","old","old", "old","old","old","old","old") And used is() is(cat.variables) ## [1] "character" "vector" "data.frameRowLabels" ## [4] "SuperClassMethod" It tells us we have a vector, containing character data. Not sure why it feels the need to tell us all the other stuff… 9.5.2 length() Our vector has 9 elements, or is 9 elements long. length(old.E.max) ## [1] 9 Note that dim(), for dimension, doesn’t work with vectors! dim(old.E.max) ## NULL It would be nice if it said something like “1 x 9” for 1 row tall and 9 elements long. So it goes. 9.5.3 str() str() stands for “structure”. It summarizes info about an object; I find it most useful for looking at lists. If our vector here was really really long, str() would only show the first part of the vector str(old.E.max) ## num [1:9] 20.8 2.8 50 33.3 29.4 38.9 29.4 52.6 14.3 9.5.4 c() We typically use c() to gather together things like numbers, as we did to make our objects above. note: this is lower case “c”! Uppercase is something else For me, R’s font makes it hard sometimes to tell the difference between “c” and “C” If code isn’t working, one problem might be a “C” instead of a “c” Use c() to combine two objects old.plus.new <- c(old.E.max, young.E.max) Look at the length length(old.plus.new) ## [1] 17 Note that str() just shows us the first few digits, not all 17 str(old.plus.new) ## num [1:17] 20.8 2.8 50 33.3 29.4 38.9 29.4 52.6 14.3 45.5 ... 9.6 Debrief We can… learn about objects using length(), is(), str() access parts of list using $ (and also brackets) access parts of vectors using square brackets [ ] save the output of a model / test to an object access part of lists for plotting instead of copying stuff "],["nucleic-acids-review.html", "Chapter 10 Nucleic Acids 10.1 DNA and RNA 10.2 DNA Double-Helix Structure 10.3 RNA 10.4 Summary 10.5 Analysis Questions 10.6 Glossary 10.7 Contributors and Attributions", " Chapter 10 Nucleic Acids Authors: OpenStax / Libretext Formatted in RMarkdown by Nathan Brouwer under the Creative Commons Attribution License 4.0 license. This chapter was adapted from LibreText General Biology, Chapter 3, Section 3.5: Nucleic Acids. The LibreText book is based on OpenStax Biology 2nd edition, Chapter 3, Section 3.5: Nucleic Acids. A full list of authors is found under the Contributors and Attributions section at the end of this document. Nucleic acids are the most important macromolecules for the continuity of life. They carry the genetic blueprint of a cell and carry instructions for the functioning of the cell. 10.1 DNA and RNA The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material found in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is found in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes, the DNA is not enclosed in a membranous envelope. The entire genetic content of a cell is known as its genome, and the study of genomes is genomics. In eukaryotic cells but not in prokaryotes, DNA forms a complex with histone proteins to form chromatin, the substance of eukaryotic chromosomes. A chromosome may contain tens of thousands of genes. Many genes contain the information to make protein products; other genes code for RNA products. DNA controls all of the cellular activities by turning the genes “on” or “off.” The other type of nucleic acid, RNA, is mostly involved in protein synthesis. The DNA molecules never leave the nucleus but instead use an intermediary to communicate with the rest of the cell. This intermediary is the messenger RNA (mRNA). Other types of RNA—like rRNA, tRNA, and microRNA—are involved in protein synthesis and its regulation. DNA and RNA are made up of monomers known as nucleotides. The nucleotides combine with each other to form a polynucleotide, DNA or RNA. Each nucleotide is made up of three components: a nitrogenous base, a pentose (five-carbon) sugar, and a phosphate group (Figure 3.5.1). Each nitrogenous base in a nucleotide is attached to a sugar molecule, which is attached to one or more phosphate groups. Figure 10.1: A nucleotide is made up of three components: a nitrogenous base, a pentose sugar, and one or more phosphate groups. Carbon residues in the pentose are numbered 1′ through 5′ (the prime distinguishes these residues from those in the base, which are numbered without using a prime notation). The base is attached to the 1′ position of the ribose, and the phosphate is attached to the 5′ position. When a polynucleotide is formed, the 5′ phosphate of the incoming nucleotide attaches to the 3′ hydroxyl group at the end of the growing chain. Two types of pentose are found in nucleotides, deoxyribose (found in DNA) and ribose (found in RNA). Deoxyribose is similar in structure to ribose, but it has an H instead of an OH at the 2′ position. Bases can be divided into two categories: purines and pyrimidines. Purines have a double ring structure, and pyrimidines have a single ring. The nitrogenous bases, important components of nucleotides, are organic molecules and are so named because they contain carbon and nitrogen. They are bases because they contain an amino group that has the potential of binding an extra hydrogen, and thus, decreases the hydrogen ion concentration in its environment, making it more basic. Each nucleotide in DNA contains one of four possible nitrogenous bases: adenine (A), guanine (G) cytosine (C), and thymine (T). Adenine and guanine are classified as purines. The primary structure of a purine is two carbon-nitrogen rings. Cytosine, thymine, and uracil are classified as pyrimidines which have a single carbon-nitrogen ring as their primary structure (Figure 3.5.1). Each of these basic carbon-nitrogen rings has different functional groups attached to it. In molecular biology shorthand, the nitrogenous bases are simply known by their symbols A, T, G, C, and U. DNA contains A, T, G, and C whereas RNA contains A, U, G, and C. The pentose sugar in DNA is deoxyribose, and in RNA, the sugar is ribose (Figure 3.5.1). The difference between the sugars is the presence of the hydroxyl group on the second carbon of the ribose and hydrogen on the second carbon of the deoxyribose. The carbon atoms of the sugar molecule are numbered as 1′, 2′, 3′, 4′, and 5′ (1′ is read as “one prime”). The phosphate residue is attached to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms a 5′–3′ phosphodiester linkage. The phosphodiester linkage is not formed by simple dehydration reaction like the other linkages connecting monomers in macromolecules: its formation involves the removal of two phosphate groups. A polynucleotide may have thousands of such phosphodiester linkages. 10.2 DNA Double-Helix Structure DNA has a double-helix structure (Figure 3.5.2). The sugar and phosphate lie on the outside of the helix, forming the backbone of the DNA. The nitrogenous bases are stacked in the interior, like the steps of a staircase, in pairs; the pairs are bound to each other by hydrogen bonds. Every base pair in the double helix is separated from the next base pair by 0.34 nm. The two strands of the helix run in opposite directions, meaning that the 5′ carbon end of one strand will face the 3′ carbon end of its matching strand. (This is referred to as antiparallel orientation and is important to DNA replication and in many nucleic acid interactions.) Figure 10.2: Figure 3.5.2 : Native DNA is an antiparallel double helix. The phosphate backbone (indicated by the curvy lines) is on the outside, and the bases are on the inside. Each base from one strand interacts via hydrogen bonding with a base from the opposing strand. (credit: Jerome Walker/Dennis Myts) Only certain types of base pairing are allowed. For example, a certain purine can only pair with a certain pyrimidine. This means A can pair with T, and G can pair with C, as shown in Figure 3.5.3. This is known as the base complementary rule. In other words, the DNA strands are complementary to each other. If the sequence of one strand is AATTGGCC, the complementary strand would have the sequence TTAACCGG. During DNA replication, each strand is copied, resulting in a daughter DNA double helix containing one parental DNA strand and a newly synthesized strand. Art Connection Figure 3.5.3 : In a double stranded DNA molecule, the two strands run antiparallel to one another so that one strand runs 5′ to 3′ and the other 3′ to 5′. The phosphate backbone is located on the outside, and the bases are in the middle. Adenine forms hydrogen bonds (or base pairs) with thymine, and guanine base pairs with cytosine. A mutation occurs, and cytosine is replaced with adenine. What impact do you think this will have on the DNA structure? 10.3 RNA Ribonucleic acid, or RNA, is mainly involved in the process of protein synthesis under the direction of DNA. RNA is usually single-stranded and is made of ribonucleotides that are linked by phosphodiester bonds. A ribonucleotide in the RNA chain contains ribose (the pentose sugar), one of the four nitrogenous bases (A, U, G, and C), and the phosphate group. There are four major types of RNA: messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and microRNA (miRNA). The first, mRNA, carries the message from DNA, which controls all of the cellular activities in a cell. If a cell requires a certain protein to be synthesized, the gene for this product is turned “on” and the messenger RNA is synthesized in the nucleus. The RNA base sequence is complementary to the coding sequence of the DNA from which it has been copied. However, in RNA, the base T is absent and U is present instead. If the DNA strand has a sequence AATTGCGC, the sequence of the complementary RNA is UUAACGCG. In the cytoplasm, the mRNA interacts with ribosomes and other cellular machinery (Figure 3.5.4). Figure 3.5.4 : A ribosome has two parts: a large subunit and a small subunit. The mRNA sits in between the two subunits. A tRNA molecule recognizes a codon on the mRNA, binds to it by complementary base pairing, and adds the correct amino acid to the growing peptide chain. The mRNA is read in sets of three bases known as codons. Each codon codes for a single amino acid. In this way, the mRNA is read and the protein product is made. Ribosomal RNA (rRNA) is a major constituent of ribosomes on which the mRNA binds. The rRNA ensures the proper alignment of the mRNA and the ribosomes; the rRNA of the ribosome also has an enzymatic activity (peptidyl transferase) and catalyzes the formation of the peptide bonds between two aligned amino acids. Transfer RNA (tRNA) is one of the smallest of the four types of RNA, usually 70–90 nucleotides long. It carries the correct amino acid to the site of protein synthesis. It is the base pairing between the tRNA and mRNA that allows for the correct amino acid to be inserted in the polypeptide chain. microRNAs are the smallest RNA molecules and their role involves the regulation of gene expression by interfering with the expression of certain mRNA messages. Table 3.5.1 below summarizes features of DNA and RNA. Table 3.5.1 *: Features of DNA and RNA. Features of DNA and RNA DNA RNA Function Carries genetic information Involved in protein synthesis Location Remains in the nucleus Leaves the nucleus Structure Double helix Usually single-stranded Sugar Deoxyribose Ribose Pyrimidines Cytosine, thymine Cytosine, uracil Purines Adenine, guanine Adenine, guanine Even though the RNA is single stranded, most RNA types show extensive intramolecular base pairing between complementary sequences, creating a predictable three-dimensional structure essential for their function. As you have learned, information flow in an organism takes place from DNA to RNA to protein. DNA dictates the structure of mRNA in a process known as transcription, and RNA dictates the structure of protein in a process known as translation. This is known as the Central Dogma of Life, which holds true for all organisms; however, exceptions to the rule occur in connection with viral infections. Link to Learning To learn more about DNA, explore the Howard Hughes Medical Institute BioInteractive animations on the topic of DNA.* 10.4 Summary Nucleic acids are molecules made up of nucleotides that direct cellular activities such as cell division and protein synthesis. Each nucleotide is made up of a pentose sugar, a nitrogenous base, and a phosphate group. There are two types of nucleic acids: DNA and RNA. DNA carries the genetic blueprint of the cell and is passed on from parents to offspring (in the form of chromosomes). It has a double-helical structure with the two strands running in opposite directions, connected by hydrogen bonds, and complementary to each other. RNA is single-stranded and is made of a pentose sugar (ribose), a nitrogenous base, and a phosphate group. RNA is involved in protein synthesis and its regulation. Messenger RNA (mRNA) is copied from the DNA, is exported from the nucleus to the cytoplasm, and contains information for the construction of proteins. Ribosomal RNA (rRNA) is a part of the ribosomes at the site of protein synthesis, whereas transfer RNA (tRNA) carries the amino acid to the site of protein synthesis. microRNA regulates the use of mRNA for protein synthesis. Art Connections 10.5 Analysis Questions Free Response 10.6 Glossary deoxyribonucleic acid (DNA): double-helical molecule that carries the hereditary information of the cell messenger RNA (mRNA): DNA that carries information from DNA to ribosomes during protein synthesis nucleic acid: biological macromolecule that carries the genetic blueprint of a cell and carries instructions for the functioning of the cell nucleotide: monomer of nucleic acids; contains a pentose sugar, one or more phosphate groups, and a nitrogenous base phosphodiester: linkage covalent chemical bond that holds together the polynucleotide chains with a phosphate group linking two pentose sugars of neighboring nucleotides polynucleotide: long chain of nucleotides purine: type of nitrogenous base in DNA and RNA; adenine and guanine are purines, pyrimidine: type of nitrogenous base in DNA and RNA; cytosine, thymine, and uracil are pyrimidines ribonucleic acid (RNA): single-stranded, often internally base paired, molecule that is involved in protein synthesis ribosomal RNA (rRNA): RNA that ensures the proper alignment of the mRNA and the ribosomes during protein synthesis and catalyzes the formation of the peptide linkage transcription: process through which messenger RNA forms on a template of DNA transfer RNA (tRNA): RNA that carries activated amino acids to the site of protein synthesis on the ribosome translation: process through which RNA directs the formation of protein 10.7 Contributors and Attributions Connie Rye (East Mississippi Community College), Robert Wise (University of Wisconsin, Oshkosh), Vladimir Jurukovski (Suffolk County Community College), Jean DeSaix (University of North Carolina at Chapel Hill), Jung Choi (Georgia Institute of Technology), Yael Avissar (Rhode Island College) among other contributing authors. Original content by OpenStax (CC BY 4.0; Download for free at http://cnx.org/contents/185cbf87-c72...f21b5eabd@9.87). "],["proteins-review.html", "Chapter 11 Proteins 11.1 Types and Functions of Proteins 11.2 Amino Acids 11.3 Evolution Connection: 11.4 Protein Structure 11.5 Denaturation and Protein Folding 11.6 Summary 11.7 Art Connections 11.8 Review Questions 11.9 Free Response 11.10 Glossary 11.11 Contributors and Attributions", " Chapter 11 Proteins Authors: OpenStax / LibreText. Formatted in RMarkdown by Nathan Brouwer under the Creative Commons Attribution License 4.0 license. This chapter was adapted from LibreText General Biology, Chapter 3, Section 3.4: Proteins. The LibreText book is based on OpenStax Biology 2nd edition, Chapter 3, Section 3.4: Nucleic Acids. A full list of authors is found under the Contributors and Attributions section at the end of this document. 11.0.1 Skills to develop: Describe the functions proteins perform in the cell and in tissues Discuss the relationship between amino acids and proteins Explain the four levels of protein organization Describe the ways in which protein shape and function are linked Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective; they may serve in transport, storage, or membranes; or they may be toxins or enzymes. Each cell in a living system may contain thousands of proteins, each with a unique function. Their structures, like their functions, vary greatly. They are all, however, polymers of amino acids, arranged in a linear sequence. 11.1 Types and Functions of Proteins Enzymes, which are produced by living cells, are catalysts in biochemical reactions (like digestion) and are usually complex or conjugated proteins. Each enzyme is specific for the substrate (a reactant that binds to an enzyme) it acts on. The enzyme may help in breakdown, rearrangement, or synthesis reactions. Enzymes that break down their substrates are called catabolic enzymes, enzymes that build more complex molecules from their substrates are called anabolic enzymes, and enzymes that affect the rate of reaction are called catalytic enzymes. It should be noted that all enzymes increase the rate of reaction and, therefore, are considered to be organic catalysts. An example of an enzyme is salivary amylase, which hydrolyzes its substrate amylose, a component of starch. Hormones are chemical-signaling molecules, usually small proteins or steroids, secreted by endocrine cells that act to control or regulate specific physiological processes, including growth, development, metabolism, and reproduction. For example, insulin is a protein hormone that helps to regulate the blood glucose level. Proteins have different shapes and molecular weights; some proteins are globular in shape whereas others are fibrous. For example, hemoglobin is a globular protein, but collagen, found in our skin, is a fibrous protein. Protein shape is critical to its function, and this shape is maintained by many different types of chemical bonds. Changes in temperature, pH, and exposure to chemicals may lead to permanent changes in the shape of the protein, leading to loss of function, known as denaturation. All proteins are made up of different arrangements of the same 20 types of amino acids. 11.2 Amino Acids Amino acids are the monomers that make up proteins. Each amino acid has the same fundamental structure, which consists of a central carbon atom, also known as the alpha (α) carbon, bonded to an amino group (NH2), a carboxyl group (COOH), and to a hydrogen atom. Every amino acid also has another atom or group of atoms bonded to the central atom known as the R group (Figure 3.4.1). Figure 11.1: Amino acids have a central asymmetric carbon to which an amino group, a carboxyl group, a hydrogen atom, and a side chain (R group) are attached. The name “amino acid” is derived from the fact that they contain both an amino group and carboxyl-acid-group in their basic structure. For each amino acid, the R group (or side chain) is different (Figure 3.4.2). Figure 11.2: There are 20 common amino acids commonly found in proteins, each with a different R group (variant group) that determines its chemical nature. Which categories of amino acid would you expect to find on the surface of a soluble protein, and which would you expect to find in the interior? What distribution of amino acids would you expect to find in a protein embedded in a lipid bilayer? The chemical nature of the side chain determines the nature of the amino acid (that is, whether it is acidic, basic, polar, or nonpolar). For example, the amino acid glycine has a hydrogen atom as the R group. Amino acids such as valine, methionine, and alanine are nonpolar or hydrophobic, while amino acids such as serine, threonine, and cysteine are polar and have hydrophilic side chains. The side chains of lysine and arginine are positively charged, and therefore these amino acids are also known as basic amino acids. Proline has an R group that is linked to the amino group, forming a ring-like structure. Proline is an exception to the standard structure of an amino acid since its amino group is not separate from the side chain Amino acids are represented by a single upper case letter or a three-letter abbreviation. For example, valine is known by the letter V or the three-letter code val. The sequence and the number of amino acids ultimately determine a protein’s shape, size, and function. Each amino acid is attached to another amino acid by a covalent bond, known as a peptide bond, which is formed by a dehydration reaction. The carboxyl group of one amino acid and the amino group of the incoming amino acid combine, releasing a molecule of water. The resulting bond is the peptide bond Figure 11.3: Peptide bond formation is a dehydration synthesis reaction. The carboxyl group of one amino acid is linked to the amino group of the incoming amino acid. In the process, a molecule of water is released. The products formed by such linkages are called peptides. As more amino acids join to this growing chain, the resulting chain is known as a polypeptide. Each polypeptide has a free amino group at one end. This end is called the N-terminal, or the amino terminal, and the other end has a free carboxyl group, also known as the C- or carboxyl terminal. The terms polypeptide and protein are sometimes used interchangeably, a polypeptide is technically a polymer of amino acids, whereas the term protein is more formally used for a polypeptide or that have combined together, often have bound non-peptide prosthetic groups, have a distinct shape, and have a unique function. After protein synthesis (translation), most proteins are modified. These are known as post-translational modifications. They may undergo cleavage, phosphorylation (addition of a phosphate group), or may require the addition of other chemical groups. Only after these modifications is the protein completely functional. 11.3 Evolution Connection: The Evolutionary Significance of Cytochrome: Cytochrome c is an important component of the electron transport chain, a part of cellular respiration, and it is normally found in the cellular organelle, the mitochondrion. This protein has a heme prosthetic group, and the central ion of the heme gets alternately reduced and oxidized during electron transfer. Because this essential protein’s role in producing cellular energy is crucial, it has changed very little over millions of years. Protein sequencing has shown that there is a considerable amount of cytochrome c amino acid sequence homology among different species; in other words, evolutionary kinship can be assessed by measuring the similarities or differences among various species’ DNA or protein sequences. Scientists have determined that human cytochrome c contains 104 amino acids. For each cytochrome c molecule from different organisms that has been sequenced to date, 37 of these amino acids appear in the same position in all samples of cytochrome c. This indicates that there may have been a common ancestor. On comparing the human and chimpanzee protein sequences, no sequence difference was found. When human and rhesus monkey sequences were compared, the single difference found was in one amino acid. In another comparison, human to yeast sequencing shows a difference in the 44th position. 11.4 Protein Structure The shape of a protein is critical to its function. For example, an enzyme can bind to a specific substrate at a site known as the active site. If this active site is altered because of local changes or changes in overall protein structure, the enzyme may be unable to bind to the substrate. To understand how the protein gets its final shape or conformation, we need to understand the four levels of protein structure: primary, secondary, tertiary, and quaternary. 11.4.1 Primary Structure The unique sequence of amino acids in a polypeptide chain is its primary structure. For example, the pancreatic hormone insulin has two polypeptide chains, A and B, and they are linked together by disulfide bonds. The N terminal amino acid of the A chain is glycine, whereas the C terminal amino acid is asparagine (Figure 3.4.4). The sequences of amino acids in the A and B chains are unique to insulin. Figure 11.4: Bovine serum insulin is a protein hormone made of two peptide chains, A (21 amino acids long) and B (30 amino acids long). In each chain, primary structure is indicated by three-letter abbreviations that represent the names of the amino acids in the order they are present. The amino acid cysteine (cys) has a sulfhydryl (SH) group as a side chain. Two sulfhydryl groups can react in the presence of oxygen to form a disulfide (S-S) bond. Two disulfide bonds connect the A and B chains together, and a third helps the A chain fold into the correct shape. Note that all disulfide bonds are the same length, but are drawn different sizes for clarity. The unique sequence for every protein is ultimately determined by the gene encoding the protein. A change in nucleotide sequence of the gene’s coding region may lead to a different amino acid being added to the growing polypeptide chain, causing a change in protein structure and function. In sickle cell anemia, the hemoglobin beta chain (a small portion of which is shown in Figure 3.4.5) has a single amino acid substitution, causing a change in protein structure and function. Specifically, the amino acid glutamic acid is substituted by valine in the beta chain. What is most remarkable to consider is that a hemoglobin molecule is made up of two alpha chains and two beta chains that each consist of about 150 amino acids. The molecule, therefore, has about 600 amino acids. The structural difference between a normal hemoglobin molecule and a sickle cell molecule—which dramatically decreases life expectancy—is a single amino acid of the 600. What is even more remarkable is that those 600 amino acids are encoded by three nucleotides each, and the mutation is caused by a single base change (point mutation), 1 in 1800 bases. Figure 11.5: The beta chain of hemoglobin is 147 residues in length, yet a single amino acid substitution leads to sickle cell anemia. In normal hemoglobin, the amino acid at position seven is glutamate. In sickle cell hemoglobin, this glutamate is replaced by a valine. Because of this change of one amino acid in the chain, hemoglobin molecules form long fibers that distort the biconcave, or disc-shaped, red blood cells and assume a crescent or “sickle” shape, which clogs arteries (Figure 3.4.6). This can lead to myriad serious health problems such as breathlessness, dizziness, headaches, and abdominal pain for those affected by this disease. Figure 11.6: In this blood smear, visualized at 535x magnification using bright field microscopy, sickle cells are crescent shaped, while normal cells are disc-shaped. (credit: modification of work by Ed Uthman; scale-bar data from Matt Russell) 11.4.2 Secondary Structure The local folding of the polypeptide in some regions gives rise to the secondary structure of the protein. The most common are the α-helix and beta-pleated sheet structures (Figure 3.4.7). Both structures are the α-helix structure—the helix held in shape by hydrogen bonds. The hydrogen bonds form between the oxygen atom in the carbonyl group in one amino acid and another amino acid that is four amino acids farther along the chain. Figure 11.7: The α-helix and β-pleated sheet are secondary structures of proteins that form because of hydrogen bonding between carbonyl and amino groups in the peptide backbone. Certain amino acids have a propensity to form an α-helix, while others have a propensity to form a β-pleated sheet. Every helical turn in an alpha helix has 3.6 amino acid residues. The R groups (the variant groups) of the polypeptide protrude out from the α-helix chain. In the beta-pleated sheet, the “pleats” are formed by hydrogen bonding between atoms on the backbone of the polypeptide chain. The R groups are attached to the carbons and extend above and below the folds of the pleat. The pleated segments align parallel or antiparallel to each other, and hydrogen bonds form between the partially positive nitrogen atom in the amino group and the partially negative oxygen atom in the carbonyl group of the peptide backbone. The α-helix and beta-pleated sheet structures are found in most globular and fibrous proteins and they play an important structural role. 11.4.3 Tertiary Structure The unique three-dimensional structure of a polypeptide is its tertiary structure (Figure 3.4.8). This structure is in part due to chemical interactions at work on the polypeptide chain. Primarily, the interactions among R groups creates the complex three- dimensional tertiary structure of a protein. The nature of the R groups found in the amino acids involved can counteract the formation of the hydrogen bonds described for standard secondary structures. For example, R groups with like charges are repelled by each other and those with unlike charges are attracted to each other (ionic bonds). When protein folding takes place, the hydrophobic R groups of nonpolar amino acids lay in the interior of the protein, whereas the hydrophilic R groups lay on the outside. The former types of interactions are also known as hydrophobic interactions. Interaction between cysteine side chains forms disulfide linkages in the presence of oxygen, the only covalent bond forming during protein folding. Figure 11.8: The tertiary structure of proteins is determined by a variety of chemical interactions. These include hydrophobic interactions, ionic bonding, hydrogen bonding and disulfide linkages. All of these interactions, weak and strong, determine the final three-dimensional shape of the protein. When a protein loses its three-dimensional shape, it may no longer be functional. 11.4.4 Quaternary Structure In nature, some proteins are formed from several polypeptides, also known as subunits, and the interaction of these subunits forms the quaternary structure. Weak interactions between the subunits help to stabilize the overall structure. For example, insulin (a globular protein) has a combination of hydrogen bonds and disulfide bonds that cause it to be mostly clumped into a ball shape. Insulin starts out as a single polypeptide and loses some internal sequences in the presence of post-translational modification after the formation of the disulfide linkages that hold the remaining chains together. Silk (a fibrous protein), however, has a beta-pleated sheet structure that is the result of hydrogen bonding between different chains. The four levels of protein structure (primary, secondary, tertiary, and quaternary) are illustrated in Figure 3.4.9. Figure 11.9: The four levels of protein structure can be observed in these illustrations. (credit: modification of work by National Human Genome Research Institute) 11.5 Denaturation and Protein Folding Each protein has its own unique sequence and shape that are held together by chemical interactions. If the protein is subject to changes in temperature, pH, or exposure to chemicals, the protein structure may change, losing its shape without losing its primary sequence in what is known as denaturation. Denaturation is often reversible because the primary structure of the polypeptide is conserved in the process if the denaturing agent is removed, allowing the protein to resume its function. Sometimes denaturation is irreversible, leading to loss of function. One example of irreversible protein denaturation is when an egg is fried. The albumin protein in the liquid egg white is denatured when placed in a hot pan. Not all proteins are denatured at high temperatures; for instance, bacteria that survive in hot springs have proteins that function at temperatures close to boiling. The stomach is also very acidic, has a low pH, and denatures proteins as part of the digestion process; however, the digestive enzymes of the stomach retain their activity under these conditions. Protein folding is critical to its function. It was originally thought that the proteins themselves were responsible for the folding process. Only recently was it found that often they receive assistance in the folding process from protein helpers known as chaperones (or chaperonins) that associate with the target protein during the folding process. They act by preventing aggregation of polypeptides that make up the complete protein structure, and they disassociate from the protein once the target protein is folded. 11.6 Summary Proteins are a class of macromolecules that perform a diverse range of functions for the cell. They help in metabolism by providing structural support and by acting as enzymes, carriers, or hormones. The building blocks of proteins (monomers) are amino acids. Each amino acid has a central carbon that is linked to an amino group, a carboxyl group, a hydrogen atom, and an R group or side chain. There are 20 commonly occurring amino acids, each of which differs in the R group. Each amino acid is linked to its neighbors by a peptide bond. A long chain of amino acids is known as a polypeptide. Proteins are organized at four levels: primary, secondary, tertiary, and (optional) quaternary. The primary structure is the unique sequence of amino acids. The local folding of the polypeptide to form structures such as the α helix and beta-pleated sheet constitutes the secondary structure. The overall three-dimensional structure is the tertiary structure. When two or more polypeptides combine to form the complete protein structure, the configuration is known as the quaternary structure of a protein. Protein shape and function are intricately linked; any change in shape caused by changes in temperature or pH may lead to protein denaturation and a loss in function. 11.7 Art Connections Which categories of amino acid would you expect to find on the surface of a soluble protein, and which would you expect to find in the interior? What distribution of amino acids would you expect to find in a protein embedded in a lipid bilayer? Polar and charged amino acid residues (the remainder after peptide bond formation) are more likely to be found on the surface of soluble proteins where they can interact with water, and nonpolar (e.g., amino acid side chains) are more likely to be found in the interior where they are sequestered from water. In membrane proteins, nonpolar and hydrophobic amino acid side chains associate with the hydrophobic tails of phospholipids, while polar and charged amino acid side chains interact with the polar head groups or with the aqueous solution. However, there are exceptions. Sometimes, positively and negatively charged amino acid side chains interact with one another in the interior of a protein, and polar or charged amino acid side chains that interact with a ligand can be found in the ligand binding pocket. 11.8 Review Questions The monomers that make up proteins are called 1. nucleotides 1. disaccharides 1. amino acids 1. chaperones Answer: C The α helix and the beta-pleated sheet are part of which protein structure? primary secondary tertiary quaternary Answer: B 11.9 Free Response Explain what happens if even one amino acid is substituted for another in a polypeptide chain. Provide a specific example. A change in gene sequence can lead to a different amino acid being added to a polypeptide chain instead of the normal one. This causes a change in protein structure and function. For example, in sickle cell anemia, the hemoglobin beta chain has a single amino acid substitution—the amino acid glutamic acid in position six is substituted by valine. Because of this change, hemoglobin molecules form aggregates, and the disc-shaped red blood cells assume a crescent shape, which results in serious health problems. Describe the differences in the four protein structures. The sequence and number of amino acids in a polypeptide chain is its primary structure. The local folding of the polypeptide in some regions is the secondary structure of the protein. The three-dimensional structure of a polypeptide is known as its tertiary structure, created in part by chemical interactions such as hydrogen bonds between polar side chains, van der Waals interactions, disulfide linkages, and hydrophobic interactions. Some proteins are formed from multiple polypeptides, also known as subunits, and the interaction of these subunits forms the quaternary structure. 11.10 Glossary alpha-helix structure (α-helix): type of secondary structure of proteins formed by folding of the polypeptide into a helix shape with hydrogen bonds stabilizing the structure amino acid: monomer of a protein; has a central carbon or alpha carbon to which an amino group, a carboxyl group, a hydrogen, and an R group or side chain is attached; the R group is different for all 20 amino acids beta-pleated sheet (beta-pleated): secondary structure found in proteins in which “pleats” are formed by hydrogen bonding between atoms on the backbone of the polypeptide chain chaperone: (also, chaperonin) protein that helps nascent protein in the folding process denaturation: loss of shape in a protein as a result of changes in temperature, pH, or exposure to chemicals enzyme: catalyst in a biochemical reaction that is usually a complex or conjugated protein hormone: chemical signaling molecule, usually protein or steroid, secreted by endocrine cells that act to control or regulate specific physiological processes peptide bond: bond formed between two amino acids by a dehydration reaction polypeptide: long chain of amino acids linked by peptide bonds primary structure: linear sequence of amino acids in a protein protein: biological macromolecule composed of one or more chains of amino acids quaternary structure: association of discrete polypeptide subunits in a protein secondary structure: regular structure formed by proteins by intramolecular hydrogen bonding between the oxygen atom of one amino acid residue and the hydrogen attached to the nitrogen atom of another amino acid residue tertiary structure: three-dimensional conformation of a protein, including interactions between secondary structural elements; formed from interactions between amino acid side chains 11.11 Contributors and Attributions Connie Rye (East Mississippi Community College), Robert Wise (University of Wisconsin, Oshkosh), Vladimir Jurukovski (Suffolk County Community College), Jean DeSaix (University of North Carolina at Chapel Hill), Jung Choi (Georgia Institute of Technology), Yael Avissar (Rhode Island College) among other contributing authors. Original content by OpenStax (CC BY 4.0; Download for free at http://cnx.org/contents/185cbf87-c72...f21b5eabd@9.87). "],["ncbi-the-national-center-for-biotechnology-information.html", "Chapter 12 NCBI: The National Center for Biotechnology Information 12.1 GenBank sequence database 12.2 PubMed and PubMed Central article database 12.3 Entrez 12.4 BLAST", " Chapter 12 NCBI: The National Center for Biotechnology Information NOTE: The following material was partially adapted by N. Brouwer from Wikipedia. See the underlying .Rmd file for information on specific paragraphs from Wikipedia. The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It was founded in 1988 and is approved and funded by the government of the United States. The NCBI houses a series of databases relevant to the basic and applied life sciences and is an important resource for bioinformatics tools and services. Major databases include GenBank for DNA sequences and PubMed, a bibliographic database for biomedical literature. All these databases are available online through the Entrez search engine. In this chapter we’ll briefly discuss the major databases, following up with specific details in subsequent chapters. One thing to note is that in practice people do no always adhere to the specific names of databases and other tools. For example, someone may say “I searched NCBI for sequences.” It would be more accurate to say “I used Entrez to search GenBank for sequences.” For in-depth details on all the features see Database resources of the National Center for Biotechnology Information 12.1 GenBank sequence database The GenBank sequence database is an open access collection of publicly available DNA and protein sequences. If you’ve work with sequence data, you’ll work with GenBank. GenBank is the actual database, and it can be searched several ways. For example, you can search for a sequence by its ID number (accession number) if you know it, or do a BLAST search using an actual sequence to look for similar sequences. A key component of GenBank are the GeneBank Records, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene pallysin from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides metadata, such as the scientific name of the organism (Treponema pallidum), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene. A key feature of PubMed records is that they are hyperlinked to other NCBI databases. For example, you can click link under the name of the paper which reported the sequence of the gene and it will take you to the PubMed record for that paper (see below). You can also click the “Run BLAST” link and you can search the database for similar sequences. This protein coded for by this particular gene has had its structured solved using x-ray crystallography, and you can see these results under “Protein 3D Structure.” In a later chapter we’ll get to know these records in further detail. 12.2 PubMed and PubMed Central article database PubMed and PubMed Central (PMC) are databases of scientific articles related to data contained in NCBI databases. PubMed contains basic bibliographic information, the abstract, relevant links to sequences, and to the websites of the actual publishers of the papers. If the text of an article is open-access, PMC should have a copy of it. Articles in PMC contain relevant hyperlinks, such as to any sequences that are mentioned. For example, the image below show where the sequence of Tp0751 is mentioned in a paper and linked to the GenBank record we looked at above. 12.3 Entrez https://en.wikipedia.org/wiki/Entrez “Entrez is a federated search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name”Entrez” (a greeting meaning “Come in” in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI.” “Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system.” 12.4 BLAST https://en.wikipedia.org/wiki/BLAST_(biotechnology) “BLAST (basic local alignment search tool)is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence.” "],["introduction-to-biological-sequences-databases.html", "Chapter 13 Introduction to biological sequences databases 13.1 Topics 13.2 Introduction 13.3 Biological sequence databases 13.4 The NCBI Sequence Database 13.5 The NCBI Sub-Databases 13.6 NCBI GenBank Record Format 13.7 The FASTA file format 13.8 RefSeq 13.9 Querying the NCBI Database 13.10 Querying the NCBI Database via the NCBI Website (for reference) 13.11 Example: finding the sequences published in Nature 460:352-358 (for reference)", " Chapter 13 Introduction to biological sequences databases library(compbio4all) By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). 13.1 Topics NCBI vs. EMBL vs. DDBJ annotation Nucleotide vs. Protein vs. EST vs. Genome PubMed GenBank Records FASTA file format FASTA header line RefSeq refining searches 13.2 Introduction NCBI is the National Center for Biotechnology Information. The NCBI Webiste is the entry point to a large number of databases giving access to biological sequences (DNA, RNA, protein) and biology-related publications. When scientists sequence DNA, RNA and proteins they typically publish their data via databases with the NCBI. Each is given a unique identification number known as an accession number. For example, each time a unique human genome sequence is produced it is uploaded to the relevant databases, assigned a unique accession, and a website created to access it. Sequence are also cross-referenced to related papers, so you can start with a sequence and find out what scientific paper it was used in, or start with a paper and see if any sequences are associated with it. This chapter provides an introduction to the general search features of the NCBI databases via the interface on the website, including how to locate sequences using accession numbers and other search parameters, such as specific authors or papers. Subsequent chapters will introduce advanced search features, and how to carry out searches using R commands. One consequence of the explosion of biological sequences used in publications is that the system of databases has become fairly complex. There are databases for different types of data, different types of molecules, data from individual experiments showing genetic variation and also the consensus sequence for a given molecule. Luckily, if you know the accession number of the sequence you are looking for – which will be our starting point throughout this book – its fairly straight forward. There are numerous other books on bioinformatics and genomics that provide all details if you need to do more complex searches. In this chapter we’ll typically refer generically to “NCBI data” and “NCBI databases.” This is a simplification, since NCBI is the name of the organization and the databases and search engines often have specific names. 13.3 Biological sequence databases Almost published biological sequences are available online, as it is a requirement of every scientific journal that any published DNA or RNA or protein sequence must be deposited in a public database. The main resources for storing and distributing sequence data are three large databases: USA: NCBI database (www.ncbi.nlm.nih.gov/) Europe: European Molecular Biology Laboratory (EMBL) database (https://www.ebi.ac.uk/ena) Japan: DNA Database of Japan (DDBJ) database (www.ddbj.nig.ac.jp/). These databases collect all publicly available DNA, RNA and protein sequence data and make it available for free. They exchange data nightly, so contain essentially the same data. The redundancy among the databases allows them to serve different communities (e.g. native languages), provide different additional services such as tutorials, and assure that the world’s scientists have their data backed up in different physical locations – a key component of good data management! 13.4 The NCBI Sequence Database In this chapter we will explore the NCBI sequence database using the accession number NC_001477, which is for the complete DEN-1 Dengue virus genome sequence. The accession number is reported in scientific papers originally describing the sequence, and also in subsequent papers that use that particular sequence. In addition to the sequence itself, for each sequence the NCBI database also stores some additional annotation data, such as the name of the species it comes from, references to publications describing that sequence, information on the structure of the proteins coded by the sequence, etc. Some of this annotation data was added by the person who sequenced a sequence and submitted it to the NCBI database, while some may have been added later by a human curator working for NCBI. 13.5 The NCBI Sub-Databases The NCBI database contains several sub-databases, the most important of which are: Nucleotide database: contains DNA and RNA sequences Protein database: contains protein sequences EST database: contains ESTs (expressed sequence tags), which are short sequences derived from mRNAs. (This terminology is likely to be unfamiliar because it is not often used in introductory biology courses. The “Expressed” of EST comes from the fact that mRNA is the result of gene expression.) Genome database: contains the DNA sequences for entire genomes PubMed: contains data on scientific publications From the main NCBI website you can initiate a search and it will look for hits across all the databases. You can narrow your search by selecting a particular database. 13.6 NCBI GenBank Record Format As mentioned above, for each sequence the NCBI database stores some extra information such as the species that it came from, publications describing the sequence, etc. This information is stored in the GenBank entry (aka GenBank Record) for the sequence. The GenBank entry for a sequence can be viewed by searching the NCBI database for the accession number for that sequence. To view the GenBank entry for the DEN-1 Dengue virus, follow these steps: Go to the NCBI website (www.ncbi.nlm.nih.gov). Search for the accession number NC_001477. Since we searched for a particular accession we are only returned a single main result which is titled “NUCLEOTIDE SEQUENCE: Dengue virus 1, complete genome.” Click on “Dengue virus 1, complete genome” to go to the GenBank entry. The GenBank entry for an accession contains a LOT of information about the sequence, such as papers describing it, features in the sequence, etc. The DEFINITION field gives a short description for the sequence. The ORGANISM field in the NCBI entry identifies the species that the sequence came from. The REFERENCE field contains scientific publications describing the sequence. The FEATURES field contains information about the location of features of interest inside the sequence, such as regulatory sequences or genes that lie inside the sequence. The ORIGIN field gives the sequence itself. 13.7 The FASTA file format The FASTA file format is a simple file format commonly used to store and share sequence information. When you download sequences from databases such as NCBI you usually want FASTA files. The first line of a FASTA file starts with the “greater than” character (>) followed by a name and/or description for the sequence. Subsequent lines contain the sequence itself. A short FASTA file might contain just something like this: ## >mysequence1 ## ACATGAGACAGACAGACCCCCAGAGACAGACCCCTAGACACAGAGAGAG ## TATGCAGGACAGGGTTTTTGCCCAGGGTGGCAGTATG A FASTA file can contain the sequence for a single, an entire genome, or more than one sequence. If a FASTA file contains many sequences, then for each sequence it will have a header line starting with a greater than character followed by the sequence itself. This is what a FASTA file with two sequence looks like. ## >mysequence1 ## ACATGAGACAGACAGACCCCCAGAGACAGACCCCTAGACACAGAGAGAG ## TATGCAGGACAGGGTTTTTGCCCAGGGTGGCAGTATG ## ## >mysequence2 ## AGGATTGAGGTATGGGTATGTTCCCGATTGAGTAGCCAGTATGAGCCAG ## AGTTTTTTACAAGTATTTTTCCCAGTAGCCAGAGAGAGAGTCACCCAGT ## ACAGAGAGC 13.8 RefSeq When carrying out searches of the NCBI database, it is important to bear in mind that the database may contain redundant sequences for the same gene that were sequenced by different laboratories and experimental. This is because many different labs have sequenced the gene, and submitted their sequences to the NCBI database, and variation exists between individual organisms due to population-level variation due to previous mutations and also potential recent spontaneous mutations. There also can be some error in the sequencing process that results in differences between sequences. There are also many different types of nucleotide sequences and protein sequences in the NCBI database. With respect to nucleotide sequences, some many be entire genomic DNA sequences, some may be mRNAs, and some may be lower quality sequences such as expressed sequence tags (ESTs, which are derived from parts of mRNAs), or DNA sequences of contigs from genome projects. That is, you can end up with an entry in the protein database based on sequence derived from a genomic sequence, from sequencing just the gene, and from other routes. Furthermore, some sequences may be manually curated by NCBI staff so that the associated entries contain extra information, but the majority of sequences are uncurated. Therefore, NCBI databases often contains redundant information for a gene, contains sequences of varying quality, and contains both uncurated and curated data. As a result, NCBI has made a special database called RefSeq (reference sequence database), which is a subset of the NCBI database. The data in RefSeq is manually curated, is high quality sequence data, and is non-redundant; this means that each gene (or splice-form / isoform of a gene, in the case of eukaryotes), protein, or genome sequence is only represented once. The data in RefSeq is curated and is of much higher quality than the rest of the NCBI Sequence Database. However, unfortunately, because of the high level of manual curation required, RefSeq does not cover all species, and is not comprehensive for the species that are covered so far. To speed up searches and simplify the results in to can be very useful to just search RefSeq. However, for detailed and thorough work the full database should probably be searched and the results scrutinized. You can easily tell that a sequence comes from RefSeq because its accession number starts with particular sequence of letters. That is, accessions of RefSeq sequences corresponding to protein records usually start with NP_, and accessions of RefSeq curated complete genome sequences usually start with NC_ or NS_. 13.9 Querying the NCBI Database You may need to interrogate the NCBI Database to find particular sequences or a set of sequences matching given criteria, such as: The sequence with accession NC_001477 The sequences published in Nature 460:352-358 All sequences from Chlamydia trachomatis Sequences submitted by Caroline Cameron, a syphilis researcher Flagellin or fibrinogen sequences The glutamine synthetase gene from Mycobacteriuma leprae Just the upstream control region of the Mycobacterium leprae dnaA gene The sequence of the Mycobacterium leprae DnaA protein The genome sequence of syphilis, Treponema pallidum subspp. pallidum All human nucleotide sequences associated with malaria There are two main ways that you can query the NCBI database to find these sets of sequences. The first possibility is to carry out searches on the NCBI website. The second possibility is to carry out searches from R using one of several packages that can interface with NCBI. As of October 2019 rentrez seems to be the best package for this.. Below, I will explain how to manually carry out queries on the NCBI database. 13.10 Querying the NCBI Database via the NCBI Website (for reference) NOTE: The following section is here for reference; you need to know its possible to refine searches but do not need to know any of these actual tags. If you are carrying out searches on the NCBI website, to narrow down your searches to specific types of sequences or to specific organisms, you will need to use “search tags”. For example, the search tags “[PROP]” and “[ORGN]” let you restrict your search to a specific subset of the NCBI Sequence Database, or to sequences from a particular taxon, respectively. Here is a list of useful search tags, which we will explain how to use below: [AC], e.g. NC_001477[AC] With a particular accession number [ORGN], e.g. Fungi[ORGN] From a particular organism or taxon [PROP], e.g. biomol_mRNA[PROP] Of a specific type (eg. mRNA) or from a specific database (eg. RefSeq) [JOUR], e.g. Nature[JOUR] Described in a paper published in a particular journal [VOL], e.g. 531[VOL] Described in a paper published in a particular journal volume [PAGE], e.g. 27[PAGE] Described in a paper with a particular start-page in a journal [AU], e.g. “Smith J”[AU] Described in a paper, or submitted to NCBI, by a particular author To carry out searches of the NCBI database, you first need to go to the NCBI website, and type your search query into the search box at the top. For example, to search for all sequences from Fungi, you would type “Fungi[ORGN]” into the search box on the NCBI website. You can combine the search tags above by using “AND”, to make more complex searches. For example, to find all mRNA sequences from Fungi, you could type “Fungi[ORGN] AND biomol_mRNA[PROP]” in the search box on the NCBI website. Likewise, you can also combine search tags by using “OR”, for example, to search for all mRNA sequences from Fungi or Bacteria, you would type “(Fungi[ORGN] OR Bacteria[ORGN]) AND biomol_mRNA[PROP]” in the search box. Note that you need to put brackets around “Fungi[ORGN] OR Bacteria[ORGN]” to specify that the word “OR” refers to these two search tags. Here are some examples of searches, some of them made by combining search terms using “AND”: NC_001477[AC] - With accession number NC_001477 Nature[JOUR] AND 460[VOL] AND 352[PAGE] - Published in Nature 460:352-358 “Chlamydia trachomatis”[ORGN] - From the bacterium Chlamydia trachomatis “Berriman M”[AU] - Published in a paper, or submitted to NCBI, by M. Berriman flagellin OR fibrinogen - Which contain the word “flagellin” or “fibrinogen” in their NCBI record “Mycobacterium leprae”[ORGN] AND dnaA - Which are from M. leprae, and contain “dnaA” in their NCBI record “Homo sapiens”[ORGN] AND “colon cancer” - Which are from human, and contain “colon cancer” in their NCBI record “Homo sapiens”[ORGN] AND malaria - Which are from human, and contain “malaria” in their NCBI record “Homo sapiens”[ORGN] AND biomol_mrna[PROP] - Which are mRNA sequences from human “Bacteria”[ORGN] AND srcdb_refseq[PROP] - Which are RefSeq sequences from Bacteria “colon cancer” AND srcdb_refseq[PROP] - From RefSeq, which contain “colon cancer” in their NCBI record Note that if you are searching for a phrase such as “colon cancer” or “Chlamydia trachomatis”, you need to put the phrase in quotes when typing it into the search box. This is because if you type the phrase in the search box without quotes, the search will be for NCBI records that contain either of the two words “colon” or “cancer” (or either of the two words “Chlamydia” or “trachomatis”), not necessarily both words. As mentioned above, the NCBI database contains several sub-databases, including the NCBI Nucleotide database and the NCBI Protein database. If you go to the NCBI website, and type one of the search queries above in the search box at the top of the page, the results page will tell you how many matching NCBI records were found in each of the NCBI sub-databases. For example, if you search for “Chlamydia trachomatis[ORGN]”, you will get matches to proteins from C. trachomatis in the NCBI Protein database, matches to DNA and RNA sequences from C. trachomatis in the NCBI Nucleotide database, matches to whole genome sequences for C. trachomatis strains in the NCBI Genome database, and so on: Alternatively, if you know in advance that you want to search a particular sub-database, for example, the NCBI Protein database, when you go to the NCBI website, you can select that sub-database from the drop-down list above the search box, so that you will search that sub-database. 13.11 Example: finding the sequences published in Nature 460:352-358 (for reference) NOTE: The following section is here for reference; you need to know its possible to refine searches but do not need to know any of these actual tags. For example, if you want to find sequences published in Nature 460:352-358, you can use the “[JOUR]”, “[VOL]” and “[PAGE]” search terms. That is, you would go to the NCBI website and type in the search box on the top: “Nature”[JOUR] AND 460[VOL] AND 352[PAGE], where [JOUR] specifies the journal name, [VOL] the volume of the journal the paper is in, and [PAGE] the page number. This should bring up a results page with “50890” beside the word “Nucleotide”, and “1” beside the word “Genome”, and “25701” beside the word “Protein”, indicating that there were 50890 hits to sequence records in the Nucleotide database, which contains DNA and RNA sequences, and 1 hit to the Genome database, which contains genome sequences, and 25701 hits to the Protein database, which contains protein sequences. If you click on the word “Nucleotide”, it will bring up a webpage with a list of links to the NCBI sequence records for those 50890 hits. The 50890 hits are all contigs from the schistosome worm Schistosoma mansoni. Likewise, if you click on the word “Protein”, it will bring up a webpage with a list of links to the NCBI sequence records for the 25701 hits, and you will see that the hits are all predicted proteins for Schistosoma mansoni. If you click on the word “Genome”, it will bring you to the NCBI record for the Schistosoma mansoni genome sequence, which has NCBI accession NS_00200. Note that the accession starts with “NS_”, which indicates that it is a RefSeq accession. Therefore, in Nature volume 460, page 352, the Schistosoma mansoni genome sequence was published, along with all the DNA sequence contigs that were sequenced for the genome project, and all the predicted proteins for the gene predictions made in the genome sequence. You can view the original paper on the Nature website at http://www.nature.com/nature/journal/v460/n7253/abs/nature08160.html. Note: Schistmosoma mansoni is a parasitic worm that is responsible for causing schistosomiasis, which is classified by the WHO as a neglected tropical disease. "],["downloading-ncbi-sequence-data-by-hand.html", "Chapter 14 Downloading NCBI sequence data by hand 14.1 Preface 14.2 Retrieving genome sequence data via the NCBI website", " Chapter 14 Downloading NCBI sequence data by hand By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). 14.1 Preface The following chapter was originally written by Avril Coghlan. It provides brief, basic information for how to access sequences via the internet. Subsequent chapters provide more details and R code. 14.2 Retrieving genome sequence data via the NCBI website You can easily retrieve DNA or protein sequence data by hand from the NCBI Sequence Database via its website www.ncbi.nlm.nih.gov. Dengue DEN-1 DNA is a viral DNA sequence and its NCBI accession number is NC_001477. To retrieve the DNA sequence for the Dengue DEN-1 virus from NCBI, go to the NCBI website, type “NC_001477” in the Search box at the top of the webpage, and press the “Search” button beside the Search box. (While this is the normal workflow, accessions related to well-known organisms can sometimes turn up using a direct Google search. This is the case for NC_001477 - if you Google it you can go directly to the website with the genome sequence.) On the results page of a normal NCBI search you will see the number of hits to “NC_001477” in each of the NCBI databases on the NCBI website. There are many databases on the NCBI website, for example, PubMed and Pubmed Central contain abstracts from scientific papers, the Genes and Genomes database contains DNA and RNA sequence data, the Proteins database contains protein sequence data, and so on. Most biologist would do this type of work by hand from within their web browser, but it can also be done by writing small programs in scripting languages such as Python or R. In R, the rentrez package is a powerful tool for intersecting with NCBI resource. In this tutorial we’ll focus on the web interface. Its good to remember, though, that almost anything done via the webpage can be automated using a computer script. A challenge when learning to use NCBI resources is that there is a tremendous amount of sequence information available and you need to learn how to sort through what the search results provide. As you are looking for the DNA sequence of the Dengue DEN-1 virus genome, you expect to see a hit in the NCBI Nucleotide database. This is indicated at the top of the page where it says “NUCLEOTIDE SEQUENCE” and lists “Dengue virus 1, complete genome.” When you click on the link for the Nucleotide database, it will bring you to the record for NC_001477 in the NCBI Nucleotide database. This will contain the name and NCBI accession of the sequence, as well as other details such as any papers describing the sequence. If you scroll down you’ll see the sequence also. If you need it, you can retrieve the DNA sequence for the DEN-1 Dengue virus genome sequence as a FASTA format sequence file in a couple ways. The easiest is just to copy and paste it into a text, .R, or other file. You can also click on “Send to” at the top right of the NC_001477 sequence record webpage (just to the left of the side bar; its kinda small). After you click on Send to you can pick several options. and then choose “File” in the menu that appears, and then choose FASTA from the “Format” menu that appears, and click on “Create file”. The sequence will then download. The default file name is sequence.fasta so you’ll probably want to change it. You can now open the FASTA file containing the DEN-1 Dengue virus genome sequence using a text editor like Notepad, WordPad, Notepad++, or even RStudio on your computer. To find a text editor on your computer search for “text” from the start menu (Windows) and usually one will come up. (Opening the file in a word processor like word isn’t recommended). "],["downloading-sequences-from-uniprot-by-hand.html", "Chapter 15 Downloading sequences from UniProt by hand 15.1 Vocab 15.2 Downloading Protein data from UniProt 15.3 Viewing the UniProt webpage for a protein sequence 15.4 Retrieving a UniProt protein sequence via the UniProt website", " Chapter 15 Downloading sequences from UniProt by hand By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). 15.1 Vocab RefSeq manual curation UniProt accession 15.2 Downloading Protein data from UniProt In a previous vignette you learned how to retrieve sequences from the NCBI database. The NCBI database is a key database in bioinformatics because it contains essentially all DNA sequences ever sequenced. As mentioned previously, a subsection of the NCBI database called RefSeq consists of high quality DNA and protein sequence data. Furthermore, the NCBI entries for the RefSeq sequences have been manually curated, which means that expert biologists employed by NCBI have added additional information to the NCBI entries for those sequences, such as details of scientific papers that describe the sequences. Another extremely important manually curated database is UniProt, which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. For each protein in UniProt, the UniProt curators read all the scientific papers that they can find about that protein, and add information from those papers to the protein’s UniProt entry. For example, for a human protein, the UniProt entry for the protein usually includes information about the biological function of the protein, in what human tissues it is expressed, whether it interacts with other human proteins, and much more. All this information has been manually gathered by the UniProt curators from scientific papers, and the papers in which the found the information are always listed in the UniProt entry for the protein. Just like NCBI, UniProt also assigns an accession to each sequence in the UniProt database. Although the same protein sequence may appear in both the NCBI database and the UniProt database, it will have different NCBI and UniProt accessions. However, there is usually a link on the NCBI entry for the protein sequence to the UniProt entry, and vice versa. 15.3 Viewing the UniProt webpage for a protein sequence If you are given the UniProt accession for a protein, to find the UniProt entry for the protein, you first need to go the UniProt website, www.uniprot.org. At the top of the UniProt website, you will see a search box, and you can type the accession of the protein that you are looking for in this search box, and then click on the “Search” button to search for it. For example, if you want to find the sequence for the chorismate lyase protein from Mycobacterium leprae (the bacterium which causes leprosy), which has UniProt accession Q9CD83, you would type just “Q9CD83” in the search box and press “Search”. The UniProt entry for UniProt accession Q9CD83 will then appear in your web browser. Beside the heading “Organism” you can see the organism is given as Mycobacterium leprae. If you scroll down you’ll find a section Names and Taxonomy and beside the heading “Taxonomic lineage”, you can see “Bacteria - Actinobacteria - Actinobacteridae - Actinomycetales - Corynebacterineae - Mycobacteriaceae- Mycobacterium”. This tells us that Mycobacterium is a species of bacteria, which belongs to a group of related bacteria called the Mycobacteriaceae, which itself belongs to a larger group of related bacteria called the Corynebacterineae, which itself belongs to an even larger group of related bacteria called the Actinomycetales, which itself belongs to the Actinobacteridae, which itself belongs to a huge group of bacteria called the Actinobacteria. 15.3.1 Protein function Back up at the top under “organism” is says “Status”, which tells us the annotation score is 2 out of 5, that it is a “Protein inferred from homology”, which means what we know about it is derived from bioinformatics and computational tools, not lab work. Beside the heading “Function”, it says that the function of this protein is that it “Removes the pyruvyl group from chorismate to provide 4-hydroxybenzoate (4HB)”. This tells us this protein is an enzyme (a protein that increases the rate of a specific biochemical reaction), and tells us what is the particular biochemical reaction that this enzyme is involved in. At the end of this info it says “By similarity”, which again indicates that what we know about this protein comes from bioinformatics, not lab work. 15.3.2 Protein sequence and size Under Sequence we see that the sequence length is 210 amino acids long (210 letters long) and has a mass of 24,045 daltons. We can access the sequence as a FASTA file from here if we want and also carry out a BLAST search from a link on the right. 15.3.3 Other information Further down the UniProt page for this protein, you will see a lot more information, as well as many links to webpages in other biological databases, such as NCBI. The huge amount of information about proteins in UniProt means that if you want to find out about a particular protein, the UniProt page for that protein is a great place to start. 15.4 Retrieving a UniProt protein sequence via the UniProt website There are a couple different ways to retrieve the sequence. At the top of the page is a tab that say “Format” which brings you to a page with th FASTA file. You can copy and paste the sequence from here if you want. To save it as a file, go to the “File” menu of your web browser, choose “Save page as”, and save the file. Remember to give the file a sensible name (eg. “Q9CD83.fasta” for accession Q9CD83), and in a place that you will remember (eg. in the “My Documents” folder). For example, you can retrieve the protein sequences for the chorismate lyase protein from Mycobacterium leprae (which has UniProt accession Q9CD83) and for the chorismate lyase protein from Mycobacterium ulcerans (UniProt accession A0PQ23), and save them as FASTA-format files (eg. “Q9CD83.fasta” and “A0PQ23.fasta”, as described above. You can also put the UniProt information into an online Basket. If you do this for both Q9CD83 and A0PQ23 you can think click on Basket, select both entries, and carry out a pairwise alignment by clicking on Align. Mycobacterium leprae is the bacterium which causes leprosy, while Mycobacterium ulcerans is a related bacterium which causes Buruli ulcer, both of which are classified by the WHO as neglected tropical diseases. The M. leprae and M. ulcerans chorismate lyase proteins are an example of a pair of homologous (related) proteins in two related species of bacteria. If you downloaded the protein sequences for UniProt accessions Q9CD83 and A0PQ23 and saved them as FASTA-format files (eg. “Q9CD83.fasta” and “A0PQ23.fasta”), you could read them into R using the read.fasta() function in the SeqinR R package (as detailed in another Vignette) or a similar function from another package. Note that the read.fasta() function normally expects that you have put your FASTA-format files in the the working directory of R. For convenience so you can explore these sequences they have been saved in a special folder in the compbio4all package and can be accessed like this for the Leprosy sequence # load compbio4all library(compbio4all) # locate the file within the package using system.file() file.1 <- system.file("./extdata/Q9CD83.fasta",package = "compbio4all") # load seqinr library("seqinr") # load fasta leprae <- read.fasta(file = file.1) lepraeseq <- leprae[[1]] We can confirm str(lepraeseq) For the other sequence # locate the file within the package using system.file() file.2 <- system.file("./extdata/A0PQ23.fasta", package = "compbio4all") # load fasta ulcerans <- read.fasta(file = file.2) ulceransseq <- ulcerans[[1]] "],["introducingFASTA.html", "Chapter 16 Introducing FASTA Files 16.1 Example FASTA file 16.2 Multiple sequences in a single FASTA file 16.3 Multiple sequence alignments can be stored in FASTA format 16.4 FASTQ Format", " Chapter 16 Introducing FASTA Files Adapted from Wikipedia: https://en.wikipedia.org/wiki/FASTA_format In bioinformatics, the FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences, in which nucleotides or amino acids are represented using single-letter codes. The format allows for sequence names and comments to precede the sequences. The format originates from the FASTA alignment software, but has now become a near universal standard in the field of bioinformatics. The simplicity of FASTA format makes it easy to manipulate and parse sequences using text-processing tools and scripting languages like the R programming language and Python. The first line in a FASTA file starts with a “>” (greater-than) symbol and holds summary information about the sequence, often starting with a unique accession number and followed by information like the name of the gene, the type of sequence, and the organism it is from. On the next is the sequence itself in a standard one-letter character string. Anything other than a valid character is be ignored (including spaces, tabs, asterisks, etc…). A multiple sequence FASTA format can be obtained by concatenating several single sequence FASTA files in a common file (also known as multi-FASTA format). Following the header line, the actual sequence is represented. Sequences may be protein sequences or nucleic acid sequences, and they can contain gaps or alignment characters. Sequences are expected to be represented in the standard amino acid and nucleic acid codes. Lower-case letters are accepted and are mapped into upper-case; a single hyphen or dash can be used to represent a gap character; and in amino acid sequences, U and * are acceptable letters. FASTQ format is a form of FASTA format extended to indicate information related to sequencing. It is created by the Sanger Centre in Cambridge. Bioconductor.org’s Biostrings package can be used to read and manipulate FASTA files in R “FASTA format is a text-based format for representing either nucleotide sequences or peptide sequences, in which base pairs or amino acids are represented using single-letter codes. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater-than (”>“) symbol in the first column. It is recommended that all lines of text be shorter than 80 characters in length.” (https://zhanglab.dcmb.med.umich.edu/FASTA/) 16.1 Example FASTA file Here is an example of the contents of a FASTA file. (If your are viewing this chapter in the form of the source .Rmd file, the cat() function is included just to print out the content properly and is not part of the FASTA format). cat(">gi|186681228|ref|YP_001864424.1| phycoerythrobilin:ferredoxin oxidoreductase MNSERSDVTLYQPFLDYAIAYMRSRLDLEPYPIPTGFESNSAVVGKGKNQEEVVTTSYAFQTAKLRQIRA AHVQGGNSLQVLNFVIFPHLNYDLPFFGADLVTLPGGHLIALDMQPLFRDDSAYQAKYTEPILPIFHAHQ QHLSWGGDFPEEAQPFFSPAFLWTRPQETAVVETQVFAAFKDYLKAYLDFVEQAEAVTDSQNLVAIKQAQ LRYLRYRAEKDPARGMFKRFYGAEWTEEYIHGFLFDLERKLTVVK") ## >gi|186681228|ref|YP_001864424.1| phycoerythrobilin:ferredoxin oxidoreductase ## MNSERSDVTLYQPFLDYAIAYMRSRLDLEPYPIPTGFESNSAVVGKGKNQEEVVTTSYAFQTAKLRQIRA ## AHVQGGNSLQVLNFVIFPHLNYDLPFFGADLVTLPGGHLIALDMQPLFRDDSAYQAKYTEPILPIFHAHQ ## QHLSWGGDFPEEAQPFFSPAFLWTRPQETAVVETQVFAAFKDYLKAYLDFVEQAEAVTDSQNLVAIKQAQ ## LRYLRYRAEKDPARGMFKRFYGAEWTEEYIHGFLFDLERKLTVVK 16.2 Multiple sequences in a single FASTA file Multiple sequences can be stored in a single FASTA file, each on separated by a line and have its own headline. cat(">LCBO - Prolactin precursor - Bovine MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGPGNCQVSLRDLFDRAVMVSHYIHDLSS EMFNEFDKRYAQGKGFITMALNSCHTSSLPTPEDKEQAQQTHHEVLMSLILGLLRSWNDPLYHL VTEVRGMKGAPDAILSRAIEIEEENKRLLEGMEMIFGQVIPGAKETEPYPVWSGLPSLQTKDED ARYSAFYNLLHCLRRDSSKIDTYLKLLNCRIIYNNNC* >MCHU - Calmodulin - Human, rabbit, bovine, rat, and chicken MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTID FPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREA DIDGDGQVNYEEFVQMMTAK* >gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus] LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG LLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVIL GLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGX IENY") ## >LCBO - Prolactin precursor - Bovine ## MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGPGNCQVSLRDLFDRAVMVSHYIHDLSS ## EMFNEFDKRYAQGKGFITMALNSCHTSSLPTPEDKEQAQQTHHEVLMSLILGLLRSWNDPLYHL ## VTEVRGMKGAPDAILSRAIEIEEENKRLLEGMEMIFGQVIPGAKETEPYPVWSGLPSLQTKDED ## ARYSAFYNLLHCLRRDSSKIDTYLKLLNCRIIYNNNC* ## ## >MCHU - Calmodulin - Human, rabbit, bovine, rat, and chicken ## MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTID ## FPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREA ## DIDGDGQVNYEEFVQMMTAK* ## ## >gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus] ## LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV ## EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG ## LLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVIL ## GLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGX ## IENY 16.3 Multiple sequence alignments can be stored in FASTA format Aligned FASTA format can be used to store the output of Multiple Sequence Alignment (MSA). This format contains Multiple entries, each with their own header line Gaps inserted to align sequences are indicated by . Each spaces added to the beginning and end of sequences that vary in length are indicated by ~ In the sample FASTA file below, the example1 sequence has a gap of 8 near its beginning. The example2 sequence has numerous ~ indicating that this sequence is missing data from its beginning that are present in the other sequences. The example3 sequence has numerous ~ at its end, indicating that this sequence is shorter than the others. cat(">example1 MKALWALLLVPLLTGCLA........EGELEVTDQLPGQSDQP.WEQALNRFWDYLRWVQ GNQARDRLEEVREQMEEVRSKMEEQTQQIRLQAEIFQARIKGWFEPLVEDMQRQWANLME KIQASVATNSIASTTVPLENQ >example2 ~~~~~~~~~~~~~~~~~~~~~~~~~~KVQQELEPEAGWQTGQP.WEAALARFWDYLRWVQ SSRARGHLEEMREQIQEVRVKMEEQADQIRQKAEAFQARLKSWFEPLLEDMQRQWDGLVE KVQAAVAT.IPTSKPVEEP~~ >example3 MRSLVVFFALAVLTGCQARSLFQAD..............APQPRWEEMVDRFWQYVSELN AGALKEKLEETAENL...RTSLEGRVDELTSLLAPYSQKIREQLQEVMDKIKEATAALPT QA~~~~~~~~~~~~~~~~~~~") ## >example1 ## MKALWALLLVPLLTGCLA........EGELEVTDQLPGQSDQP.WEQALNRFWDYLRWVQ ## GNQARDRLEEVREQMEEVRSKMEEQTQQIRLQAEIFQARIKGWFEPLVEDMQRQWANLME ## KIQASVATNSIASTTVPLENQ ## >example2 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~KVQQELEPEAGWQTGQP.WEAALARFWDYLRWVQ ## SSRARGHLEEMREQIQEVRVKMEEQADQIRQKAEAFQARLKSWFEPLLEDMQRQWDGLVE ## KVQAAVAT.IPTSKPVEEP~~ ## >example3 ## MRSLVVFFALAVLTGCQARSLFQAD..............APQPRWEEMVDRFWQYVSELN ## AGALKEKLEETAENL...RTSLEGRVDELTSLLAPYSQKIREQLQEVMDKIKEATAALPT ## QA~~~~~~~~~~~~~~~~~~~ 16.4 FASTQ Format Adapted from Wikipedia: https://en.wikipedia.org/wiki/FASTQ_format FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA formatted sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer. A FASTQ file normally uses four lines per sequence. Line 1 begins with a @ character and is followed by a sequence identifier and an optional description (like a FASTA title line). Line 2 is the raw sequence letters. Line 3 begins with a + character and is optionally followed by the same sequence identifier (and any description) again. Line 4 encodes the quality values for the sequence in Line 2 of the file, and must contain the same number of symbols as letters in the sequence. A FASTQ file containing a single sequence might look like this:” cat("@SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65") ## @SEQ_ID ## GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT ## + ## !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 Here are the quality value characters in left-to-right increasing order of quality (ASCII):” !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~ FASTQ files typically do not include line breaks and do not wrap around when they reach the width of a normal page or file. "],["a-complete-bioinformatics-workflow-in-r.html", "Chapter 17 A complete bioinformatics workflow in R", " Chapter 17 A complete bioinformatics workflow in R By: Nathan L. Brouwer "],["worked-example-building-a-phylogeny-in-r.html", "Chapter 18 “Worked example: Building a phylogeny in R” 18.1 Introduction 18.2 Software Preliminaires 18.3 Downloading macro-molecular sequences 18.4 Prepping macromolecular sequences 18.5 Aligning sequences 18.6 The shroom family of genes 18.7 Downloading multiple sequences 18.8 Multiple sequence alignment 18.9 Genetic distance. 18.10 Phylognetic trees (finally!)", " Chapter 18 “Worked example: Building a phylogeny in R” 18.1 Introduction Phylogenies play an important role in computational biology and bioinformatics. Phylogenetics itself is an obligatly computational field that only began rapid growth when computational power allowed the many algorithms it relies on to be done rapidly. Phylogeneies of species, genes and proteins are used to address many biological issues, including Patterns of protein evolution Origin and evolution of phenotypic traits Origin and progression of epidemics Origin of evolution of diseases (eg, zooenoses) Prediction of protein function from its sequence … and many more The actual building of a phylogeny is a computationally intensive task; moreover, there are many bioinformatics and computational tasks the precede the construction of a phylogeny: genome sequencing and assembly computational gene prediction and annotation database searching and results screening pairwise sequence alignment data organization and cleaning multiple sequence alignment evaluation and validation of alignment accuracy Once all of these steps have been carried out, the building of a phylogeny involves picking a model of sequence evolution or other description of evolution picking a statistical approach to tree construction evaluating uncertainty in the final tree In this chapter we will work through many of these steps. In most cases we will pick the easiest or fastest option; in later chapters we will unpack the various options. This chapter is written as an interactive R sessions. You can follow along by opening the .Rmd file of the chapter or typing the appropriate commands into your own script. I assume that all the necessary packages have been installed and they only need to be loaded into R using the library() command. This lesson walks you through and entire workflow for a bioinformatics, including obtaining FASTA sequences cleaning sequences creating alignments creating distance a distance matrix building a phylogenetic tree We’ll examine the Shroom family of genes, which produces Shroom proteins essential for tissue formation in many multicellular eukaryotes, including neural tube formation in vertebrates. We’ll examine shroom in several very different organism, including humans, mice and sea urchins. There is more than one type of shroom in vertebrates, and we’ll also look at two different Shroom genes: shroom 1 and shroom 2. This lesson draws on skills from previous sections of the book, but is written to act as a independent summary of these activities. There is therefore review of key aspects of R and bioinformatics throughout it. 18.2 Software Preliminaires 18.2.1 Vocab argument function list named list vector named vector for() loop R console 18.2.2 R functions library() round() plot() mtext() nchar() rentrez::entrez_fetch() compbio4all::entrez_fetch_list() compbio4all::print_msa() (Coghlan 2011) Biostrings::AAStringSet() msa::msa() msa::msaConvert() msa::msaPrettyPrint() seqinr::dist.alignment() ape::nj() A few things need to be done to get started with our R session. 18.2.3 Download necessary packages Many R sessions begin by downloading necessary software packages to augment R’s functionality. If you don’t have them already, you’ll need the following packages from CRAN: ape seqinr rentrez devtools The CRAN packages can be loaded with install.packages(). You’ll also need these packages from Bioconductor: msa Biostrings For installing packages from Bioconductor, see the chapter at the beginning of this book on this process. Finally, you’ll need this package from GitHub compbio4all ggmsa To install packages from GitHub you can use the code devtools::install_github(\"brouwern/combio4all\") and devtools::install_github(\"brouwern/ggmsa\") 18.2.4 Load packages into memory We now need to load up all our bioinformatics and phylogenetics software into R. This is done with the library() command. To run this code just clock on the sideways green triangle all the way to the right of the code. NOTE: You’ll likely see some red code appear on your screen. No worries, totally normal! # github packages library(compbio4all) # CRAN packages library(rentrez) library(seqinr) library(ape) # Bioconductor packages library(msa) library(Biostrings) 18.3 Downloading macro-molecular sequences We’re going to explore some sequences. First we need to download them. To do this we’ll use a function, entrez_fretch(), which accesses the Entrez system of database (ncbi.nlm.nih.gov/search/). This function is from the rentrez package, which stands for “R-Entrez.” We need to tell entrez_fetch() several things db = ... the type of entrez database. id = ... the accession (ID) number of the sequence rettype = ... file type what we want the function to return. Formally, these things are called arguments by R. We’ll use these settings: db = \"protein\" to access the Entrez database of protein sequences rettype = \"fasta\", which is a standard file format for nucleic acid and protein sequences We’ll set id = ... to sequences whose accession numbers are: NP_065910: Human shroom 3 AAF13269: Mouse shroom 3a CAA58534: Human shroom 2 XP_783573: Sea urchin shroom There are two highly conserved regions of shroom 3 1. ASD 1: aa 884 to aa 1062 in hShroom3 1. ASD 2: aa 1671 to aa 1955 in hShroom3 Normally we’d have to download these sequences by hand through pointing and clicking on GeneBank records on the NCBI website. In R we can do it automatically; this might take a second. All the code needed is this: # Human shroom 3 (H. sapiens) hShroom3 <- entrez_fetch(db = "protein", id = "NP_065910", rettype = "fasta") The output is in FASTA format; we’ll use the cat() to do a little formatting for us: cat(hShroom3) ## >NP_065910.3 protein Shroom3 [Homo sapiens] ## MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKL ## QAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAA ## WSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYL ## RRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGR ## ERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNI ## PRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSL ## GSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGM ## LYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFC ## SVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDF ## QEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTK ## AEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQ ## GFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEP ## LGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGS ## PESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPH ## TPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFP ## ESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAY ## LQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLS ## GANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSG ## FGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTP ## CPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGP ## RGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTS ## DPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPV ## YSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEA ## ESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDK ## SLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYY ## SVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEA ## LISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHED ## ARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDF ## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL Note the initial >, then the header line of NP_065910.3 protein Shroom3 [Homo sapiens]. After that is the amino acid sequence. The underlying data also includes the newline character \\n to designate where each line of amino acids stops. We can get the rest of the data by just changing the id = ... argument: # Mouse shroom 3a (M. musculus) mShroom3a <- entrez_fetch(db = "protein", id = "AAF13269", rettype = "fasta") # Human shroom 2 (H. sapiens) hShroom2 <- entrez_fetch(db = "protein", id = "CAA58534", rettype = "fasta") # Sea-urchin shroom sShroom <- entrez_fetch(db = "protein", id = "XP_783573", rettype = "fasta") I’m going to check about how long each of these sequences is - each should have an at least slightly different length. If any are identical, I might have repeated an accession name or re-used an object name. The function nchar() counts of the number of characters in an R object. nchar(hShroom3) ## [1] 2070 nchar(mShroom3a) ## [1] 2083 nchar(sShroom) ## [1] 1758 nchar(hShroom2) ## [1] 1673 18.4 Prepping macromolecular sequences “90% of data analysis is data cleaning” (-Just about every data analyst and data scientist on twitter) We have our sequences, but the current format isn’t directly usable for us yet because there are several things that aren’t sequence information metadata (the header) page formatting information (the newline character) We can remove this non-sequence information using a function I wrote called fasta_cleaner(), which is in the compbio4all package. The function uses regular expressions to remove the info we don’t need. ASIDE: If we run the name of the command with out any quotation marks we can see the code: fasta_cleaner ## function(fasta_object, parse = TRUE){ ## ## fasta_object <- sub("^(>)(.*?)(\\\\n)(.*)(\\\\n\\\\n)","\\\\4",fasta_object) ## fasta_object <- gsub("\\n", "", fasta_object) ## ## if(parse == TRUE){ ## fasta_object <- stringr::str_split(fasta_object, ## pattern = "", ## simplify = FALSE) ## } ## ## return(fasta_object[[1]]) ## } ## <bytecode: 0x7f81ca9a24b8> ## <environment: namespace:compbio4all> End ASIDE Now use the function to clean our sequences; we won’t worry about what pare = ... is for. hShroom3 <- fasta_cleaner(hShroom3, parse = F) mShroom3a <- fasta_cleaner(mShroom3a, parse = F) hShroom2 <- fasta_cleaner(hShroom2, parse = F) sShroom <- fasta_cleaner(sShroom, parse = F) Now let’s take a peek at what our sequences look like: hShroom3 ## [1] "MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKLQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAAWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYLRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGRERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNIPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSLGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGMLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFCSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDFQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTKAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEPLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGSPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPHTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFPESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAYLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLSGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSGFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTPCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGPRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTSDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPVYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEAESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDKSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYYSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEALISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHEDARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDFIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL" 18.5 Aligning sequences We can do a global alignment of one sequence against another using the pairwiseAlignment() function from the Bioconductor package Biostrings (note that capital “B” in Biostrings; most R package names are all lower case, but not this one). Let’s align human versus mouse shroom: align.h3.vs.m3a <- Biostrings::pairwiseAlignment( hShroom3, mShroom3a) We can peek at the alignment align.h3.vs.m3a ## Global PairwiseAlignmentsSingleSubject (1 of 1) ## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL ## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL ## score: 2189.934 The score tells us how closely they are aligned; higher scores mean the sequences are more similar. Its hard to interpret the number on its own so we can get the percent sequence identity (PID) using the pid() function. Biostrings::pid(align.h3.vs.m3a) ## [1] 70.56511 So, shroom3 from humans and shroom3 from mice are ~71% similar (at least using this particular method of alignment, and there are many ways to do this!) What about human shroom 3 and sea-urchin shroom? align.h3.vs.h2 <- Biostrings::pairwiseAlignment( hShroom3, hShroom2) First check out the score using score(), which accesses it directly without all the other information. score(align.h3.vs.h2) ## [1] -5673.853 Now the percent sequence alignment with pid(): Biostrings::pid(align.h3.vs.h2) ## [1] 33.83277 So Human shroom 3 and Mouse shroom 3 are 71% identical, but Human shroom 3 and human shroom 2 are only 34% similar? How does it work out evolutionary that a human and mouse gene are more similar than a human and a human gene? What are the evolutionary relationships among these genes within the shroom gene family? 18.6 The shroom family of genes I’ve copied a table from a published paper which has accession numbers for 15 different Shroom genes. shroom_table <- c("CAA78718" , "X. laevis Apx" , "xShroom1", "NP_597713" , "H. sapiens APXL2" , "hShroom1", "CAA58534" , "H. sapiens APXL", "hShroom2", "ABD19518" , "M. musculus Apxl" , "mShroom2", "AAF13269" , "M. musculus ShroomL" , "mShroom3a", "AAF13270" , "M. musculus ShroomS" , "mShroom3b", "NP_065910", "H. sapiens Shroom" , "hShroom3", "ABD59319" , "X. laevis Shroom-like", "xShroom3", "NP_065768", "H. sapiens KIAA1202" , "hShroom4a", "AAK95579" , "H. sapiens SHAP-A" , "hShroom4b", #"DQ435686" , "M. musculus KIAA1202" , "mShroom4", "ABA81834" , "D. melanogaster Shroom", "dmShroom", "EAA12598" , "A. gambiae Shroom", "agShroom", "XP_392427" , "A. mellifera Shroom" , "amShroom", "XP_783573" , "S. purpuratus Shroom" , "spShroom") #sea urchin I’ll do a bit of formatting; you can ignore these details if you want # convert to matrix shroom_table_matrix <- matrix(shroom_table, byrow = T, nrow = 14) # convert to dataframe shroom_table <- data.frame(shroom_table_matrix, stringsAsFactors = F) # name columns names(shroom_table) <- c("accession", "name.orig","name.new") # Create simplified species names shroom_table$spp <- "Homo" shroom_table$spp[grep("laevis",shroom_table$name.orig)] <- "Xenopus" shroom_table$spp[grep("musculus",shroom_table$name.orig)] <- "Mus" shroom_table$spp[grep("melanogaster",shroom_table$name.orig)] <- "Drosophila" shroom_table$spp[grep("gambiae",shroom_table$name.orig)] <- "mosquito" shroom_table$spp[grep("mellifera",shroom_table$name.orig)] <- "bee" shroom_table$spp[grep("purpuratus",shroom_table$name.orig)] <- "sea urchin" Take a look: shroom_table ## accession name.orig name.new spp ## 1 CAA78718 X. laevis Apx xShroom1 Xenopus ## 2 NP_597713 H. sapiens APXL2 hShroom1 Homo ## 3 CAA58534 H. sapiens APXL hShroom2 Homo ## 4 ABD19518 M. musculus Apxl mShroom2 Mus ## 5 AAF13269 M. musculus ShroomL mShroom3a Mus ## 6 AAF13270 M. musculus ShroomS mShroom3b Mus ## 7 NP_065910 H. sapiens Shroom hShroom3 Homo ## 8 ABD59319 X. laevis Shroom-like xShroom3 Xenopus ## 9 NP_065768 H. sapiens KIAA1202 hShroom4a Homo ## 10 AAK95579 H. sapiens SHAP-A hShroom4b Homo ## 11 ABA81834 D. melanogaster Shroom dmShroom Drosophila ## 12 EAA12598 A. gambiae Shroom agShroom mosquito ## 13 XP_392427 A. mellifera Shroom amShroom bee ## 14 XP_783573 S. purpuratus Shroom spShroom sea urchin 18.7 Downloading multiple sequences Instead of getting one sequence at a time we can download several by accessing the “accession” column from the table shroom_table$accession ## [1] "CAA78718" "NP_597713" "CAA58534" "ABD19518" "AAF13269" "AAF13270" ## [7] "NP_065910" "ABD59319" "NP_065768" "AAK95579" "ABA81834" "EAA12598" ## [13] "XP_392427" "XP_783573" We can give this whole set of accessions to entrez_fetch(): shrooms <- entrez_fetch(db = "protein", id = shroom_table$accession, rettype = "fasta") We can look at what we got here with cat() (I won’t display this because it is very long!) cat(shrooms) The current format of these data is a single, long set of data. This is a standard way to store, share and transmit FASTA files, but in R we’ll need a slightly different format. We’ll download all of the sequences again, this time using a function from compbio4all called entrez_fetch_list() which is a wrapper function I wrote to put the output of entrez_fetch() into an R data format called a list. shrooms_list <- entrez_fetch_list(db = "protein", id = shroom_table$accession, rettype = "fasta") Now we have a list which as 14 elements, one for each sequence in our table. length(shrooms_list) ## [1] 14 We now need to clean up each one fo these sequences. We can do that using a simple for() loop: for(i in 1:length(shrooms_list)){ shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F) } Second to last step: we need to take each one of our sequences from our list and put it into a vector, in particular a named vector # make a vector to store output shrooms_vector <- rep(NA, length(shrooms_list)) # run the loop for(i in 1:length(shrooms_vector)){ shrooms_vector[i] <- shrooms_list[[i]] } # name the vector names(shrooms_vector) <- names(shrooms_list) Now the final step: we need to convert our named vector to a string set using Biostrings::AAStringSet(). Note the _ss tag at the end of the object we’re assigning the output to, which designates this as a string set. shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector) 18.8 Multiple sequence alignment We must align all of the sequences we downloaded and use that alignment to build a phylogenetic tree. This will tell us how the different genes, both within and between species, are likely to be related. 18.8.1 Building an Multiple Sequence Alignment (MSA) We’ll use the software msa, which implements the ClustalW multiple sequence alignment algorithm. Normally we’d have to download the ClustalW program and either point-and-click our way through it or use the command line*, but these folks wrote up the algorithm in R so we can do this with a line of R code. This will take a second or two. shrooms_align <- msa(shrooms_vector_ss, method = "ClustalW") ## use default substitution matrix 18.8.2 Viewing an MSA Once we build an MSA we need to visualize it. 18.8.2.1 Viewing an MSA in R We can look at the output from msa(), but its not very helpful shrooms_align ## CLUSTAL 2.1 ## ## Call: ## msa(shrooms_vector_ss, method = "ClustalW") ## ## MsaAAMultipleAlignment with 14 rows and 2252 columns ## aln names ## [1] -------------------------...------------------------- NP_065768 ## [2] -------------------------...------------------------- AAK95579 ## [3] -------------------------...SVFGGVFPTLTSPL----------- AAF13269 ## [4] -------------------------...SVFGGVFPTLTSPL----------- AAF13270 ## [5] -------------------------...CTFSGIFPTLTSPL----------- NP_065910 ## [6] -------------------------...NKS--LPPPLTSSL----------- ABD59319 ## [7] -------------------------...------------------------- CAA58534 ## [8] -------------------------...------------------------- ABD19518 ## [9] -------------------------...LT----------------------- NP_597713 ## [10] -------------------------...------------------------- CAA78718 ## [11] -------------------------...------------------------- EAA12598 ## [12] -------------------------...------------------------- ABA81834 ## [13] MTELQPSPPGYRVQDEAPGPPSCPP...------------------------- XP_392427 ## [14] -------------------------...AATSSSSNGIGGPEQLNSNATSSYC XP_783573 ## Con -------------------------...------------------------- Consensus A function called print_msa() (Coghlan 2011) which I’ve put intocombio4all can give us more informative output by printing out the actual alignment into the R console. To use print_msa() We need to make a few minor tweaks though first. These are behind the scenes changes so don’t worry about the details right now. We’ll change the name to shrooms_align_seqinr to indicate that one of our changes is putting this into a format defined by the bioinformatics package seqinr. class(shrooms_align) <- "AAMultipleAlignment" shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment") I won’t display the output from shrooms_align_seqinr because its very long; we have 14 shroom genes, and shroom happens to be a rather long gene. print_msa(alignment = shrooms_align_seqinr, chunksize = 60) 18.8.2.2 Displaying an MSA as an R plot I’m going to just show about 100 amino acids near the end of the alignment, where there is the most overlap across all of the sequences. This is set with the start = ... and end = ... arguments. Note that we’re using the shrooms_align object. ggmsa::ggmsa(shrooms_align, # shrooms_align, NOT shrooms_align_seqinr start = 2000, end = 2100) ## Registered S3 methods overwritten by 'ggalt': ## method from ## grid.draw.absoluteGrob ggplot2 ## grobHeight.absoluteGrob ggplot2 ## grobWidth.absoluteGrob ggplot2 ## grobX.absoluteGrob ggplot2 ## grobY.absoluteGrob ggplot2 18.8.2.3 Saving an MSA as PDF We can take a look at the alignment in PDF format if we want. I this case I’m going to just show about 100 amino acids near the end of the alignment, where there is the most overlap across all of the sequences. This is set with the y = c(...) argument. msaPrettyPrint(shrooms_align, # alignment file = "shroom_msa.pdf", # file name y=c(2000, 2100), # range askForOverwrite=FALSE) You can see where R is saving things by running getwd() getwd() ## [1] "/Users/nlb24/OneDrive - University of Pittsburgh/0-books/lbrb-bk/lbrb" On a Mac you can usually find the file by searching in Finder for the file name, which I set to be “shroom_msa.pdf” using the file = ... argument above. 18.9 Genetic distance. Next need to first get an estimate of how similar each sequences is. The more amino acids that are identical to each other, the more similar. Instead of similarity, we usually work in terms of difference or genetic distance (a.k.a. evolutionary distance). This is done with the dist.alignment() function. shrooms_dist <- seqinr::dist.alignment(shrooms_align_seqinr, matrix = "identity") We’ve made a matrix using dist.alignment(); let’s round it off so its easier to look at using the round() function. shrooms_dist_rounded <- round(shrooms_dist, digits = 3) Now let’s look at it shrooms_dist_rounded ## NP_065768 AAK95579 AAF13269 AAF13270 NP_065910 ABD59319 CAA58534 ## AAK95579 0.000 ## AAF13269 0.884 0.917 ## AAF13270 0.897 0.917 0.000 ## NP_065910 0.878 0.912 0.533 0.536 ## ABD59319 0.893 0.921 0.783 0.783 0.782 ## CAA58534 0.872 0.908 0.838 0.849 0.840 0.864 ## ABD19518 0.866 0.912 0.834 0.846 0.838 0.855 0.548 ## NP_597713 0.916 0.939 0.903 0.903 0.902 0.904 0.896 ## CAA78718 0.925 0.955 0.896 0.895 0.893 0.893 0.898 ## EAA12598 0.914 0.947 0.899 0.899 0.902 0.897 0.891 ## ABA81834 0.938 0.943 0.935 0.934 0.936 0.940 0.935 ## XP_392427 0.936 0.963 0.935 0.934 0.938 0.941 0.938 ## XP_783573 0.940 0.958 0.942 0.939 0.942 0.935 0.942 ## ABD19518 NP_597713 CAA78718 EAA12598 ABA81834 XP_392427 ## AAK95579 ## AAF13269 ## AAF13270 ## NP_065910 ## ABD59319 ## CAA58534 ## ABD19518 ## NP_597713 0.900 ## CAA78718 0.891 0.919 ## EAA12598 0.896 0.920 0.922 ## ABA81834 0.935 0.932 0.946 0.882 ## XP_392427 0.934 0.927 0.947 0.878 0.923 ## XP_783573 0.946 0.947 0.941 0.925 0.954 0.943 18.10 Phylognetic trees (finally!) We got our sequence, built multiple sequence alignment, and calculated the genetic distance between sequences. Now we are - finally - ready to build a phylogenetic tree. First, we let R figure out the structure of the tree. There are MANY ways to build phylogenetic trees. We’ll use a common one used for exploring sequences called neighbor joining algorithm via the function nj(). Neighbor joining uses genetic distances to cluster sequences into clades. tree <- nj(shrooms_dist) 18.10.1 Plotting phylogenetic trees Now we’ll make a quick plot of our tree using plot() (and add a little label using a function called mtext()). # plot tree plot.phylo (tree, main="Phylogenetic Tree", type = "unrooted", use.edge.length = F) # add label mtext(text = "Shroom family gene tree - unrooted, no branch lengths") This is an **unrooted tree*. For the sake of plotting we’ve also ignored the evolutionary distance between the sequences. To make a rooted tree we remove type = \"unrooted. # plot tree plot.phylo (tree, main="Phylogenetic Tree", use.edge.length = F) # add label mtext(text = "Shroom family gene tree - rooted, no branch lenths") We can include information about branch length by setting use.edge.length = ... to T. # plot tree plot.phylo (tree, main="Phylogenetic Tree", use.edge.length = T) # add label mtext(text = "Shroom family gene tree - rooted, with branch lenths") Some of the branches are now very short, but most are very long, indicating that these genes have been evolving independently for many millions of years. Let’s make a fancier plot. Don’t worry about all the steps; I’ve added some more code to add some annotations on the right-hand side to help us see what’s going on. plot(tree, main="Phylogenetic Tree") mtext(text = "Shroom family gene tree") x <- 0.551 x2 <- 0.6 # label Shrm 3 segments(x0 = x, y0 = 1, x1 = x, y1 = 4, lwd=2) text(x = x*1.01, y = 2.5, "Shrm 3",adj = 0) segments(x0 = x, y0 = 5, x1 = x, y1 = 6, lwd=2) text(x = x*1.01, y = 5.5, "Shrm 2",adj = 0) segments(x0 = x, y0 = 7, x1 = x, y1 = 9, lwd=2) text(x = x*1.01, y = 8, "Shrm 1",adj = 0) segments(x0 = x, y0 = 10, x1 = x, y1 = 13, lwd=2) text(x = x*1.01, y = 12, "Shrm ?",adj = 0) segments(x0 = x, y0 = 14, x1 = x, y1 = 15, lwd=2) text(x = x*1.01, y = 14.5, "Shrm 4",adj = 0) segments(x0 = x2, y0 = 1, x1 = x2, y1 = 6, lwd=2) segments(x0 = x2, y0 = 7, x1 = x2, y1 = 9, lwd=2) segments(x0 = x2, y0 = 10, x1 = x2, y1 = 15, lwd=2) "],["logarithms-in-r.html", "Chapter 19 Logarithms in R", " Chapter 19 Logarithms in R By Nathan Brouwer Logging splits up multiplication into addition. So, log(m*n) is the same as log(m) + log(n) You can check this m<-10 n<-11 log(m*n) ## [1] 4.70048 log(m)+log(n) ## [1] 4.70048 log(m*n) == log(m)+log(n) ## [1] TRUE Exponentiation undos logs exp(log(m*n)) ## [1] 110 m*n ## [1] 110 The key equation in BLAST’s E values is u = ln(Kmn)/lambda This can be changed to [ln(K) + ln(mn)]/lambda We can check this K <- 1 m <- 10 n <- 11 lambda <- 110 log(K*m*n)/lambda ## [1] 0.04273164 (log(K) + log(m*n))/lambda ## [1] 0.04273164 log(K*m*n)/lambda == (log(K) + log(m*n))/lambda ## [1] TRUE "],["appendix-01-getting-access-to-r.html", "Appendix 01: Getting access to R 19.1 Getting Started With R and RStudio", " Appendix 01: Getting access to R 19.1 Getting Started With R and RStudio R is a piece of software that does calculations and makes graphs. RStudio is a GUI (graphical user interface) that acts as a front-end to R Your can use R directly, but most people use a GUI of some kind RStudio has become the most popular GUI The following instructions will lead you click by click through downloading R and RStudio and starting an initial session. If you have trouble with downloading either program go to YouTube and search for something like “Downloading R” or “Installing RStudio” and you should be able to find something helpful, such as “How to Download R for Windows”. 19.1.1 RStudio Cloud TODO: Add RStudio cloud 19.1.2 Getting R onto your own computer To get R on to your computer first go to the CRAN website at https://cran.r-project.org/ (CRAN stands for “comprehensive R Archive Network”). At the top of the screen are three bullet points; select the appropriate one (or click the link below) Download R for Linux Download R for (Mac) OS X Download R for Windows Each page is formatted slightly differently. For a current Mac, click on the top link, which as of 8/16/2018 was “R-3.5.1.pkg” or click this link. If you have an older Mac you might have to scroll down to find your operating system under “Binaries for legacy OS X systems.” For PC select “base” or click this link. When its downloaded, run the installer and accept the defaults. 19.1.3 Getting RStudio onto your computer RStudio is an R interface developed by a company of the same name. RStudio has a number of commercial products, but much of their portfolio is freeware. You can download RStudio from their website www.rstudio.com/ . The download page (www.rstudio.com/products/rstudio/download/) is a bit busy because it shows all of their commercial products; the free version is on the far left side of the table of products. Click on the big green DOWNLOAD button under the column on the left that says “RStudio Desktop Open Source License” (or click on this link ). This will scroll you down to a list of downloads titled “Installers for Supported Platforms.” Windows users can select the top option RStudio 1.1.456 - Windows Vista/7/8/10 and Mac the second option RStudio 1.1.456 - Mac OS X 10.6+ (64-bit). (Versions names are current of 8/16/2018). Run the installer after it downloads and accept the default. RStudio will automatically link up with the most current version of R you have on your computer. Find the RStudio icon on your desktop or search for “RStudio” from your task bar and you’ll be read to go. 19.1.4 Keep R and RStudio current Both R and RStudio undergo regular updates and you will occasionally have to re-download and install one or both of them. In practice I probably do this about every 6 months. "],["getting-started-with-r-itself-or-not.html", "Getting started with R itself (or not) Vocabulary R commands 19.2 Help! 19.3 Other features of RStudio 19.4 Practice (OPTIONAL)", " Getting started with R itself (or not) Vocabulary console script editor / source viewer interactive programming scripts / script files .R files text files / plain text files command execution / execute a command from script editor comments / code comments commenting out / commenting out code stackoverflow.com the rstats hashtag R commands c(…) mean(…) sd(…) ? read.csv(…) This is a walk-through of a very basic R session. It assumes you have successfully installed R and RStudio onto your computer, and nothing else. Most people who use R do not actually use the program itself - they use a GUI (graphical user interface) “front end” that make R a bit easier to use. However, you will probably run into the icon for the underlying R program on your desktop or elsewhere on your computer. It usually looks like this: ADD IMAGE HERE The long string of numbers have to do with the version and whether is 32 or 64 bit (not important for what we do). If you are curious you can open it up and take a look - it actually looks a lot like RStudio, where we will do all our work (or rather, RStudio looks like R). Sometimes when people are getting started with R they will accidentally open R instead of RStudio; if things don’t seem to look or be working the way you think they should, you might be in R, not RStudio 19.1.4.1 R’s console as a scientific calculator You can interact with R’s console similar to a scientific calculator. For example, you can use parentheses to set up mathematical statements like 5*(1+1) ## [1] 10 Note however that you have to be explicit about multiplication. If you try the following it won’t work. 5(1+1) R also has built-in functions that work similar to what you might have used in Excel. For example, in Excel you can calculate the average of a set of numbers by typing “=average(1,2,3)” into a cell. R can do the same thing except The command is “mean” You don’t start with “=” You have to package up the numbers like what is shown below using “c(…)” mean(c(1,2,3)) ## [1] 2 Where “c(…)” packages up the numbers the way the mean() function wants to see them. If you just do the following R will give you an answer, but its the wrong one mean(1,2,3) This is a common issue with R – and many programs, really – it won’t always tell you when somethind didn’t go as planned. This is because it doesn’t know something didn’t go as planned; you have to learn the rules R plays by. 19.1.4.2 Practice: math in the console See if you can reproduce the following results Division 10/3 ## [1] 3.333333 The standard deviation sd(c(5,10,15)) # note the use of "c(...)" ## [1] 5 19.1.4.3 The script editor While you can interact with R directly within the console, the standard way to work in R is to write what are known as scripts. These are computer code instructions written to R in a script file. These are save with the extension .R but area really just a form of plain text file. To work with scripts, what you do is type commands in the script editor, then tell R to excute the command. This can be done several ways. First, you tell RStudio the line of code you want to run by either * Placing the cursor at the end a line of code, OR * Clicking and dragging over the code you want to run in order highlight it. Second, you tell RStudio to run the code by * Clicking the “Run” icon in the upper right hand side of the script editor (a grey box with a green error emerging from it) * pressing the control key (“ctrl)” and then then enter key on the keyboard The code you’ve chosen to run will be sent by RStudio from the script editor over to the console. The console will show you both the code and then the output. You can run several lines of code if you want; the console will run a line, print the output, and then run the next line. First I’ll use the command mean(), and then the command sd() for the standard deviation: mean(c(1,2,3)) ## [1] 2 sd(c(1,2,3)) ## [1] 1 19.1.4.4 Comments One of the reasons we use script files is that we can combine R code with comments that tell us what the R code is doing. Comments are preceded by the hashtag symbol #. Frequently we’ll write code like this: #The mean of 3 numbers mean(c(1,2,3)) If you highlight all of this code (including the comment) and then click on “run”, you’ll see that RStudio sends all of the code over console. ## [1] 2 Comments can also be placed at the end of a line of code mean(c(1,2,3)) #Note the use of c(...) Sometimes we write code and then don’t want R to run it. We can prevent R from executing the code even if its sent to the console by putting a “#” infront of the code. If I run this code, I will get just the mean but not the sd. mean(c(1,2,3)) #sd(c(1,2,3)) Doing this is called commenting out a line of code. 19.2 Help! There are many resource for figuring out R and RStudio, including R’s built in “help” function Q&A websites like stackoverflow.com twitter, using the hashtag #rstats blogs online books and course materials 19.2.1 Getting “help” from R If you are using a function in R you can get info about how it works like this ?mean In RStudio the help screen should appear, probably above your console. If you start reading this help file, though, you don’t have to go far until you start seeing lots of R lingo, like “S3 method”,“na.rm”, “vectors”. Unfortunately, the R help files are usually not written for beginners, and reading help files is a skill you have to acquire. For example, when we load data into R in subsequent lessons we will use a function called “read.csv” Access the help file by typing “?read.csv” into the console and pressing enter. Surprisingly, the function that R give you the help file isn’t what you asked for, but is read.table(). This is a related function to read.csv, but when you’re a beginner thing like this can really throw you off. Kieran Healy as produced a great cheatsheet for reading R’s help pages as part of his forthcoming book. It should be available online at http://socviz.co/appendix.html#a-little-more-about-r 19.2.2 Getting help from the internet The best way to get help for any topic is to just do an internet search like this: “R read.csv”. Usually the first thing on the results list will be the R help file, but the second or third will be a blog post or something else where a usually helpful person has discussed how that function works. Sometimes for very basic R commands like this might not always be productive but its always work a try. For but things related to stats, plotting, and programming there is frequently lots of information. Also try searching YouTube. 19.2.3 Getting help from online forums Often when you do an internet search for an R topic you’ll see results from the website www.stackoverflow.com, or maybe www.crossvalidated.com if its a statistics topic. These are excellent resources and many questions that you may have already have answers on them. Stackoverflow has an internal search function and also suggests potentially relevant posts. Before posting to one of these sites yourself, however, do some research; there is a particular type and format of question that is most likely to get a useful response. Sadly, people new to the site often get “flamed” by impatient pros. 19.2.4 Getting help from twitter Twitter is a surprisingly good place to get information or to find other people knew to R. Its often most useful to ask people for learning resources or general reference, but you can also post direct questions and see if anyone responds, though usually its more advanced users who engage in twitter-based code discussion. A standard tweet might be “Hey #rstats twitter, am knew to #rstats and really stuck on some of the basics. Any suggestions for good resources for someone starting from scratch?” 19.3 Other features of RStudio 19.3.1 Ajusting pane the layout You can adjust the location of each of RStudio 4 window panes, as well as their size. To set the pane layout go to 1. ”Tools” on the top menu 1. ”Global options” 1. “Pane Layout” Use the drop-down menus to set things up. I recommend 1. Lower left: “Console”” 1. Top right: “Source” 1. Top left: “Plot, Packages, Help Viewer” 1. This will leave the “Environment…” panel in the lower right. 19.3.2 Adjusting size of windows You can clicked on the edge of a pane and adjust its size. For most R work we want the console to be big. For beginners, the “Environment, history, files” panel can be made really small. 19.4 Practice (OPTIONAL) Practice the following operations. Type the directly into the console and execute them. Also write them in a script in the script editor and run them. Square roots sqrt(42) ## [1] 6.480741 The date Some functions in R can be executed within nothing in the parentheses. date() ## [1] "Sat Sep 18 00:31:13 2021" Exponents The ^ is used for exponents 42^2 ## [1] 1764 A series of numbers A colon between two numbers creates a series of numbers. 1:42 ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 logs The default for the log() function is the natural log. log(42) ## [1] 3.73767 log10() gives the base-10 log. log10(42) ## [1] 1.623249 exp() raises e to a power exp(3.73767) ## [1] 42.00002 Multiple commands can be nested sqrt(42)^2 log(sqrt(42)^2) exp(log(sqrt(42)^2)) "]] +[["index.html", "A Little Book of R for Bioinformatics 2.0 Preface to version 2.0", " A Little Book of R for Bioinformatics 2.0 Avirl Coghlan, with contributions by Nathan L. Brouwer 2021-09-18 Preface to version 2.0 Welcome to A Little Book of R for Bioinformatics 2.0!. This book is based on the original A Little Book of R for Bioinformatics by Dr. Avril Coghlan (Hereafter “ALBRB 1.0”). Dr. Coghlan’s book was one of the first and most thorough introductions to using R for bioinformatics and computational biology, and was generously published under the Creative Commons 3.0 Attribution License (CC BY 3.0). In addition to describing how to do bioinformatics in R, Coghlan provided numerous functions to facilitate important tasks, practice questions, and references to further reading. ALBRB 1.0 was extremely useful to me when I was learning bioinformatics and computational biology. In this version of the book, which I’ll refer to as ALBRB 2.0, I have adapted Dr. Coghlan’s original book to suit my own teaching needs, updated it with current packages now available in R, added background materials from other open-access sources, and added in my original materials. Below I’ve outlined the general types of changes I’ve made to the original book. I have tried to link back to the original content that these updates are derived from and note how changes were made. Any errors or inconsistencies should be ascribed to me, not Dr. Coghlan. If you have any feedback, please email me at brouwern@gmail.com ~Nathan Brouwer, June 2021 Changes implemented in ALBRB 2.0 by Nathan Brouwer Converted the entire book to RMarkdown and published it via bookdown. Added instructions for using RStudio and RStudio Cloud. Updated instructions to reflect any changes in software, including changes to how the bioinformatics repository Bioconductor now works. Split up chapters into smaller units. Reorganized the order of some material. Added biological background information by integrating information from the Open Access textbook LibreText General Biology Added links to the books I am developing, Get R Done! and Computational Biology for All. Moved functions written by Coghlan and datasets to my teaching package compbio4all. Functions names changed from camelCase to snake_case Functions re-written so as not to use Bioconductor to reduce/eliminate dependency of compbio4all on Bioconductor. Changed some plotting to ggplot2 or ggpubr. Added additional subheadings Added vocab and function lists to the beginning of many chapters At times replaced non-biological examples with biological ones. Change from British to American English (Sorry! Couldn’t help myself - the spellchecker made me do it!) Provided additional links to external resources. Added use of rentrez for querying NCBI databases "],["downloadR.html", "Chapter 1 Downloading R 1.1 Preface 1.2 Introduction to R 1.3 Installing R 1.4 Starting R", " Chapter 1 Downloading R By: Avril Coghlan Adapted, edited and expanded: Nathan Brouwer (brouwern@gmail.com) under the Creative Commons 3.0 Attribution License (CC BY 3.0). 1.1 Preface The following introduction to R is based on the first part of “How to install R and a Brief Introduction to R” by Avril Coghlan, which was released under the Creative Commons 3.0 Attribution License (CC BY 3.0). For additional information see the Appendices and “Getting R onto your computer”. 1.2 Introduction to R R (www.r-project.org) is a commonly used free statistics software. R allows you to carry out statistical analyses in an interactive mode, as well as allowing programming. 1.3 Installing R To use R, you first need to install the R program on your computer. 1.3.1 Installing R on a Windows PC These instructions will focus on installing R on a Windows PC. However, I will also briefly mention how to install R on a Macintosh or Linux computer (see below). These steps have not been checked as of 8/13/2019 so there may be small variations in what the prompts are. Installing R, however, is basically that same as any other program. Clicking “Yes” etc on everything should work. PROTIP: Even if you have used R before its good to regularly update it to avoid conflicts with recently produced software. Minor updates of R are made very regularly (approximately every 6 months), as R is actively being improved all the time. It is worthwhile installing new versions of R a couple times a year, to make sure that you have a recent version of R (to ensure compatibility with all the latest versions of the R packages that you have downloaded). To install R on your Windows computer, follow these steps: Go to https://cran.r-project.org/ Under “Download and Install R”, click on the “Windows” link. Under “Subdirectories”, click on the “base” link. On the next page, you should see a link saying something like “Download R 4.1.0 for Windows” (or R X.X.X, where X.X.X gives the version of the program). Click on this link. You may be asked if you want to save or run a file “R-x.x.x-win32.exe”. Choose “Save” and save the file. Then double-click on the icon for the file to run it. You will be asked what language to install it in. The R Setup Wizard will appear in a window. Click “Next” at the bottom of the R Setup wizard window. The next page says “Information” at the top. Click “Next” again. The next page says “Select Destination Location” at the top. By default, it will suggest to install R on the C drive in the “Program Files” directory on your computer. Click “Next” at the bottom of the R Setup wizard window. The next page says “Select components” at the top. Click “Next” again. The next page says “Startup options” at the top. Click “Next” again. The next page says “Select start menu folder” at the top. Click “Next” again. The next page says “Select additional tasks” at the top. Click “Next” again. R should now be installing. This will take about a minute. When R has finished, you will see “Completing the R for Windows Setup Wizard” appear. Click “Finish”. To start R, you can do one of the following steps: Check if there is an “R” icon on the desktop of the computer that you are using. If so, double-click on the “R” icon to start R. If you cannot find an “R” icon, try the next step instead. Click on the “Start” button at the bottom left of your computer screen, and then choose “All programs”, and start R by selecting “R” (or R X.X.X, where X.X.X gives the version of R) from the menu of programs. The R console (a rectangle) should pop up: 1.3.2 How to install R on non-Windows computers (eg. Macintosh or Linux computers) These steps have not been checked as of 8/13/2019 so there may be small variations in what the prompts are. Installing R, however, is basically that same as any other program. Clicking “Yes” etc on everything should work. The instructions above are for installing R on a Windows PC. If you want to install R on a computer that has a non-Windows operating system (for example, a Macintosh or computer running Linux, you should download the appropriate R installer for that operating system at https://cran.r-project.org/ and follow the R installation instructions for the appropriate operating system at https://cran.r-project.org/doc/FAQ/R-FAQ.html#How-can-R-be-installed_003f . 1.4 Starting R To start R, Check if there is an R icon on the desktop of the computer that you are using. If so, double-click on the R icon to start R. If you cannot find an R icon, try the next step instead. You can also start R from the Start menu in Windows. Click on the “Start” button at the bottom left of your computer screen, and then choose “All programs”, and start R by selecting “R” (or R X.X.X, where X.X.X gives the version of R, e.g.. R 2.10.0) from the menu of programs. Say “Hi” to R and take a quick look at how it looks. Now say “Goodbye”, because we will never actually do any work in this version of R; instead, we’ll use the RStudio IDE (integrated development environment). "],["installing-the-rstudio-ide-installrstudio.html", "Chapter 2 Installing the RStudio IDE {$installRStudio} 2.1 Getting to know RStudio 2.2 RStudio versus RStudio Cloud", " Chapter 2 Installing the RStudio IDE {$installRStudio} By: Nathan Brouwer The name “R” refers both to the programming language and the program that runs that language. When you download itR* there is also a basic GUI (graphical user interface) that you can access via the R icon. Other GUIs are available, and the most popular currently is RStudio. RStudio a for-profit company that is a main driver of development of R. Much of what they produce has free basic versions or is entirely free. They produce software (RStudio), cloud-based applications (RStudio Cloud), and web server infrastructure for business applications of R. A brief overview of installing RStudio can be found here “Getting RStudio on to your computer” 2.1 Getting to know RStudio For a brief overview of RStudio see “Getting started with RStudio” A good overview of what the different parts of RStudio can be seen in the image in this tweet: https://twitter.com/RLadiesNCL/status/1138812826917724160?s=20 2.2 RStudio versus RStudio Cloud RStudio and RStudio cloud work almost identically, so anything you read about RStudio will apply to RStudio Cloud. RStudio is easy to download an use, but RStudio Cloud eliminates even the minor hiccups that occur. Free accounts with RStudio Cloud allow up to 15 hours per month, which is enough for you to get a taste for using R. "],["installing-packages.html", "Chapter 3 Installing R packages 3.1 Downloading packages with the RStudio IDE 3.2 Downloading packages with the function install.packages() 3.3 Using packages after they are downloaded", " Chapter 3 Installing R packages By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). R is a programming language, and packages (aka libraries) are bundles of software built using R. Most sessions using R involve using additional R packages. This is especially true for bioinformatics and computational biology. NOTE: If you are working in an RStudio Cloud environment organized by someone else (e.g. a course instructor), they likely are taking care of many of the package management issues. The following information is still useful to be familiar with. 3.1 Downloading packages with the RStudio IDE There is a point-and-click interface for installing R packages in RStudio. There is a brief introduction to downloading packages on this site: http://web.cs.ucla.edu/~gulzar/rstudio/ I’ve summarized it here: “Click on the”Packages” tab in the bottom-right section and then click on “Install”. The following dialog box will appear. In the “Install Packages” dialog, write the package name you want to install under the Packages field and then click install. This will install the package you searched for or give you a list of matching package based on your package text. 3.2 Downloading packages with the function install.packages() The easiest way to install a package if you know its name is to use the R function install.packages()`. Note that it might be better to call this “download.packages” since after you install it, you also have to load it! Frequently I will include install.packages(...) at the beginning of a chapter the first time we use a package to make sure the package is downloaded. Note, however, that if you already have downloaded the package, running install.packages(...) will download a new copy. While packages do get updated from time to time, but its best to re-run install.packages(...) only occassionaly. We’ll download a package used for plotting called ggplot2, which stands for “Grammar of Graphics.” ggplot2 was developed by Dr. Hadley Wickham, who is now the Chief Scientists for RStudio. To download ggplot2, run the following command: install.packages("ggplot2") # note the " " Often when you download a package you’ll see a fair bit of angry-looking red text, and sometime other things will pop up. Usually there’s nothing of interest here, but sometimes you need to read things carefully over it for hints about why something didn’t work. 3.3 Using packages after they are downloaded To actually make the functions in package accessible you need to use the library() command. Note that this is not in quotes. library(ggplot2) # note: NO " " "],["installing-bioconductor.html", "Chapter 4 Installing Bioconductor 4.1 Bioconductor 4.2 Installing BiocManager 4.3 The ins and outs of package installation 4.4 Actually loading a package", " Chapter 4 Installing Bioconductor By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0), including details on install Bioconductor and common prompts and error messages that appear during installation. 4.1 Bioconductor R packages (aka “libraries”) can live in many places. Most are accessed via CRAN, the Comprehensive R Archive Network. The bioinformatics and computational biology community also has its own package hosting system called Bioconductor. R has played an important part in the development and application of bioinformatics techniques in the 21th century. Bioconductor 1.0 was released in 2002 with 15 packages. As of winter 2021, there are almost 2000 packages in the current release! NOTE: If you are working in an RStudio Cloud environment organized by someone else (eg a course instructor), they likely are taking care of most of package management issues, inlcuding setting up Bioconductor. The following information is still useful to be familiar with. To interface with Bioconductor you need the BiocManager package. The Bioconductor people have put BiocManager on CRAN to allow you to set up interactions with Bioconductor. See the BiocManager documentation for more information (https://cran.r-project.org/web/packages/BiocManager/vignettes/BiocManager.html). Note that if you have an old version of R you will need to update it to interact with Bioconductor. 4.2 Installing BiocManager BiocManager can be installed using the install.packages() packages command. install.packages("BiocManager") # Remember the " "; don't worry about the red text Once downloaded, BioManager needs to be explicitly loaded into your active R session using library() library(BiocManager) # no quotes; again, ignore the red text Individual Bioconductor packages can then be downloaded using the install() command. An essential packages is Biostrings. To do this , BiocManager::install("Biostrings") 4.3 The ins and outs of package installation IMPORANT Bioconductor has many dependencies - other packages which is relies on. When you install Bioconductor packages you may need to update these packages. If something seems to not be working during this process, restart R and begin the Bioconductor installation process until things seem to work. Below I discuss the series of prompts I had to deal with while re-installing Biostrings while editing this chapter. 4.3.1 Updating other packages when downloading a package When I re-installed Biostrings while writing this I was given a HUGE blog of red test that contained something like what’s shown below (this only aout 1/3 of the actual output!): 'getOption("repos")' replaces Bioconductor standard repositories, see '?repositories' for details replacement repositories: CRAN: https://cran.rstudio.com/ Bioconductor version 3.11 (BiocManager 1.30.16), R 4.0.5 (2021-03-31) Old packages: 'ade4', 'ape', 'aster', 'bayestestR', 'bio3d', 'bitops', 'blogdown', 'bookdown', 'brio', 'broom', 'broom.mixed', 'broomExtra', 'bslib', 'cachem', 'callr', 'car', 'circlize', 'class', 'cli', 'cluster', 'colorspace', 'corrplot', 'cpp11', 'curl', 'devtools', 'DHARMa', 'doBy', 'dplyr', 'DT', 'e1071', 'ellipsis', 'emmeans', 'emojifont', 'extRemes', 'fansi', 'flextable', 'forecast', 'formatR', 'gap', 'gargle', 'gert', 'GGally' Hidden at the bottom was a prompt: \"Update all/some/none? [a/s/n]:\" Its a little vague, but what it wants me to do is type in a, s or n and press enter to tell it what to do. I almost always chose a, though this may take a while to update everything. 4.3.2 Packages “from source” You are likely to get lots of random-looking feedback from R when doing Bioconductor-related installations. Look carefully for any prompts as the very last line. While updating Biostrings I was told: “There are binary versions available but the source versions are later:” and given a table of packages. I was then asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)” I almost always chose “no”. 4.3.3 More on angry red text After the prompt about packages from source, R proceeded to download a lot of updates to packages, which took a few minutes. Lots of red text scrolled by, but this is normal. 4.4 Actually loading a package Again, to actually load the Biostrings package into your active R sessions requires the libary() command: library(Biostrings) As you might expect, there’s more red text scrolling up my screen! I can tell that is actually worked because at the end of all the red stuff is the R prompt of “>” and my cursor. "],["basic-R.html", "Chapter 5 A Brief introduction to R 5.1 Vocabulary 5.2 R functions 5.3 Interacting with R 5.4 Variables in R 5.5 Arguments 5.6 Help files with help() and ? 5.7 Searching for functions with help.search() and RSiteSearch() 5.8 More on functions 5.9 Quiting R 5.10 Links and Further Reading", " Chapter 5 A Brief introduction to R By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). This chapter provides a brief introduction to R. At the end of are links to additional resources for getting started with R. 5.1 Vocabulary scalar vector list class numeric character assignment elements of an object indices attributes of an object argument of a function 5.2 R functions <- [ ] $ table() function c() log10() help(), ? help.search() RSiteSearch() mean() return() q() 5.3 Interacting with R You will type R commands into the RStudio console in order to carry out analyses in R. In the RStudio console you will see the R prompt starting with the symbol “>”. “>” will always be there at the beginning of each new command - don’t try to delete it! Moreover, you never need to type it. We type the commands needed for a particular task after this prompt. The command is carried out by R after you hit the Return key. Once you have started R, you can start typing commands into the RStudio console, and the results will be calculated immediately, for example: 2*3 ## [1] 6 Note that prior to the output of “6” it shows “[1]”. Now subtraction: 10-3 ## [1] 7 Again, prior to the output of “7” it shows “[1]”. R can act like a basic calculator that you type commands in to. You can also use it like a more advanced scientific calculator and create variables that store information. All variables created by R are called objects. In R, we assign values to variables using an arrow-looking function <- the assignment operator. For example, we can assign the value 2*3 to the variable x using the command: x <- 2*3 To view the contents of any R object, just type its name, press enter, and the contents of that R object will be displayed: x ## [1] 6 5.4 Variables in R There are several different types of objects in R with fancy math names, including scalars, vectors, matrices (singular: matrix), arrays, dataframes, tables, and lists. The scalar** variable x above is one example of an R object. While a scalar variable such as x has just one element, a vector consists of several elements. The elements in a vector are all of the same type (e.g.. numbers or alphabetic characters), while lists may include elements such as characters as well as numeric quantities. Vectors and dataframes are the most common variables you’ll use. You’ll also encounter matrices often, and lists are ubiquitous in R but beginning users often don’t encounter them because they remain behind the scenes. 5.4.1 Vectors To create a vector, we can use the c() (combine) function. For example, to create a vector called myvector that has elements with values 8, 6, 9, 10, and 5, we type: myvector <- c(8, 6, 9, 10, 5) # note: commas between each number! To see the contents of the variable myvector, we can just type its name and press enter: myvector ## [1] 8 6 9 10 5 5.4.2 Vector indexing The [1] is the index of the first element in the vector. We can extract any element of the vector by typing the vector name with the index of that element given in square brackets [...]. For example, to get the value of the 4th element in the vector myvector, we type: myvector[4] ## [1] 10 5.4.3 Character vectors Vectors can contain letters, such as those designating nucleic acids my.seq <- c("A","T","C","G") They can also contain multi-letter strings: my.oligos <- c("ATCGC","TTTCGC","CCCGCG","GGGCGC") 5.4.4 Lists NOTE: below is a discussion of lists in R. This is excellent information, but not necessary if this is your very very first time using R. In contrast to a vector, a list can contain elements of different types, for example, both numbers and letters. A list can even include other variables such as a vector. The list() function is used to create a list. For example, we could create a list mylist by typing: mylist <- list(name="Charles Darwin", wife="Emma Darwin", myvector) We can then print out the contents of the list mylist by typing its name: mylist ## $name ## [1] "Charles Darwin" ## ## $wife ## [1] "Emma Darwin" ## ## [[3]] ## [1] 8 6 9 10 5 The elements in a list are numbered, and can be referred to using indices. We can extract an element of a list by typing the list name with the index of the element given in double square brackets (in contrast to a vector, where we only use single square brackets). We can extract the second element from mylist by typing: mylist[[2]] # note the double square brackets [[...]] ## [1] "Emma Darwin" As a baby step towards our next task, we can wrap index values as in the c() command like this: mylist[[c(2)]] # note the double square brackets [[...]] ## [1] "Emma Darwin" The number 2 and c(2) mean the same thing. Now, we can extract the second AND third elements from mylist. First, we put the indices 2 and 3 into a vector c(2,3), then wrap that vector in double square brackets: [c(2,3)]. All together it looks like this. mylist[c(2,3)] # note the double brackets ## $wife ## [1] "Emma Darwin" ## ## [[2]] ## [1] 8 6 9 10 5 Elements of lists may also be named, resulting in a named lists. The elements may then be referred to by giving the list name, followed by “$”, followed by the element name. For example, mylist$name is the same as mylist[[1]] and mylist$wife is the same as mylist[[2]]: mylist$wife ## [1] "Emma Darwin" We can find out the names of the named elements in a list by using the attributes() function, for example: attributes(mylist) ## $names ## [1] "name" "wife" "" When you use the attributes() function to find the named elements of a list variable, the named elements are always listed under a heading “$names”. Therefore, we see that the named elements of the list variable mylist are called “name” and “wife”, and we can retrieve their values by typing mylist$name and mylist$wife, respectively. 5.4.5 Tables Another type of object that you will encounter in R is a table. The table() function allows you to total up or tabulate the number of times a value occurs within a vector. Tables are typically used on vectors containing character data, such as letters, words, or names, but can work on numeric data data. 5.4.5.1 Tables - The basics If we made a vector variable “nucleotides” containing the of a DNA molecule, we can use the table() function to produce a table variable that contains the number of bases with each possible nucleotides: bases <- c("A", "T", "A", "A", "T", "C", "G", "C", "G") Now make the table table(bases) ## bases ## A C G T ## 3 2 2 2 We can store the table variable produced by the function table(), and call the stored table “bases.table”, by typing: bases.table <- table(bases) Tables also work on vectors containing numbers. First, a vector of numbers. numeric.vecter <- c(1,1,1,1,3,4,4,4,4) Second, a table, showing how many times each number occurs. table(numeric.vecter) ## numeric.vecter ## 1 3 4 ## 4 1 4 5.4.5.2 Tables - further details To access elements in a table variable, you need to use double square brackets, just like accessing elements in a list. For example, to access the fourth element in the table bases.table (the number of Ts in the sequence), we type: bases.table[[4]] # double brackets! ## [1] 2 Alternatively, you can use the name of the fourth element in the table (“John”) to find the value of that table element: bases.table[["T"]] ## [1] 2 5.5 Arguments Functions in R usually require arguments, which are input variables (i.e.. objects) that are passed to them, which they then carry out some operation on. For example, the log10() function is passed a number, and it then calculates the log to the base 10 of that number: log10(100) ## [1] 2 There’s a more generic function, log(), where we pass it not only a number to take the log of, but also the specific base of the logarithm. To take the log base 10 with the log() function we do this log(100, base = 10) ## [1] 2 We can also take logs with other bases, such as 2: log(100, base = 2) ## [1] 6.643856 5.6 Help files with help() and ? In R, you can get help about a particular function by using the help() function. For example, if you want help about the log10() function, you can type: help("log10") When you use the help() function, a box or web page will show up in one of the panes of RStudio with information about the function that you asked for help with. You can also use the ? next to the function like this ?log10 Help files are a mixed bag in R, and it can take some getting used to them. An excellent overview of this is Kieran Healy’s “How to read an R help page.” 5.7 Searching for functions with help.search() and RSiteSearch() If you are not sure of the name of a function, but think you know part of its name, you can search for the function name using the help.search() and RSiteSearch() functions. The help.search() function searches to see if you already have a function installed (from one of the R packages that you have installed) that may be related to some topic you’re interested in. RSiteSearch() searches all R functions (including those in packages that you haven’t yet installed) for functions related to the topic you are interested in. For example, if you want to know if there is a function to calculate the standard deviation (SD) of a set of numbers, you can search for the names of all installed functions containing the word “deviation” in their description by typing: help.search("deviation") Among the functions that were found, is the function sd() in the stats package (an R package that comes with the base R installation), which is used for calculating the standard deviation. Now, instead of searching just the packages we’ve have on our computer let’s search all R packages on CRAN. Let’s look for things related to DNA. Note that RSiteSearch() doesn’t provide output within RStudio, but rather opens up your web browser for you to display the results. RSiteSearch("DNA") The results of the RSiteSearch() function will be hits to descriptions of R functions, as well as to R mailing list discussions of those functions. 5.8 More on functions We can perform computations with R using objects such as scalars and vectors. For example, to calculate the average of the values in the vector myvector (i.e.. the average of 8, 6, 9, 10 and 5), we can use the mean() function: mean(myvector) # note: no " " ## [1] 7.6 We have been using built-in R functions such as mean(), length(), print(), plot(), etc. 5.8.1 Writing your own functions NOTE: *Writing your own functions is an advanced skills. New users can skip this section. We can also create our own functions in R to do calculations that you want to carry out very often on different input data sets. For example, we can create a function to calculate the value of 20 plus square of some input number: myfunction <- function(x) { return(20 + (x*x)) } This function will calculate the square of a number (x), and then add 20 to that value. The return() statement returns the calculated value. Once you have typed in this function, the function is then available for use. For example, we can use the function for different input numbers (e.g.. 10, 25): myfunction(10) ## [1] 120 5.9 Quiting R To quit R either close the program, or type: q() 5.10 Links and Further Reading Some links are included here for further reading. For a more in-depth introduction to R, a good online tutorial is available on the “Kickstarting R” website, cran.r-project.org/doc/contrib/Lemon-kickstart. There is another nice (slightly more in-depth) tutorial to R available on the “Introduction to R” website, cran.r-project.org/doc/manuals/R-intro.html. Chapter 3 of Danielle Navarro’s book is an excellent intro to the basics of R. "],["vectors-in-R.html", "Chapter 6 A primer for working with vectors 6.1 Preface 6.2 Vocab", " Chapter 6 A primer for working with vectors By: Avril Coghlan Adapted, edited and expanded: Nathan Brouwer (brouwern@gmail.com) under the Creative Commons 3.0 Attribution License (CC BY 3.0). 6.1 Preface This is a modification of part of“DNA Sequence Statistics (2)” from Avril Coghlan’s A little book of R for bioinformatics.. Most of text and code was originally written by Dr. Coghlan and distributed under the Creative Commons 3.0 license. 6.2 Vocab base R scalar, vector, matrix vectorized operation regular expressions "],["functions.html", "Chapter 7 Functions 7.1 Vectors in R 7.2 Math on vectors 7.3 Functions on vectors 7.4 Operations with two vectors 7.5 Subsetting vectors 7.6 Sequences of numbers 7.7 Vectors can hold numeric or character data 7.8 Regular expressions can modify character data", " Chapter 7 Functions seq() is(), is.vector(), is.matrix() gsub() 7.1 Vectors in R Variables in R include scalars, vectors, and lists. Functions in R carry out operations on variables, for example, using the log10() function to calculate the log to the base 10 of a scalar variable x, or using the mean() function to calculate the average of the values in a vector variable myvector. For example, we can use log10() on a scalar object like this: # store value in object x <- 100 # take log base 10 of object log10(x) ## [1] 2 Note that while mathematically x is a single number, or a scalar, R considers it to be a vector: is.vector(x) ## [1] TRUE There are many “is” commands. What is returned when you run is.matrix() on a vector? is.matrix(x) ## [1] FALSE Mathematically this is a bit odd, since often a vector is defined as a one-dimensional matrix, e.g., a single column or single row of a matrix. But in R land, a vector is a vector, and matrix is a matrix, and there are no explicit scalars. 7.2 Math on vectors Vectors can serve as the input for mathematical operations. When this is done R does the mathematical operation separately on each element of the vector. This is a unique feature of R that can be hard to get used to even for people with previous programming experience. Let’s make a vector of numbers: myvector <- c(30,16,303,99,11,111) What happens when we multiply myvector by 10? myvector*10 ## [1] 300 160 3030 990 110 1110 R has taken each of the 6 values, 30 through 111, of myvector and multiplied each one by 10, giving us 6 results. That is, what R did was ## 30*10 # first value of myvector ## 16*10 # second value of myvector ## 303*10 # .... ## 99*10 ## 111*10 # last value of myvector The normal order of operations rules apply to vectors as they do to operations we’re more used to. So multiplying myvector by 10 is the same whether you put he 10 before or after vector. That is myvector\\*10 is the same as 10\\*myvector. myvector*10 ## [1] 300 160 3030 990 110 1110 10*myvector ## [1] 300 160 3030 990 110 1110 What happen when you subtract 30 from myvector? Write the code below. myvector-30 ## [1] 0 -14 273 69 -19 81 So, what R did was ## 30-30 # first value of myvector ## 16-30 # second value of myvector ## 303-30 # .... ## 99-30 ## 111-30 # last value of myvector Again, myvector-30 is vectorized operation. You can also square a vector myvector^2 ## [1] 900 256 91809 9801 121 12321 Which is the same as ## 30^2 # first value of myvector ## 16^2 # second value of myvector ## 303^2 # .... ## 99^2 ## 111^2 # last value of myvector Also you can take the square root of a vector using the functions sqrt()… sqrt(myvector) ## [1] 5.477226 4.000000 17.406895 9.949874 3.316625 10.535654 …and take the log of a vector with log()… log(myvector) ## [1] 3.401197 2.772589 5.713733 4.595120 2.397895 4.709530 …and just about any other mathematical operation. Here we are working on a separate vector object; all of these rules apply to a column in a matrix or a dataframe. This attribute of R is called vectorization. When you run the code myvector*10 or log(myvector) you are doing a vectorized operation - its like normal math with special vector-based super power to get more done faster than you normally could. 7.3 Functions on vectors As we just saw, we can use functions on vectors. Typically these use the vectors as an input and all the numbers are processed into an output. Call the mean() function on the vector we made called myvector. mean(myvector) ## [1] 95 Note how we get a single value back - the mean of all the values in the vector. R saw that we had a vector of multiple and knew that the mean is a function that doesn’t get applied to single number, but sets of numbers. The function sd() calculates the standard deviation. Apply the sd() to myvector: sd(myvector) ## [1] 110.5061 7.4 Operations with two vectors You can also subtract one vector from another vector. This can be a little weird when you first see it. Make another vector with the numbers 5, 10, 15, 20, 25, 30. Call this myvector2: myvector2 <- c(5, 10, 15, 20, 25, 30) Now subtract myvector2 from myvector. What happens? myvector-myvector2 ## [1] 25 6 288 79 -14 81 7.5 Subsetting vectors You can extract an element of a vector by typing the vector name with the index of that element given in square brackets. For example, to get the value of the 3rd element in the vector myvector, we type: myvector[3] ## [1] 303 Extract the 4th element of the vector: myvector[4] ## [1] 99 You can extract more than one element by using a vector in the brackets: First, say I want to extract the 3rd and the 4th element. I can make a vector with 3 and 4 in it: nums <- c(3,4) Then put that vector in the brackets: myvector[nums] ## [1] 303 99 We can also do it directly like this, skipping the vector-creation step: myvector[c(3,4)] ## [1] 303 99 In the chunk below extract the 1st and 2nd elements: myvector[c(1,2)] ## [1] 30 16 7.6 Sequences of numbers Often we want a vector of numbers in sequential order. That is, a vector with the numbers 1, 2, 3, 4, … or 5, 10, 15, 20, … The easiest way to do this is using a colon 1:10 ## [1] 1 2 3 4 5 6 7 8 9 10 Note that in R 1:10 is equivalent to c(1:10) c(1:10) ## [1] 1 2 3 4 5 6 7 8 9 10 Usually to emphasize that a vector is being created I will use c(1:10) We can do any number to any numbers c(20:30) ## [1] 20 21 22 23 24 25 26 27 28 29 30 We can also do it in reverse. In the code below put 30 before 20: c(30:20) ## [1] 30 29 28 27 26 25 24 23 22 21 20 A useful function in R is the seq() function, which is an explicit function that can be used to create a vector containing a sequence of numbers that run from a particular number to another particular number. seq(1, 10) ## [1] 1 2 3 4 5 6 7 8 9 10 Using seq() instead of a : can be useful for readability to make it explicit what is going on. More importantly, seq has an argument by = ... so you can make a sequence of number with any interval between For example, if we want to create the sequence of numbers from 1 to 10 in steps of 1 (i.e.. 1, 2, 3, 4, … 10), we can type: seq(1, 10, by = 1) ## [1] 1 2 3 4 5 6 7 8 9 10 We can change the step size by altering the value of the by argument given to the function seq(). For example, if we want to create a sequence of numbers from 1-100 in steps of 20 (i.e.. 1, 21, 41, … 101), we can type: seq(1, 101, by = 20) ## [1] 1 21 41 61 81 101 7.7 Vectors can hold numeric or character data The vector we created above holds numeric data, as indicated by class() class(myvector) ## [1] "numeric" Vectors can also holder character data, like the genetic code: # vector of character data myvector <- c("A","T","G") # how it looks myvector ## [1] "A" "T" "G" # what is "is" class(myvector) ## [1] "character" 7.8 Regular expressions can modify character data We can use regular expressions to modify character data. For example, change the Ts to Us myvector <- gsub("T", "U", myvector) Now check it out myvector ## [1] "A" "U" "G" Regular expressions are a deep subject in computing. You can find some more information about them here. "],["plotting-vectors.html", "Chapter 8 Plotting vectors in base R 8.1 Preface 8.2 Plotting numeric data 8.3 Other plotting packages", " Chapter 8 Plotting vectors in base R By: Avril Coghlan Adapted, edited and expanded: Nathan Brouwer (brouwern@gmail.com) under the Creative Commons 3.0 Attribution License (CC BY 3.0). 8.1 Preface This is a modification of part of“DNA Sequence Statistics (2)” from Avril Coghlan’s A little book of R for bioinformatics.. Most of text and code was originally written by Dr. Coghlan and distributed under the Creative Commons 3.0 license. 8.2 Plotting numeric data R allows the production of a variety of plots, including scatterplots, histograms, piecharts, and boxplots. Usually we make plots from dataframes with 2 or more columns, but we can also make them from two separate vectors. This flexibility is useful, but also can cause some confusion. For example, if you have two equal-length vectors of numbers numeric.vect1 and numeric.vect1, you can plot a scatterplot of the values in myvector1 against the values in myvector2 using the base R plot()function. First, let’s make up some data in put it in vectors: numeric.vect1 <- c(10, 15, 22, 35, 43) numeric.vect2 <- c(3, 3.2, 3.9, 4.1, 5.2) Not plot with the base R plot() function: plot(numeric.vect1, numeric.vect2) Note that there is a comma between the two vector names. When building plots from dataframes you usually see a tilde (~), but when you have two vectors you can use just a comma. Also note the order of the vectors within the plot() command and which axes they appear on. The first vector is numeric.vect1 and it appears on the horizontal x-axis. If you want to label the axes on the plot, you can do this by giving the plot() function values for its optional arguments xlab = and ylab =: plot(numeric.vect1, # note again the comma, not a ~ numeric.vect2, xlab="vector1", ylab="vector2") We can store character data in vectors so if we want we could do this to set up our labels: mylabels <- c("numeric.vect1","numeric.vect2") Then use bracket notation to call the labels from the vector plot(numeric.vect1, numeric.vect2, xlab=mylabels[1], ylab=mylabels[2]) If we want we can use a tilde to make our plot like this: plot(numeric.vect2 ~ numeric.vect1) Note that now, numeric.vect2 is on the left and numeric.vect1 is on the right. This flexibility can be tricky to keep track of. We can also combine these vectors into a dataframe and plot the data by referencing the data frame. First, we combine the two separate vectors into a dataframe using the cbind() command. df <- cbind(numeric.vect1, numeric.vect2) Then we plot it like this, referencing the dataframe df via the data = ... argument. plot(numeric.vect2 ~ numeric.vect1, data = df) 8.3 Other plotting packages Base R has lots of plotting functions; additionally, people have written packages to implement new plotting capabilities. The package ggplot2 is currently the most popular plotting package, and ggpubr is a package which makes ggplot2 easier to use. For quick plots we’ll use base R functions, and when we get to more important things we’ll use ggplot2 and ggpubr. "],["R-objects.html", "Chapter 9 Intro to R objects 9.1 Commands used 9.2 R Objects 9.3 Differences between objects 9.4 The Data 9.5 The assignment operator “<-” makes object 9.6 Debrief", " Chapter 9 Intro to R objects By: Nathan Brouwer 9.1 Commands used <- c() length() dim() is() 9.2 R Objects Everything in R is an object, works with an object, tells you about an object, etc We’ll do a simple data analysis with a t.test and then look at properties of R objects There are several types of objects: vectors, matrices, lists, dataframes R objects can hold numbers, text, or both A typical dataframe has columns of numeric data and columns of text that represent factor variables (aka “categorical variables”) 9.3 Differences between objects Different objects are used and show up in different contexts. Most practical stats work in R is done with dataframes . A dataframe is kind of like a spreadsheet, loaded into R. For the sake of simplicity, we often load data in as a vector. This just makes things smoother when we are starting out. vectors pop up in many places, usually in a support role until you start doing more programming. matrices are occasionally used for applied stats stuff but show up more for programming. A matrix is like a stripped-down dataframe. lists show up everywhere, but you often don’t know it; many R functions make lists Understanding lists will help you efficiently work with stats output and make plots. 9.4 The Data We’ll use the following data to explore R objects. Motulsky 2nd Ed, Chapter 30, page 220, Table 30.1. Maximal relaxation of muscle strips of old and young rat bladders stimulated w/ high concentrations of nonrepinephrine (Frazier et al 2006). Response variable is %E.max 9.5 The assignment operator “<-” makes object The code above has made two objects. We can use several commands to learn about these objects. is(): what an object is, i.e., vector, matrix, list, dataframe length():how long an object is; only works with vectors and lists, not dataframes! dim(): how long AND how wide and object is; doesn’t work with vectors, only dataframes and matrices :( 9.5.1 is() What is our “old.E.max” object? is(old.E.max) ## [1] "numeric" "vector" is(young.E.max) ## [1] "numeric" "vector" Its a vector, containing numeric data. What if we made a vector like this? cat.variables <- c("old","old","old","old", "old","old","old","old","old") And used is() is(cat.variables) ## [1] "character" "vector" "data.frameRowLabels" ## [4] "SuperClassMethod" It tells us we have a vector, containing character data. Not sure why it feels the need to tell us all the other stuff… 9.5.2 length() Our vector has 9 elements, or is 9 elements long. length(old.E.max) ## [1] 9 Note that dim(), for dimension, doesn’t work with vectors! dim(old.E.max) ## NULL It would be nice if it said something like “1 x 9” for 1 row tall and 9 elements long. So it goes. 9.5.3 str() str() stands for “structure”. It summarizes info about an object; I find it most useful for looking at lists. If our vector here was really really long, str() would only show the first part of the vector str(old.E.max) ## num [1:9] 20.8 2.8 50 33.3 29.4 38.9 29.4 52.6 14.3 9.5.4 c() We typically use c() to gather together things like numbers, as we did to make our objects above. note: this is lower case “c”! Uppercase is something else For me, R’s font makes it hard sometimes to tell the difference between “c” and “C” If code isn’t working, one problem might be a “C” instead of a “c” Use c() to combine two objects old.plus.new <- c(old.E.max, young.E.max) Look at the length length(old.plus.new) ## [1] 17 Note that str() just shows us the first few digits, not all 17 str(old.plus.new) ## num [1:17] 20.8 2.8 50 33.3 29.4 38.9 29.4 52.6 14.3 45.5 ... 9.6 Debrief We can… learn about objects using length(), is(), str() access parts of list using $ (and also brackets) access parts of vectors using square brackets [ ] save the output of a model / test to an object access part of lists for plotting instead of copying stuff "],["nucleic-acids-review.html", "Chapter 10 Nucleic Acids 10.1 DNA and RNA 10.2 DNA Double-Helix Structure 10.3 RNA 10.4 Summary 10.5 Analysis Questions 10.6 Glossary 10.7 Contributors and Attributions", " Chapter 10 Nucleic Acids Authors: OpenStax / Libretext Formatted in RMarkdown by Nathan Brouwer under the Creative Commons Attribution License 4.0 license. This chapter was adapted from LibreText General Biology, Chapter 3, Section 3.5: Nucleic Acids. The LibreText book is based on OpenStax Biology 2nd edition, Chapter 3, Section 3.5: Nucleic Acids. A full list of authors is found under the Contributors and Attributions section at the end of this document. Nucleic acids are the most important macromolecules for the continuity of life. They carry the genetic blueprint of a cell and carry instructions for the functioning of the cell. 10.1 DNA and RNA The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material found in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is found in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes, the DNA is not enclosed in a membranous envelope. The entire genetic content of a cell is known as its genome, and the study of genomes is genomics. In eukaryotic cells but not in prokaryotes, DNA forms a complex with histone proteins to form chromatin, the substance of eukaryotic chromosomes. A chromosome may contain tens of thousands of genes. Many genes contain the information to make protein products; other genes code for RNA products. DNA controls all of the cellular activities by turning the genes “on” or “off.” The other type of nucleic acid, RNA, is mostly involved in protein synthesis. The DNA molecules never leave the nucleus but instead use an intermediary to communicate with the rest of the cell. This intermediary is the messenger RNA (mRNA). Other types of RNA—like rRNA, tRNA, and microRNA—are involved in protein synthesis and its regulation. DNA and RNA are made up of monomers known as nucleotides. The nucleotides combine with each other to form a polynucleotide, DNA or RNA. Each nucleotide is made up of three components: a nitrogenous base, a pentose (five-carbon) sugar, and a phosphate group (Figure 3.5.1). Each nitrogenous base in a nucleotide is attached to a sugar molecule, which is attached to one or more phosphate groups. Figure 10.1: A nucleotide is made up of three components: a nitrogenous base, a pentose sugar, and one or more phosphate groups. Carbon residues in the pentose are numbered 1′ through 5′ (the prime distinguishes these residues from those in the base, which are numbered without using a prime notation). The base is attached to the 1′ position of the ribose, and the phosphate is attached to the 5′ position. When a polynucleotide is formed, the 5′ phosphate of the incoming nucleotide attaches to the 3′ hydroxyl group at the end of the growing chain. Two types of pentose are found in nucleotides, deoxyribose (found in DNA) and ribose (found in RNA). Deoxyribose is similar in structure to ribose, but it has an H instead of an OH at the 2′ position. Bases can be divided into two categories: purines and pyrimidines. Purines have a double ring structure, and pyrimidines have a single ring. The nitrogenous bases, important components of nucleotides, are organic molecules and are so named because they contain carbon and nitrogen. They are bases because they contain an amino group that has the potential of binding an extra hydrogen, and thus, decreases the hydrogen ion concentration in its environment, making it more basic. Each nucleotide in DNA contains one of four possible nitrogenous bases: adenine (A), guanine (G) cytosine (C), and thymine (T). Adenine and guanine are classified as purines. The primary structure of a purine is two carbon-nitrogen rings. Cytosine, thymine, and uracil are classified as pyrimidines which have a single carbon-nitrogen ring as their primary structure (Figure 3.5.1). Each of these basic carbon-nitrogen rings has different functional groups attached to it. In molecular biology shorthand, the nitrogenous bases are simply known by their symbols A, T, G, C, and U. DNA contains A, T, G, and C whereas RNA contains A, U, G, and C. The pentose sugar in DNA is deoxyribose, and in RNA, the sugar is ribose (Figure 3.5.1). The difference between the sugars is the presence of the hydroxyl group on the second carbon of the ribose and hydrogen on the second carbon of the deoxyribose. The carbon atoms of the sugar molecule are numbered as 1′, 2′, 3′, 4′, and 5′ (1′ is read as “one prime”). The phosphate residue is attached to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms a 5′–3′ phosphodiester linkage. The phosphodiester linkage is not formed by simple dehydration reaction like the other linkages connecting monomers in macromolecules: its formation involves the removal of two phosphate groups. A polynucleotide may have thousands of such phosphodiester linkages. 10.2 DNA Double-Helix Structure DNA has a double-helix structure (Figure 3.5.2). The sugar and phosphate lie on the outside of the helix, forming the backbone of the DNA. The nitrogenous bases are stacked in the interior, like the steps of a staircase, in pairs; the pairs are bound to each other by hydrogen bonds. Every base pair in the double helix is separated from the next base pair by 0.34 nm. The two strands of the helix run in opposite directions, meaning that the 5′ carbon end of one strand will face the 3′ carbon end of its matching strand. (This is referred to as antiparallel orientation and is important to DNA replication and in many nucleic acid interactions.) Figure 10.2: Figure 3.5.2 : Native DNA is an antiparallel double helix. The phosphate backbone (indicated by the curvy lines) is on the outside, and the bases are on the inside. Each base from one strand interacts via hydrogen bonding with a base from the opposing strand. (credit: Jerome Walker/Dennis Myts) Only certain types of base pairing are allowed. For example, a certain purine can only pair with a certain pyrimidine. This means A can pair with T, and G can pair with C, as shown in Figure 3.5.3. This is known as the base complementary rule. In other words, the DNA strands are complementary to each other. If the sequence of one strand is AATTGGCC, the complementary strand would have the sequence TTAACCGG. During DNA replication, each strand is copied, resulting in a daughter DNA double helix containing one parental DNA strand and a newly synthesized strand. Art Connection Figure 3.5.3 : In a double stranded DNA molecule, the two strands run antiparallel to one another so that one strand runs 5′ to 3′ and the other 3′ to 5′. The phosphate backbone is located on the outside, and the bases are in the middle. Adenine forms hydrogen bonds (or base pairs) with thymine, and guanine base pairs with cytosine. A mutation occurs, and cytosine is replaced with adenine. What impact do you think this will have on the DNA structure? 10.3 RNA Ribonucleic acid, or RNA, is mainly involved in the process of protein synthesis under the direction of DNA. RNA is usually single-stranded and is made of ribonucleotides that are linked by phosphodiester bonds. A ribonucleotide in the RNA chain contains ribose (the pentose sugar), one of the four nitrogenous bases (A, U, G, and C), and the phosphate group. There are four major types of RNA: messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and microRNA (miRNA). The first, mRNA, carries the message from DNA, which controls all of the cellular activities in a cell. If a cell requires a certain protein to be synthesized, the gene for this product is turned “on” and the messenger RNA is synthesized in the nucleus. The RNA base sequence is complementary to the coding sequence of the DNA from which it has been copied. However, in RNA, the base T is absent and U is present instead. If the DNA strand has a sequence AATTGCGC, the sequence of the complementary RNA is UUAACGCG. In the cytoplasm, the mRNA interacts with ribosomes and other cellular machinery (Figure 3.5.4). Figure 3.5.4 : A ribosome has two parts: a large subunit and a small subunit. The mRNA sits in between the two subunits. A tRNA molecule recognizes a codon on the mRNA, binds to it by complementary base pairing, and adds the correct amino acid to the growing peptide chain. The mRNA is read in sets of three bases known as codons. Each codon codes for a single amino acid. In this way, the mRNA is read and the protein product is made. Ribosomal RNA (rRNA) is a major constituent of ribosomes on which the mRNA binds. The rRNA ensures the proper alignment of the mRNA and the ribosomes; the rRNA of the ribosome also has an enzymatic activity (peptidyl transferase) and catalyzes the formation of the peptide bonds between two aligned amino acids. Transfer RNA (tRNA) is one of the smallest of the four types of RNA, usually 70–90 nucleotides long. It carries the correct amino acid to the site of protein synthesis. It is the base pairing between the tRNA and mRNA that allows for the correct amino acid to be inserted in the polypeptide chain. microRNAs are the smallest RNA molecules and their role involves the regulation of gene expression by interfering with the expression of certain mRNA messages. Table 3.5.1 below summarizes features of DNA and RNA. Table 3.5.1 *: Features of DNA and RNA. Features of DNA and RNA DNA RNA Function Carries genetic information Involved in protein synthesis Location Remains in the nucleus Leaves the nucleus Structure Double helix Usually single-stranded Sugar Deoxyribose Ribose Pyrimidines Cytosine, thymine Cytosine, uracil Purines Adenine, guanine Adenine, guanine Even though the RNA is single stranded, most RNA types show extensive intramolecular base pairing between complementary sequences, creating a predictable three-dimensional structure essential for their function. As you have learned, information flow in an organism takes place from DNA to RNA to protein. DNA dictates the structure of mRNA in a process known as transcription, and RNA dictates the structure of protein in a process known as translation. This is known as the Central Dogma of Life, which holds true for all organisms; however, exceptions to the rule occur in connection with viral infections. Link to Learning To learn more about DNA, explore the Howard Hughes Medical Institute BioInteractive animations on the topic of DNA.* 10.4 Summary Nucleic acids are molecules made up of nucleotides that direct cellular activities such as cell division and protein synthesis. Each nucleotide is made up of a pentose sugar, a nitrogenous base, and a phosphate group. There are two types of nucleic acids: DNA and RNA. DNA carries the genetic blueprint of the cell and is passed on from parents to offspring (in the form of chromosomes). It has a double-helical structure with the two strands running in opposite directions, connected by hydrogen bonds, and complementary to each other. RNA is single-stranded and is made of a pentose sugar (ribose), a nitrogenous base, and a phosphate group. RNA is involved in protein synthesis and its regulation. Messenger RNA (mRNA) is copied from the DNA, is exported from the nucleus to the cytoplasm, and contains information for the construction of proteins. Ribosomal RNA (rRNA) is a part of the ribosomes at the site of protein synthesis, whereas transfer RNA (tRNA) carries the amino acid to the site of protein synthesis. microRNA regulates the use of mRNA for protein synthesis. Art Connections 10.5 Analysis Questions Free Response 10.6 Glossary deoxyribonucleic acid (DNA): double-helical molecule that carries the hereditary information of the cell messenger RNA (mRNA): DNA that carries information from DNA to ribosomes during protein synthesis nucleic acid: biological macromolecule that carries the genetic blueprint of a cell and carries instructions for the functioning of the cell nucleotide: monomer of nucleic acids; contains a pentose sugar, one or more phosphate groups, and a nitrogenous base phosphodiester: linkage covalent chemical bond that holds together the polynucleotide chains with a phosphate group linking two pentose sugars of neighboring nucleotides polynucleotide: long chain of nucleotides purine: type of nitrogenous base in DNA and RNA; adenine and guanine are purines, pyrimidine: type of nitrogenous base in DNA and RNA; cytosine, thymine, and uracil are pyrimidines ribonucleic acid (RNA): single-stranded, often internally base paired, molecule that is involved in protein synthesis ribosomal RNA (rRNA): RNA that ensures the proper alignment of the mRNA and the ribosomes during protein synthesis and catalyzes the formation of the peptide linkage transcription: process through which messenger RNA forms on a template of DNA transfer RNA (tRNA): RNA that carries activated amino acids to the site of protein synthesis on the ribosome translation: process through which RNA directs the formation of protein 10.7 Contributors and Attributions Connie Rye (East Mississippi Community College), Robert Wise (University of Wisconsin, Oshkosh), Vladimir Jurukovski (Suffolk County Community College), Jean DeSaix (University of North Carolina at Chapel Hill), Jung Choi (Georgia Institute of Technology), Yael Avissar (Rhode Island College) among other contributing authors. Original content by OpenStax (CC BY 4.0; Download for free at http://cnx.org/contents/185cbf87-c72...f21b5eabd@9.87). "],["proteins-review.html", "Chapter 11 Proteins 11.1 Types and Functions of Proteins 11.2 Amino Acids 11.3 Evolution Connection: 11.4 Protein Structure 11.5 Denaturation and Protein Folding 11.6 Summary 11.7 Art Connections 11.8 Review Questions 11.9 Free Response 11.10 Glossary 11.11 Contributors and Attributions", " Chapter 11 Proteins Authors: OpenStax / LibreText. Formatted in RMarkdown by Nathan Brouwer under the Creative Commons Attribution License 4.0 license. This chapter was adapted from LibreText General Biology, Chapter 3, Section 3.4: Proteins. The LibreText book is based on OpenStax Biology 2nd edition, Chapter 3, Section 3.4: Nucleic Acids. A full list of authors is found under the Contributors and Attributions section at the end of this document. 11.0.1 Skills to develop: Describe the functions proteins perform in the cell and in tissues Discuss the relationship between amino acids and proteins Explain the four levels of protein organization Describe the ways in which protein shape and function are linked Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective; they may serve in transport, storage, or membranes; or they may be toxins or enzymes. Each cell in a living system may contain thousands of proteins, each with a unique function. Their structures, like their functions, vary greatly. They are all, however, polymers of amino acids, arranged in a linear sequence. 11.1 Types and Functions of Proteins Enzymes, which are produced by living cells, are catalysts in biochemical reactions (like digestion) and are usually complex or conjugated proteins. Each enzyme is specific for the substrate (a reactant that binds to an enzyme) it acts on. The enzyme may help in breakdown, rearrangement, or synthesis reactions. Enzymes that break down their substrates are called catabolic enzymes, enzymes that build more complex molecules from their substrates are called anabolic enzymes, and enzymes that affect the rate of reaction are called catalytic enzymes. It should be noted that all enzymes increase the rate of reaction and, therefore, are considered to be organic catalysts. An example of an enzyme is salivary amylase, which hydrolyzes its substrate amylose, a component of starch. Hormones are chemical-signaling molecules, usually small proteins or steroids, secreted by endocrine cells that act to control or regulate specific physiological processes, including growth, development, metabolism, and reproduction. For example, insulin is a protein hormone that helps to regulate the blood glucose level. Proteins have different shapes and molecular weights; some proteins are globular in shape whereas others are fibrous. For example, hemoglobin is a globular protein, but collagen, found in our skin, is a fibrous protein. Protein shape is critical to its function, and this shape is maintained by many different types of chemical bonds. Changes in temperature, pH, and exposure to chemicals may lead to permanent changes in the shape of the protein, leading to loss of function, known as denaturation. All proteins are made up of different arrangements of the same 20 types of amino acids. 11.2 Amino Acids Amino acids are the monomers that make up proteins. Each amino acid has the same fundamental structure, which consists of a central carbon atom, also known as the alpha (α) carbon, bonded to an amino group (NH2), a carboxyl group (COOH), and to a hydrogen atom. Every amino acid also has another atom or group of atoms bonded to the central atom known as the R group (Figure 3.4.1). Figure 11.1: Amino acids have a central asymmetric carbon to which an amino group, a carboxyl group, a hydrogen atom, and a side chain (R group) are attached. The name “amino acid” is derived from the fact that they contain both an amino group and carboxyl-acid-group in their basic structure. For each amino acid, the R group (or side chain) is different (Figure 3.4.2). Figure 11.2: There are 20 common amino acids commonly found in proteins, each with a different R group (variant group) that determines its chemical nature. Which categories of amino acid would you expect to find on the surface of a soluble protein, and which would you expect to find in the interior? What distribution of amino acids would you expect to find in a protein embedded in a lipid bilayer? The chemical nature of the side chain determines the nature of the amino acid (that is, whether it is acidic, basic, polar, or nonpolar). For example, the amino acid glycine has a hydrogen atom as the R group. Amino acids such as valine, methionine, and alanine are nonpolar or hydrophobic, while amino acids such as serine, threonine, and cysteine are polar and have hydrophilic side chains. The side chains of lysine and arginine are positively charged, and therefore these amino acids are also known as basic amino acids. Proline has an R group that is linked to the amino group, forming a ring-like structure. Proline is an exception to the standard structure of an amino acid since its amino group is not separate from the side chain Amino acids are represented by a single upper case letter or a three-letter abbreviation. For example, valine is known by the letter V or the three-letter code val. The sequence and the number of amino acids ultimately determine a protein’s shape, size, and function. Each amino acid is attached to another amino acid by a covalent bond, known as a peptide bond, which is formed by a dehydration reaction. The carboxyl group of one amino acid and the amino group of the incoming amino acid combine, releasing a molecule of water. The resulting bond is the peptide bond Figure 11.3: Peptide bond formation is a dehydration synthesis reaction. The carboxyl group of one amino acid is linked to the amino group of the incoming amino acid. In the process, a molecule of water is released. The products formed by such linkages are called peptides. As more amino acids join to this growing chain, the resulting chain is known as a polypeptide. Each polypeptide has a free amino group at one end. This end is called the N-terminal, or the amino terminal, and the other end has a free carboxyl group, also known as the C- or carboxyl terminal. The terms polypeptide and protein are sometimes used interchangeably, a polypeptide is technically a polymer of amino acids, whereas the term protein is more formally used for a polypeptide or that have combined together, often have bound non-peptide prosthetic groups, have a distinct shape, and have a unique function. After protein synthesis (translation), most proteins are modified. These are known as post-translational modifications. They may undergo cleavage, phosphorylation (addition of a phosphate group), or may require the addition of other chemical groups. Only after these modifications is the protein completely functional. 11.3 Evolution Connection: The Evolutionary Significance of Cytochrome: Cytochrome c is an important component of the electron transport chain, a part of cellular respiration, and it is normally found in the cellular organelle, the mitochondrion. This protein has a heme prosthetic group, and the central ion of the heme gets alternately reduced and oxidized during electron transfer. Because this essential protein’s role in producing cellular energy is crucial, it has changed very little over millions of years. Protein sequencing has shown that there is a considerable amount of cytochrome c amino acid sequence homology among different species; in other words, evolutionary kinship can be assessed by measuring the similarities or differences among various species’ DNA or protein sequences. Scientists have determined that human cytochrome c contains 104 amino acids. For each cytochrome c molecule from different organisms that has been sequenced to date, 37 of these amino acids appear in the same position in all samples of cytochrome c. This indicates that there may have been a common ancestor. On comparing the human and chimpanzee protein sequences, no sequence difference was found. When human and rhesus monkey sequences were compared, the single difference found was in one amino acid. In another comparison, human to yeast sequencing shows a difference in the 44th position. 11.4 Protein Structure The shape of a protein is critical to its function. For example, an enzyme can bind to a specific substrate at a site known as the active site. If this active site is altered because of local changes or changes in overall protein structure, the enzyme may be unable to bind to the substrate. To understand how the protein gets its final shape or conformation, we need to understand the four levels of protein structure: primary, secondary, tertiary, and quaternary. 11.4.1 Primary Structure The unique sequence of amino acids in a polypeptide chain is its primary structure. For example, the pancreatic hormone insulin has two polypeptide chains, A and B, and they are linked together by disulfide bonds. The N terminal amino acid of the A chain is glycine, whereas the C terminal amino acid is asparagine (Figure 3.4.4). The sequences of amino acids in the A and B chains are unique to insulin. Figure 11.4: Bovine serum insulin is a protein hormone made of two peptide chains, A (21 amino acids long) and B (30 amino acids long). In each chain, primary structure is indicated by three-letter abbreviations that represent the names of the amino acids in the order they are present. The amino acid cysteine (cys) has a sulfhydryl (SH) group as a side chain. Two sulfhydryl groups can react in the presence of oxygen to form a disulfide (S-S) bond. Two disulfide bonds connect the A and B chains together, and a third helps the A chain fold into the correct shape. Note that all disulfide bonds are the same length, but are drawn different sizes for clarity. The unique sequence for every protein is ultimately determined by the gene encoding the protein. A change in nucleotide sequence of the gene’s coding region may lead to a different amino acid being added to the growing polypeptide chain, causing a change in protein structure and function. In sickle cell anemia, the hemoglobin beta chain (a small portion of which is shown in Figure 3.4.5) has a single amino acid substitution, causing a change in protein structure and function. Specifically, the amino acid glutamic acid is substituted by valine in the beta chain. What is most remarkable to consider is that a hemoglobin molecule is made up of two alpha chains and two beta chains that each consist of about 150 amino acids. The molecule, therefore, has about 600 amino acids. The structural difference between a normal hemoglobin molecule and a sickle cell molecule—which dramatically decreases life expectancy—is a single amino acid of the 600. What is even more remarkable is that those 600 amino acids are encoded by three nucleotides each, and the mutation is caused by a single base change (point mutation), 1 in 1800 bases. Figure 11.5: The beta chain of hemoglobin is 147 residues in length, yet a single amino acid substitution leads to sickle cell anemia. In normal hemoglobin, the amino acid at position seven is glutamate. In sickle cell hemoglobin, this glutamate is replaced by a valine. Because of this change of one amino acid in the chain, hemoglobin molecules form long fibers that distort the biconcave, or disc-shaped, red blood cells and assume a crescent or “sickle” shape, which clogs arteries (Figure 3.4.6). This can lead to myriad serious health problems such as breathlessness, dizziness, headaches, and abdominal pain for those affected by this disease. Figure 11.6: In this blood smear, visualized at 535x magnification using bright field microscopy, sickle cells are crescent shaped, while normal cells are disc-shaped. (credit: modification of work by Ed Uthman; scale-bar data from Matt Russell) 11.4.2 Secondary Structure The local folding of the polypeptide in some regions gives rise to the secondary structure of the protein. The most common are the α-helix and beta-pleated sheet structures (Figure 3.4.7). Both structures are the α-helix structure—the helix held in shape by hydrogen bonds. The hydrogen bonds form between the oxygen atom in the carbonyl group in one amino acid and another amino acid that is four amino acids farther along the chain. Figure 11.7: The α-helix and β-pleated sheet are secondary structures of proteins that form because of hydrogen bonding between carbonyl and amino groups in the peptide backbone. Certain amino acids have a propensity to form an α-helix, while others have a propensity to form a β-pleated sheet. Every helical turn in an alpha helix has 3.6 amino acid residues. The R groups (the variant groups) of the polypeptide protrude out from the α-helix chain. In the beta-pleated sheet, the “pleats” are formed by hydrogen bonding between atoms on the backbone of the polypeptide chain. The R groups are attached to the carbons and extend above and below the folds of the pleat. The pleated segments align parallel or antiparallel to each other, and hydrogen bonds form between the partially positive nitrogen atom in the amino group and the partially negative oxygen atom in the carbonyl group of the peptide backbone. The α-helix and beta-pleated sheet structures are found in most globular and fibrous proteins and they play an important structural role. 11.4.3 Tertiary Structure The unique three-dimensional structure of a polypeptide is its tertiary structure (Figure 3.4.8). This structure is in part due to chemical interactions at work on the polypeptide chain. Primarily, the interactions among R groups creates the complex three- dimensional tertiary structure of a protein. The nature of the R groups found in the amino acids involved can counteract the formation of the hydrogen bonds described for standard secondary structures. For example, R groups with like charges are repelled by each other and those with unlike charges are attracted to each other (ionic bonds). When protein folding takes place, the hydrophobic R groups of nonpolar amino acids lay in the interior of the protein, whereas the hydrophilic R groups lay on the outside. The former types of interactions are also known as hydrophobic interactions. Interaction between cysteine side chains forms disulfide linkages in the presence of oxygen, the only covalent bond forming during protein folding. Figure 11.8: The tertiary structure of proteins is determined by a variety of chemical interactions. These include hydrophobic interactions, ionic bonding, hydrogen bonding and disulfide linkages. All of these interactions, weak and strong, determine the final three-dimensional shape of the protein. When a protein loses its three-dimensional shape, it may no longer be functional. 11.4.4 Quaternary Structure In nature, some proteins are formed from several polypeptides, also known as subunits, and the interaction of these subunits forms the quaternary structure. Weak interactions between the subunits help to stabilize the overall structure. For example, insulin (a globular protein) has a combination of hydrogen bonds and disulfide bonds that cause it to be mostly clumped into a ball shape. Insulin starts out as a single polypeptide and loses some internal sequences in the presence of post-translational modification after the formation of the disulfide linkages that hold the remaining chains together. Silk (a fibrous protein), however, has a beta-pleated sheet structure that is the result of hydrogen bonding between different chains. The four levels of protein structure (primary, secondary, tertiary, and quaternary) are illustrated in Figure 3.4.9. Figure 11.9: The four levels of protein structure can be observed in these illustrations. (credit: modification of work by National Human Genome Research Institute) 11.5 Denaturation and Protein Folding Each protein has its own unique sequence and shape that are held together by chemical interactions. If the protein is subject to changes in temperature, pH, or exposure to chemicals, the protein structure may change, losing its shape without losing its primary sequence in what is known as denaturation. Denaturation is often reversible because the primary structure of the polypeptide is conserved in the process if the denaturing agent is removed, allowing the protein to resume its function. Sometimes denaturation is irreversible, leading to loss of function. One example of irreversible protein denaturation is when an egg is fried. The albumin protein in the liquid egg white is denatured when placed in a hot pan. Not all proteins are denatured at high temperatures; for instance, bacteria that survive in hot springs have proteins that function at temperatures close to boiling. The stomach is also very acidic, has a low pH, and denatures proteins as part of the digestion process; however, the digestive enzymes of the stomach retain their activity under these conditions. Protein folding is critical to its function. It was originally thought that the proteins themselves were responsible for the folding process. Only recently was it found that often they receive assistance in the folding process from protein helpers known as chaperones (or chaperonins) that associate with the target protein during the folding process. They act by preventing aggregation of polypeptides that make up the complete protein structure, and they disassociate from the protein once the target protein is folded. 11.6 Summary Proteins are a class of macromolecules that perform a diverse range of functions for the cell. They help in metabolism by providing structural support and by acting as enzymes, carriers, or hormones. The building blocks of proteins (monomers) are amino acids. Each amino acid has a central carbon that is linked to an amino group, a carboxyl group, a hydrogen atom, and an R group or side chain. There are 20 commonly occurring amino acids, each of which differs in the R group. Each amino acid is linked to its neighbors by a peptide bond. A long chain of amino acids is known as a polypeptide. Proteins are organized at four levels: primary, secondary, tertiary, and (optional) quaternary. The primary structure is the unique sequence of amino acids. The local folding of the polypeptide to form structures such as the α helix and beta-pleated sheet constitutes the secondary structure. The overall three-dimensional structure is the tertiary structure. When two or more polypeptides combine to form the complete protein structure, the configuration is known as the quaternary structure of a protein. Protein shape and function are intricately linked; any change in shape caused by changes in temperature or pH may lead to protein denaturation and a loss in function. 11.7 Art Connections Which categories of amino acid would you expect to find on the surface of a soluble protein, and which would you expect to find in the interior? What distribution of amino acids would you expect to find in a protein embedded in a lipid bilayer? Polar and charged amino acid residues (the remainder after peptide bond formation) are more likely to be found on the surface of soluble proteins where they can interact with water, and nonpolar (e.g., amino acid side chains) are more likely to be found in the interior where they are sequestered from water. In membrane proteins, nonpolar and hydrophobic amino acid side chains associate with the hydrophobic tails of phospholipids, while polar and charged amino acid side chains interact with the polar head groups or with the aqueous solution. However, there are exceptions. Sometimes, positively and negatively charged amino acid side chains interact with one another in the interior of a protein, and polar or charged amino acid side chains that interact with a ligand can be found in the ligand binding pocket. 11.8 Review Questions The monomers that make up proteins are called 1. nucleotides 1. disaccharides 1. amino acids 1. chaperones Answer: C The α helix and the beta-pleated sheet are part of which protein structure? primary secondary tertiary quaternary Answer: B 11.9 Free Response Explain what happens if even one amino acid is substituted for another in a polypeptide chain. Provide a specific example. A change in gene sequence can lead to a different amino acid being added to a polypeptide chain instead of the normal one. This causes a change in protein structure and function. For example, in sickle cell anemia, the hemoglobin beta chain has a single amino acid substitution—the amino acid glutamic acid in position six is substituted by valine. Because of this change, hemoglobin molecules form aggregates, and the disc-shaped red blood cells assume a crescent shape, which results in serious health problems. Describe the differences in the four protein structures. The sequence and number of amino acids in a polypeptide chain is its primary structure. The local folding of the polypeptide in some regions is the secondary structure of the protein. The three-dimensional structure of a polypeptide is known as its tertiary structure, created in part by chemical interactions such as hydrogen bonds between polar side chains, van der Waals interactions, disulfide linkages, and hydrophobic interactions. Some proteins are formed from multiple polypeptides, also known as subunits, and the interaction of these subunits forms the quaternary structure. 11.10 Glossary alpha-helix structure (α-helix): type of secondary structure of proteins formed by folding of the polypeptide into a helix shape with hydrogen bonds stabilizing the structure amino acid: monomer of a protein; has a central carbon or alpha carbon to which an amino group, a carboxyl group, a hydrogen, and an R group or side chain is attached; the R group is different for all 20 amino acids beta-pleated sheet (beta-pleated): secondary structure found in proteins in which “pleats” are formed by hydrogen bonding between atoms on the backbone of the polypeptide chain chaperone: (also, chaperonin) protein that helps nascent protein in the folding process denaturation: loss of shape in a protein as a result of changes in temperature, pH, or exposure to chemicals enzyme: catalyst in a biochemical reaction that is usually a complex or conjugated protein hormone: chemical signaling molecule, usually protein or steroid, secreted by endocrine cells that act to control or regulate specific physiological processes peptide bond: bond formed between two amino acids by a dehydration reaction polypeptide: long chain of amino acids linked by peptide bonds primary structure: linear sequence of amino acids in a protein protein: biological macromolecule composed of one or more chains of amino acids quaternary structure: association of discrete polypeptide subunits in a protein secondary structure: regular structure formed by proteins by intramolecular hydrogen bonding between the oxygen atom of one amino acid residue and the hydrogen attached to the nitrogen atom of another amino acid residue tertiary structure: three-dimensional conformation of a protein, including interactions between secondary structural elements; formed from interactions between amino acid side chains 11.11 Contributors and Attributions Connie Rye (East Mississippi Community College), Robert Wise (University of Wisconsin, Oshkosh), Vladimir Jurukovski (Suffolk County Community College), Jean DeSaix (University of North Carolina at Chapel Hill), Jung Choi (Georgia Institute of Technology), Yael Avissar (Rhode Island College) among other contributing authors. Original content by OpenStax (CC BY 4.0; Download for free at http://cnx.org/contents/185cbf87-c72...f21b5eabd@9.87). "],["ncbi-the-national-center-for-biotechnology-information.html", "Chapter 12 NCBI: The National Center for Biotechnology Information 12.1 Key concepts 12.2 NCBI 12.3 GenBank sequence database 12.4 PubMed and PubMed Central article database 12.5 Entrez 12.6 BLAST", " Chapter 12 NCBI: The National Center for Biotechnology Information NOTE: The following material was partially adapted by N. Brouwer from Wikipedia. See the underlying .Rmd file for information on specific paragraphs from Wikipedia. 12.1 Key concepts NCBI Rentrez accession numbers BLAST 12.2 NCBI The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It was founded in 1988 and is approved and funded by the government of the United States. The NCBI houses a series of databases relevant to the basic and applied life sciences and is an important resource for bioinformatics tools and services. Major databases include GenBank for DNA sequences and PubMed, a bibliographic database for biomedical literature. All these databases are available online through the Entrez search engine. In this chapter we’ll briefly discuss the major databases, following up with specific details in subsequent chapters. One thing to note is that in practice people do no always adhere to the specific names of databases and other tools. For example, someone may say “I searched NCBI for sequences.” It would be more accurate to say “I used Entrez to search GenBank for sequences.” For in-depth details on all the features see Database resources of the National Center for Biotechnology Information 12.3 GenBank sequence database The GenBank sequence database is an open access collection of publicly available DNA and protein sequences. If you’ve work with sequence data, you’ll work with GenBank. GenBank is the actual database, and it can be searched several ways. For example, you can search for a sequence by its ID number (accession number) if you know it, or do a BLAST search using an actual sequence to look for similar sequences. A key component of GenBank are the GeneBank Records, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene pallysin https://www.ncbi.nlm.nih.gov/protein/AAC65720 from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides metadata, such as the scientific name of the organism (Treponema pallidum), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene. A key feature of PubMed records is that they are hyperlinked to other NCBI databases. For example, you can click link under the name of the paper which reported the sequence of the gene and it will take you to the PubMed record for that paper (see below). You can also click the “Run BLAST” link and you can search the database for similar sequences. This protein coded for by this particular gene has had its structured solved using x-ray crystallography, and you can see these results under “Protein 3D Structure.” In a later chapter we’ll get to know these records in further detail. 12.4 PubMed and PubMed Central article database PubMed and PubMed Central (PMC) are databases of scientific articles related to data contained in NCBI databases. PubMed contains basic bibliographic information, the abstract, relevant links to sequences, and to the websites of the actual publishers of the papers. If the text of an article is open-access, PMC should have a copy of it. Articles in PMC contain relevant hyperlinks, such as to any sequences that are mentioned. For example, the image below show where the sequence of Tp0751 is mentioned in a paper and linked to the GenBank record we looked at above. 12.5 Entrez Entrez is a search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name “Entrez” (a greeting meaning “Come in” in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI. Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system. 12.6 BLAST BLAST (Basic Local Alignment Search Tool) is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. "],["introduction-to-biological-sequences-databases.html", "Chapter 13 Introduction to biological sequences databases 13.1 Topics 13.2 Introduction 13.3 Biological sequence databases 13.4 The NCBI Sequence Database 13.5 The NCBI Sub-Databases 13.6 NCBI GenBank Record Format 13.7 The FASTA file format 13.8 RefSeq 13.9 Querying the NCBI Database 13.10 Querying the NCBI Database via the NCBI Website (for reference) 13.11 Example: finding the sequences published in Nature 460:352-358 (for reference)", " Chapter 13 Introduction to biological sequences databases library(compbio4all) By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). 13.1 Topics NCBI vs. EMBL vs. DDBJ annotation Nucleotide vs. Protein vs. EST vs. Genome PubMed GenBank Records FASTA file format FASTA header line RefSeq refining searches 13.2 Introduction NCBI is the National Center for Biotechnology Information. The NCBI Webiste www.ncbi.nlm.nih.gov/ is the entry point to a large number of databases giving access to biological sequences (DNA, RNA, protein) and biology-related publications. When scientists sequence DNA, RNA and proteins they typically publish their data via databases with the NCBI. Each is given a unique identification number known as an accession number. For example, each time a unique human genome sequence is produced it is uploaded to the relevant databases, assigned a unique accession, and a website created to access it. Sequence are also cross-referenced to related papers, so you can start with a sequence and find out what scientific paper it was used in, or start with a paper and see if any sequences are associated with it. This chapter provides an introduction to the general search features of the NCBI databases via the interface on the website, including how to locate sequences using accession numbers and other search parameters, such as specific authors or papers. Subsequent chapters will introduce advanced search features, and how to carry out searches using R commands. One consequence of the explosion of biological sequences used in publications is that the system of databases has become fairly complex. There are databases for different types of data, different types of molecules, data from individual experiments showing genetic variation and also the consensus sequence for a given molecule. Luckily, if you know the accession number of the sequence you are looking for – which will be our starting point throughout this book – its fairly straight forward. There are numerous other books on bioinformatics and genomics that provide all details if you need to do more complex searches. In this chapter we’ll typically refer generically to “NCBI data” and “NCBI databases.” This is a simplification, since NCBI is the name of the organization and the databases and search engines often have specific names. 13.3 Biological sequence databases Almost published biological sequences are available online, as it is a requirement of every scientific journal that any published DNA or RNA or protein sequence must be deposited in a public database. The main resources for storing and distributing sequence data are three large databases: USA: NCBI database (www.ncbi.nlm.nih.gov/) Europe: European Molecular Biology Laboratory (EMBL) database (https://www.ebi.ac.uk/ena) Japan: DNA Database of Japan (DDBJ) database (www.ddbj.nig.ac.jp/). These databases collect all publicly available DNA, RNA and protein sequence data and make it available for free. They exchange data nightly, so contain essentially the same data. The redundancy among the databases allows them to serve different communities (e.g. native languages), provide different additional services such as tutorials, and assure that the world’s scientists have their data backed up in different physical locations – a key component of good data management! 13.4 The NCBI Sequence Database In this chapter we will explore the NCBI sequence database using the accession number NC_001477, which is for the complete DEN-1 Dengue virus genome sequence. The accession number is reported in scientific papers originally describing the sequence, and also in subsequent papers that use that particular sequence. In addition to the sequence itself, for each sequence the NCBI database also stores some additional annotation data, such as the name of the species it comes from, references to publications describing that sequence, information on the structure of the proteins coded by the sequence, etc. Some of this annotation data was added by the person who sequenced a sequence and submitted it to the NCBI database, while some may have been added later by a human curator working for NCBI. 13.5 The NCBI Sub-Databases The NCBI database contains several sub-databases, the most important of which are: Nucleotide database: contains DNA and RNA sequences Protein database: contains protein sequences EST database: contains ESTs (expressed sequence tags), which are short sequences derived from mRNAs. (This terminology is likely to be unfamiliar because it is not often used in introductory biology courses. The “Expressed” of EST comes from the fact that mRNA is the result of gene expression.) Genome database: contains the DNA sequences for entire genomes PubMed: contains data on scientific publications From the main NCBI website you can initiate a search and it will look for hits across all the databases. You can narrow your search by selecting a particular database. 13.6 NCBI GenBank Record Format As mentioned above, for each sequence the NCBI database stores some extra information such as the species that it came from, publications describing the sequence, etc. This information is stored in the GenBank entry (aka GenBank Record) for the sequence. The GenBank entry for a sequence can be viewed by searching the NCBI database for the accession number for that sequence. To view the GenBank entry for the DEN-1 Dengue virus, follow these steps: Go to the NCBI website (www.ncbi.nlm.nih.gov). Search for the accession number NC_001477. Since we searched for a particular accession we are only returned a single main result which is titled “NUCLEOTIDE SEQUENCE: Dengue virus 1, complete genome.” Click on “Dengue virus 1, complete genome” to go to the GenBank entry. The GenBank entry for an accession contains a LOT of information about the sequence, such as papers describing it, features in the sequence, etc. The DEFINITION field gives a short description for the sequence. The ORGANISM field in the NCBI entry identifies the species that the sequence came from. The REFERENCE field contains scientific publications describing the sequence. The FEATURES field contains information about the location of features of interest inside the sequence, such as regulatory sequences or genes that lie inside the sequence. The ORIGIN field gives the sequence itself. 13.7 The FASTA file format The FASTA file format is a simple file format commonly used to store and share sequence information. When you download sequences from databases such as NCBI you usually want FASTA files. The first line of a FASTA file starts with the “greater than” character (>) followed by a name and/or description for the sequence. Subsequent lines contain the sequence itself. A short FASTA file might contain just something like this: ## >mysequence1 ## ACATGAGACAGACAGACCCCCAGAGACAGACCCCTAGACACAGAGAGAG ## TATGCAGGACAGGGTTTTTGCCCAGGGTGGCAGTATG A FASTA file can contain the sequence for a single, an entire genome, or more than one sequence. If a FASTA file contains many sequences, then for each sequence it will have a header line starting with a greater than character followed by the sequence itself. This is what a FASTA file with two sequence looks like. ## >mysequence1 ## ACATGAGACAGACAGACCCCCAGAGACAGACCCCTAGACACAGAGAGAG ## TATGCAGGACAGGGTTTTTGCCCAGGGTGGCAGTATG ## ## >mysequence2 ## AGGATTGAGGTATGGGTATGTTCCCGATTGAGTAGCCAGTATGAGCCAG ## AGTTTTTTACAAGTATTTTTCCCAGTAGCCAGAGAGAGAGTCACCCAGT ## ACAGAGAGC 13.8 RefSeq When carrying out searches of the NCBI database, it is important to bear in mind that the database may contain redundant sequences for the same gene that were sequenced by different laboratories and experimental. This is because many different labs have sequenced the gene, and submitted their sequences to the NCBI database, and variation exists between individual organisms due to population-level variation due to previous mutations and also potential recent spontaneous mutations. There also can be some error in the sequencing process that results in differences between sequences. There are also many different types of nucleotide sequences and protein sequences in the NCBI database. With respect to nucleotide sequences, some many be entire genomic DNA sequences, some may be mRNAs, and some may be lower quality sequences such as expressed sequence tags (ESTs, which are derived from parts of mRNAs), or DNA sequences of contigs from genome projects. That is, you can end up with an entry in the protein database based on sequence derived from a genomic sequence, from sequencing just the gene, and from other routes. Furthermore, some sequences may be manually curated by NCBI staff so that the associated entries contain extra information, but the majority of sequences are uncurated. Therefore, NCBI databases often contains redundant information for a gene, contains sequences of varying quality, and contains both uncurated and curated data. As a result, NCBI has made a special database called RefSeq (reference sequence database), which is a subset of the NCBI database. The data in RefSeq is manually curated, is high quality sequence data, and is non-redundant; this means that each gene (or splice-form / isoform of a gene, in the case of eukaryotes), protein, or genome sequence is only represented once. The data in RefSeq is curated and is of much higher quality than the rest of the NCBI Sequence Database. However, unfortunately, because of the high level of manual curation required, RefSeq does not cover all species, and is not comprehensive for the species that are covered so far. To speed up searches and simplify the results in to can be very useful to just search RefSeq. However, for detailed and thorough work the full database should probably be searched and the results scrutinized. You can easily tell that a sequence comes from RefSeq because its accession number starts with particular sequence of letters. That is, accessions of RefSeq sequences corresponding to protein records usually start with NP_, and accessions of RefSeq curated complete genome sequences usually start with NC_ or NS_. 13.9 Querying the NCBI Database You may need to interrogate the NCBI Database to find particular sequences or a set of sequences matching given criteria, such as: The sequence with accession NC_001477 The sequences published in Nature 460:352-358 All sequences from Chlamydia trachomatis Sequences submitted by Caroline Cameron, a syphilis researcher Flagellin or fibrinogen sequences The glutamine synthetase gene from Mycobacteriuma leprae Just the upstream control region of the Mycobacterium leprae dnaA gene The sequence of the Mycobacterium leprae DnaA protein The genome sequence of syphilis, Treponema pallidum subspp. pallidum All human nucleotide sequences associated with malaria There are two main ways that you can query the NCBI database to find these sets of sequences. The first possibility is to carry out searches on the NCBI website. The second possibility is to carry out searches from R using one of several packages that can interface with NCBI. As of October 2019 rentrez seems to be the best package for this.. Below, I will explain how to manually carry out queries on the NCBI database. 13.10 Querying the NCBI Database via the NCBI Website (for reference) NOTE: The following section is here for reference; you need to know its possible to refine searches but do not need to know any of these actual tags. If you are carrying out searches on the NCBI website, to narrow down your searches to specific types of sequences or to specific organisms, you will need to use “search tags”. For example, the search tags “[PROP]” and “[ORGN]” let you restrict your search to a specific subset of the NCBI Sequence Database, or to sequences from a particular taxon, respectively. Here is a list of useful search tags, which we will explain how to use below: [AC], e.g. NC_001477[AC] With a particular accession number [ORGN], e.g. Fungi[ORGN] From a particular organism or taxon [PROP], e.g. biomol_mRNA[PROP] Of a specific type (eg. mRNA) or from a specific database (eg. RefSeq) [JOUR], e.g. Nature[JOUR] Described in a paper published in a particular journal [VOL], e.g. 531[VOL] Described in a paper published in a particular journal volume [PAGE], e.g. 27[PAGE] Described in a paper with a particular start-page in a journal [AU], e.g. “Smith J”[AU] Described in a paper, or submitted to NCBI, by a particular author To carry out searches of the NCBI database, you first need to go to the NCBI website, and type your search query into the search box at the top. For example, to search for all sequences from Fungi, you would type “Fungi[ORGN]” into the search box on the NCBI website. You can combine the search tags above by using “AND”, to make more complex searches. For example, to find all mRNA sequences from Fungi, you could type “Fungi[ORGN] AND biomol_mRNA[PROP]” in the search box on the NCBI website. Likewise, you can also combine search tags by using “OR”, for example, to search for all mRNA sequences from Fungi or Bacteria, you would type “(Fungi[ORGN] OR Bacteria[ORGN]) AND biomol_mRNA[PROP]” in the search box. Note that you need to put brackets around “Fungi[ORGN] OR Bacteria[ORGN]” to specify that the word “OR” refers to these two search tags. Here are some examples of searches, some of them made by combining search terms using “AND”: NC_001477[AC] - With accession number NC_001477 Nature[JOUR] AND 460[VOL] AND 352[PAGE] - Published in Nature 460:352-358 “Chlamydia trachomatis”[ORGN] - From the bacterium Chlamydia trachomatis “Berriman M”[AU] - Published in a paper, or submitted to NCBI, by M. Berriman flagellin OR fibrinogen - Which contain the word “flagellin” or “fibrinogen” in their NCBI record “Mycobacterium leprae”[ORGN] AND dnaA - Which are from M. leprae, and contain “dnaA” in their NCBI record “Homo sapiens”[ORGN] AND “colon cancer” - Which are from human, and contain “colon cancer” in their NCBI record “Homo sapiens”[ORGN] AND malaria - Which are from human, and contain “malaria” in their NCBI record “Homo sapiens”[ORGN] AND biomol_mrna[PROP] - Which are mRNA sequences from human “Bacteria”[ORGN] AND srcdb_refseq[PROP] - Which are RefSeq sequences from Bacteria “colon cancer” AND srcdb_refseq[PROP] - From RefSeq, which contain “colon cancer” in their NCBI record Note that if you are searching for a phrase such as “colon cancer” or “Chlamydia trachomatis”, you need to put the phrase in quotes when typing it into the search box. This is because if you type the phrase in the search box without quotes, the search will be for NCBI records that contain either of the two words “colon” or “cancer” (or either of the two words “Chlamydia” or “trachomatis”), not necessarily both words. As mentioned above, the NCBI database contains several sub-databases, including the NCBI Nucleotide database and the NCBI Protein database. If you go to the NCBI website, and type one of the search queries above in the search box at the top of the page, the results page will tell you how many matching NCBI records were found in each of the NCBI sub-databases. For example, if you search for “Chlamydia trachomatis[ORGN]”, you will get matches to proteins from C. trachomatis in the NCBI Protein database, matches to DNA and RNA sequences from C. trachomatis in the NCBI Nucleotide database, matches to whole genome sequences for C. trachomatis strains in the NCBI Genome database, and so on: Alternatively, if you know in advance that you want to search a particular sub-database, for example, the NCBI Protein database, when you go to the NCBI website, you can select that sub-database from the drop-down list above the search box, so that you will search that sub-database. 13.11 Example: finding the sequences published in Nature 460:352-358 (for reference) NOTE: The following section is here for reference; you need to know its possible to refine searches but do not need to know any of these actual tags. For example, if you want to find sequences published in Nature 460:352-358, you can use the “[JOUR]”, “[VOL]” and “[PAGE]” search terms. That is, you would go to the NCBI website and type in the search box on the top: “Nature”[JOUR] AND 460[VOL] AND 352[PAGE], where [JOUR] specifies the journal name, [VOL] the volume of the journal the paper is in, and [PAGE] the page number. This should bring up a results page with “50890” beside the word “Nucleotide”, and “1” beside the word “Genome”, and “25701” beside the word “Protein”, indicating that there were 50890 hits to sequence records in the Nucleotide database, which contains DNA and RNA sequences, and 1 hit to the Genome database, which contains genome sequences, and 25701 hits to the Protein database, which contains protein sequences. If you click on the word “Nucleotide”, it will bring up a webpage with a list of links to the NCBI sequence records for those 50890 hits. The 50890 hits are all contigs from the schistosome worm Schistosoma mansoni. Likewise, if you click on the word “Protein”, it will bring up a webpage with a list of links to the NCBI sequence records for the 25701 hits, and you will see that the hits are all predicted proteins for Schistosoma mansoni. If you click on the word “Genome”, it will bring you to the NCBI record for the Schistosoma mansoni genome sequence, which has NCBI accession NS_00200. Note that the accession starts with “NS_”, which indicates that it is a RefSeq accession. Therefore, in Nature volume 460, page 352, the Schistosoma mansoni genome sequence was published, along with all the DNA sequence contigs that were sequenced for the genome project, and all the predicted proteins for the gene predictions made in the genome sequence. You can view the original paper on the Nature website at http://www.nature.com/nature/journal/v460/n7253/abs/nature08160.html. Note: Schistmosoma mansoni is a parasitic worm that is responsible for causing schistosomiasis, which is classified by the WHO as a neglected tropical disease. "],["downloading-ncbi-sequence-data-by-hand.html", "Chapter 14 Downloading NCBI sequence data by hand 14.1 Preface 14.2 Retrieving genome sequence data via the NCBI website", " Chapter 14 Downloading NCBI sequence data by hand By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). 14.1 Preface The following chapter was originally written by Avril Coghlan. It provides brief, basic information for how to access sequences via the internet. Subsequent chapters provide more details and R code. 14.2 Retrieving genome sequence data via the NCBI website You can easily retrieve DNA or protein sequence data by hand from the NCBI Sequence Database via its website www.ncbi.nlm.nih.gov. Dengue DEN-1 DNA is a viral DNA sequence and its NCBI accession number is NC_001477. To retrieve the DNA sequence for the Dengue DEN-1 virus from NCBI, go to the NCBI website, type “NC_001477” in the Search box at the top of the webpage, and press the “Search” button beside the Search box. (While this is the normal workflow, accessions related to well-known organisms can sometimes turn up using a direct Google search. This is the case for NC_001477 - if you Google it you can go directly to the website with the genome sequence.) On the results page of a normal NCBI search you will see the number of hits to “NC_001477” in each of the NCBI databases on the NCBI website. There are many databases on the NCBI website, for example, PubMed and Pubmed Central contain abstracts from scientific papers, the Genes and Genomes database contains DNA and RNA sequence data, the Proteins database contains protein sequence data, and so on. Most biologist would do this type of work by hand from within their web browser, but it can also be done by writing small programs in scripting languages such as Python or R. In R, the rentrez package is a powerful tool for intersecting with NCBI resource. In this tutorial we’ll focus on the web interface. Its good to remember, though, that almost anything done via the webpage can be automated using a computer script. A challenge when learning to use NCBI resources is that there is a tremendous amount of sequence information available and you need to learn how to sort through what the search results provide. As you are looking for the DNA sequence of the Dengue DEN-1 virus genome, you expect to see a hit in the NCBI Nucleotide database. This is indicated at the top of the page where it says “NUCLEOTIDE SEQUENCE” and lists “Dengue virus 1, complete genome.” When you click on the link for the Nucleotide database, it will bring you to the record for NC_001477 in the NCBI Nucleotide database. This will contain the name and NCBI accession of the sequence, as well as other details such as any papers describing the sequence. If you scroll down you’ll see the sequence also. If you need it, you can retrieve the DNA sequence for the DEN-1 Dengue virus genome sequence as a FASTA format sequence file in a couple ways. The easiest is just to copy and paste it into a text, .R, or other file. You can also click on “Send to” at the top right of the NC_001477 sequence record webpage (just to the left of the side bar; its kinda small). After you click on Send to you can pick several options. and then choose “File” in the menu that appears, and then choose FASTA from the “Format” menu that appears, and click on “Create file”. The sequence will then download. The default file name is sequence.fasta so you’ll probably want to change it. You can now open the FASTA file containing the DEN-1 Dengue virus genome sequence using a text editor like Notepad, WordPad, Notepad++, or even RStudio on your computer. To find a text editor on your computer search for “text” from the start menu (Windows) and usually one will come up. (Opening the file in a word processor like word isn’t recommended). "],["downloading-sequences-from-uniprot-by-hand.html", "Chapter 15 Downloading sequences from UniProt by hand 15.1 Vocab 15.2 Downloading Protein data from UniProt 15.3 Viewing the UniProt webpage for a protein sequence 15.4 Retrieving a UniProt protein sequence via the UniProt website", " Chapter 15 Downloading sequences from UniProt by hand By: Avril Coghlan. Adapted, edited and expanded: Nathan Brouwer under the Creative Commons 3.0 Attribution License (CC BY 3.0). 15.1 Vocab RefSeq manual curation UniProt accession 15.2 Downloading Protein data from UniProt In a previous vignette you learned how to retrieve sequences from the NCBI database. The NCBI database is a key database in bioinformatics because it contains essentially all DNA sequences ever sequenced. As mentioned previously, a subsection of the NCBI database called RefSeq consists of high quality DNA and protein sequence data. Furthermore, the NCBI entries for the RefSeq sequences have been manually curated, which means that expert biologists employed by NCBI have added additional information to the NCBI entries for those sequences, such as details of scientific papers that describe the sequences. Another extremely important manually curated database is UniProt www.uniprot.org, which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. For each protein in UniProt, the UniProt curators read all the scientific papers that they can find about that protein, and add information from those papers to the protein’s UniProt entry. For example, for a human protein, the UniProt entry for the protein usually includes information about the biological function of the protein, in what human tissues it is expressed, whether it interacts with other human proteins, and much more. All this information has been manually gathered by the UniProt curators from scientific papers, and the papers in which the found the information are always listed in the UniProt entry for the protein. Just like NCBI, UniProt also assigns an accession to each sequence in the UniProt database. Although the same protein sequence may appear in both the NCBI database and the UniProt database, it will have different NCBI and UniProt accessions. However, there is usually a link on the NCBI entry for the protein sequence to the UniProt entry, and vice versa. 15.3 Viewing the UniProt webpage for a protein sequence If you are given the UniProt accession for a protein, to find the UniProt entry for the protein, you first need to go the UniProt website, www.uniprot.org. At the top of the UniProt website, you will see a search box, and you can type the accession of the protein that you are looking for in this search box, and then click on the “Search” button to search for it. For example, if you want to find the sequence for the chorismate lyase protein from Mycobacterium leprae (the bacterium which causes leprosy), which has UniProt accession Q9CD83, you would type just “Q9CD83” in the search box and press “Search”. The UniProt entry for UniProt accession Q9CD83 will then appear in your web browser. Beside the heading “Organism” you can see the organism is given as Mycobacterium leprae. If you scroll down you’ll find a section Names and Taxonomy and beside the heading “Taxonomic lineage”, you can see “Bacteria - Actinobacteria - Actinobacteridae - Actinomycetales - Corynebacterineae - Mycobacteriaceae- Mycobacterium”. This tells us that Mycobacterium is a species of bacteria, which belongs to a group of related bacteria called the Mycobacteriaceae, which itself belongs to a larger group of related bacteria called the Corynebacterineae, which itself belongs to an even larger group of related bacteria called the Actinomycetales, which itself belongs to the Actinobacteridae, which itself belongs to a huge group of bacteria called the Actinobacteria. 15.3.1 Protein function Back up at the top under “organism” is says “Status”, which tells us the annotation score is 2 out of 5, that it is a “Protein inferred from homology”, which means what we know about it is derived from bioinformatics and computational tools, not lab work. Beside the heading “Function”, it says that the function of this protein is that it “Removes the pyruvyl group from chorismate to provide 4-hydroxybenzoate (4HB)”. This tells us this protein is an enzyme (a protein that increases the rate of a specific biochemical reaction), and tells us what is the particular biochemical reaction that this enzyme is involved in. At the end of this info it says “By similarity”, which again indicates that what we know about this protein comes from bioinformatics, not lab work. 15.3.2 Protein sequence and size Under Sequence we see that the sequence length is 210 amino acids long (210 letters long) and has a mass of 24,045 daltons. We can access the sequence as a FASTA file from here if we want and also carry out a BLAST search from a link on the right. 15.3.3 Other information Further down the UniProt page for this protein, you will see a lot more information, as well as many links to webpages in other biological databases, such as NCBI. The huge amount of information about proteins in UniProt means that if you want to find out about a particular protein, the UniProt page for that protein is a great place to start. 15.4 Retrieving a UniProt protein sequence via the UniProt website There are a couple different ways to retrieve the sequence. At the top of the page is a tab that say “Format” which brings you to a page with th FASTA file. You can copy and paste the sequence from here if you want. To save it as a file, go to the “File” menu of your web browser, choose “Save page as”, and save the file. Remember to give the file a sensible name (eg. “Q9CD83.fasta” for accession Q9CD83), and in a place that you will remember (eg. in the “My Documents” folder). For example, you can retrieve the protein sequences for the chorismate lyase protein from Mycobacterium leprae (which has UniProt accession Q9CD83) and for the chorismate lyase protein from Mycobacterium ulcerans (UniProt accession A0PQ23), and save them as FASTA-format files (eg. “Q9CD83.fasta” and “A0PQ23.fasta”, as described above. You can also put the UniProt information into an online Basket. If you do this for both Q9CD83 and A0PQ23 you can think click on Basket, select both entries, and carry out a pairwise alignment by clicking on Align. Mycobacterium leprae is the bacterium which causes leprosy, while Mycobacterium ulcerans is a related bacterium which causes Buruli ulcer, both of which are classified by the WHO as neglected tropical diseases. The M. leprae and M. ulcerans chorismate lyase proteins are an example of a pair of homologous (related) proteins in two related species of bacteria. If you downloaded the protein sequences for UniProt accessions Q9CD83 and A0PQ23 and saved them as FASTA-format files (eg. “Q9CD83.fasta” and “A0PQ23.fasta”), you could read them into R using the read.fasta() function in the SeqinR R package (as detailed in another Vignette) or a similar function from another package. Note that the read.fasta() function normally expects that you have put your FASTA-format files in the the working directory of R. For convenience so you can explore these sequences they have been saved in a special folder in the compbio4all package and can be accessed like this for the Leprosy sequence # load compbio4all library(compbio4all) # locate the file within the package using system.file() file.1 <- system.file("./extdata/Q9CD83.fasta",package = "compbio4all") # load seqinr library("seqinr") # load fasta leprae <- read.fasta(file = file.1) lepraeseq <- leprae[[1]] We can confirm str(lepraeseq) # 'SeqFastadna' chr [1:210] "m" "t" "n" "r" "t" "l" "s" "r" "e" "e" "i" ... # - attr(*, "name")= chr "sp|Q9CD83|PHBS_MYCLE" # - attr(*, "Annot")= chr ">sp|Q9CD83|PHBS_MYCLE Chorismate pyruvate-lyase # OS=Mycobacterium leprae (strain TN) OX=272631 GN=ML0133 PE=3 SV=1" For the other sequence # locate the file within the package using system.file() file.2 <- system.file("./extdata/A0PQ23.fasta", package = "compbio4all") # load fasta ulcerans <- read.fasta(file = file.2) ulceransseq <- ulcerans[[1]] str(ulceransseq)[[1]] # 'SeqFastadna' chr [1:212] "m" "l" "a" "v" "l" "p" "e" "k" "r" "e" "m" ... # - attr(*, "name")= chr "tr|A0PQ23|A0PQ23_MYCUA" # - attr(*, "Annot")= chr ">tr|A0PQ23|A0PQ23_MYCUA Chorismate pyruvate-lyase # OS=Mycobacterium ulcerans (strain Agy99) OX=362242 GN=MUL_2003 PE=4 SV=1" "],["introducingFASTA.html", "Chapter 16 Introducing FASTA Files 16.1 Example FASTA file 16.2 Multiple sequences in a single FASTA file 16.3 Multiple sequence alignments can be stored in FASTA format 16.4 FASTQ Format", " Chapter 16 Introducing FASTA Files Adapted from Wikipedia: https://en.wikipedia.org/wiki/FASTA_format In bioinformatics, the FASTA format is a text-based format for representing either nucleotide sequences or amino acid (protein) sequences, in which nucleotides or amino acids are represented using single-letter codes. The format allows for sequence names and comments to precede the sequences. The format originates from the FASTA alignment software, but has now become a near universal standard in the field of bioinformatics. The simplicity of FASTA format makes it easy to manipulate and parse sequences using text-processing tools and scripting languages like the R programming language and Python. The first line in a FASTA file starts with a “>” (greater-than) symbol and holds summary information about the sequence, often starting with a unique accession number and followed by information like the name of the gene, the type of sequence, and the organism it is from. On the next is the sequence itself in a standard one-letter character string. Anything other than a valid character is be ignored (including spaces, tabs, asterisks, etc…). A multiple sequence FASTA format can be obtained by concatenating several single sequence FASTA files in a common file (also known as multi-FASTA format). Following the header line, the actual sequence is represented. Sequences may be protein sequences or nucleic acid sequences, and they can contain gaps or alignment characters. Sequences are expected to be represented in the standard amino acid and nucleic acid codes. Lower-case letters are accepted and are mapped into upper-case; a single hyphen or dash can be used to represent a gap character; and in amino acid sequences, U and * are acceptable letters. FASTQ format is a form of FASTA format extended to indicate information related to sequencing. It is created by the Sanger Centre in Cambridge. Bioconductor.org’s Biostrings package can be used to read and manipulate FASTA files in R “FASTA format is a text-based format for representing either nucleotide sequences or peptide sequences, in which base pairs or amino acids are represented using single-letter codes. A sequence in FASTA format begins with a single-line description, followed by lines of sequence data. The description line is distinguished from the sequence data by a greater-than (”>“) symbol in the first column. It is recommended that all lines of text be shorter than 80 characters in length.” (https://zhanglab.dcmb.med.umich.edu/FASTA/) 16.1 Example FASTA file Here is an example of the contents of a FASTA file. (If your are viewing this chapter in the form of the source .Rmd file, the cat() function is included just to print out the content properly and is not part of the FASTA format). cat(">gi|186681228|ref|YP_001864424.1| phycoerythrobilin:ferredoxin oxidoreductase MNSERSDVTLYQPFLDYAIAYMRSRLDLEPYPIPTGFESNSAVVGKGKNQEEVVTTSYAFQTAKLRQIRA AHVQGGNSLQVLNFVIFPHLNYDLPFFGADLVTLPGGHLIALDMQPLFRDDSAYQAKYTEPILPIFHAHQ QHLSWGGDFPEEAQPFFSPAFLWTRPQETAVVETQVFAAFKDYLKAYLDFVEQAEAVTDSQNLVAIKQAQ LRYLRYRAEKDPARGMFKRFYGAEWTEEYIHGFLFDLERKLTVVK") ## >gi|186681228|ref|YP_001864424.1| phycoerythrobilin:ferredoxin oxidoreductase ## MNSERSDVTLYQPFLDYAIAYMRSRLDLEPYPIPTGFESNSAVVGKGKNQEEVVTTSYAFQTAKLRQIRA ## AHVQGGNSLQVLNFVIFPHLNYDLPFFGADLVTLPGGHLIALDMQPLFRDDSAYQAKYTEPILPIFHAHQ ## QHLSWGGDFPEEAQPFFSPAFLWTRPQETAVVETQVFAAFKDYLKAYLDFVEQAEAVTDSQNLVAIKQAQ ## LRYLRYRAEKDPARGMFKRFYGAEWTEEYIHGFLFDLERKLTVVK 16.2 Multiple sequences in a single FASTA file Multiple sequences can be stored in a single FASTA file, each on separated by a line and have its own headline. cat(">LCBO - Prolactin precursor - Bovine MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGPGNCQVSLRDLFDRAVMVSHYIHDLSS EMFNEFDKRYAQGKGFITMALNSCHTSSLPTPEDKEQAQQTHHEVLMSLILGLLRSWNDPLYHL VTEVRGMKGAPDAILSRAIEIEEENKRLLEGMEMIFGQVIPGAKETEPYPVWSGLPSLQTKDED ARYSAFYNLLHCLRRDSSKIDTYLKLLNCRIIYNNNC* >MCHU - Calmodulin - Human, rabbit, bovine, rat, and chicken MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTID FPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREA DIDGDGQVNYEEFVQMMTAK* >gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus] LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG LLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVIL GLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGX IENY") ## >LCBO - Prolactin precursor - Bovine ## MDSKGSSQKGSRLLLLLVVSNLLLCQGVVSTPVCPNGPGNCQVSLRDLFDRAVMVSHYIHDLSS ## EMFNEFDKRYAQGKGFITMALNSCHTSSLPTPEDKEQAQQTHHEVLMSLILGLLRSWNDPLYHL ## VTEVRGMKGAPDAILSRAIEIEEENKRLLEGMEMIFGQVIPGAKETEPYPVWSGLPSLQTKDED ## ARYSAFYNLLHCLRRDSSKIDTYLKLLNCRIIYNNNC* ## ## >MCHU - Calmodulin - Human, rabbit, bovine, rat, and chicken ## MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMINEVDADGNGTID ## FPEFLTMMARKMKDTDSEEEIREAFRVFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIREA ## DIDGDGQVNYEEFVQMMTAK* ## ## >gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus] ## LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV ## EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG ## LLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVIL ## GLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGX ## IENY 16.3 Multiple sequence alignments can be stored in FASTA format Aligned FASTA format can be used to store the output of Multiple Sequence Alignment (MSA). This format contains Multiple entries, each with their own header line Gaps inserted to align sequences are indicated by . Each spaces added to the beginning and end of sequences that vary in length are indicated by ~ In the sample FASTA file below, the example1 sequence has a gap of 8 near its beginning. The example2 sequence has numerous ~ indicating that this sequence is missing data from its beginning that are present in the other sequences. The example3 sequence has numerous ~ at its end, indicating that this sequence is shorter than the others. cat(">example1 MKALWALLLVPLLTGCLA........EGELEVTDQLPGQSDQP.WEQALNRFWDYLRWVQ GNQARDRLEEVREQMEEVRSKMEEQTQQIRLQAEIFQARIKGWFEPLVEDMQRQWANLME KIQASVATNSIASTTVPLENQ >example2 ~~~~~~~~~~~~~~~~~~~~~~~~~~KVQQELEPEAGWQTGQP.WEAALARFWDYLRWVQ SSRARGHLEEMREQIQEVRVKMEEQADQIRQKAEAFQARLKSWFEPLLEDMQRQWDGLVE KVQAAVAT.IPTSKPVEEP~~ >example3 MRSLVVFFALAVLTGCQARSLFQAD..............APQPRWEEMVDRFWQYVSELN AGALKEKLEETAENL...RTSLEGRVDELTSLLAPYSQKIREQLQEVMDKIKEATAALPT QA~~~~~~~~~~~~~~~~~~~") ## >example1 ## MKALWALLLVPLLTGCLA........EGELEVTDQLPGQSDQP.WEQALNRFWDYLRWVQ ## GNQARDRLEEVREQMEEVRSKMEEQTQQIRLQAEIFQARIKGWFEPLVEDMQRQWANLME ## KIQASVATNSIASTTVPLENQ ## >example2 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~KVQQELEPEAGWQTGQP.WEAALARFWDYLRWVQ ## SSRARGHLEEMREQIQEVRVKMEEQADQIRQKAEAFQARLKSWFEPLLEDMQRQWDGLVE ## KVQAAVAT.IPTSKPVEEP~~ ## >example3 ## MRSLVVFFALAVLTGCQARSLFQAD..............APQPRWEEMVDRFWQYVSELN ## AGALKEKLEETAENL...RTSLEGRVDELTSLLAPYSQKIREQLQEVMDKIKEATAALPT ## QA~~~~~~~~~~~~~~~~~~~ 16.4 FASTQ Format Adapted from Wikipedia: https://en.wikipedia.org/wiki/FASTQ_format FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA formatted sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer. A FASTQ file normally uses four lines per sequence. Line 1 begins with a @ character and is followed by a sequence identifier and an optional description (like a FASTA title line). Line 2 is the raw sequence letters. Line 3 begins with a + character and is optionally followed by the same sequence identifier (and any description) again. Line 4 encodes the quality values for the sequence in Line 2 of the file, and must contain the same number of symbols as letters in the sequence. A FASTQ file containing a single sequence might look like this:” cat("@SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65") ## @SEQ_ID ## GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT ## + ## !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 Here are the quality value characters in left-to-right increasing order of quality (ASCII):” !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\]^_`abcdefghijklmnopqrstuvwxyz{|}~ FASTQ files typically do not include line breaks and do not wrap around when they reach the width of a normal page or file. "],["a-complete-bioinformatics-workflow-in-r.html", "Chapter 17 A complete bioinformatics workflow in R", " Chapter 17 A complete bioinformatics workflow in R By: Nathan L. Brouwer "],["worked-example-building-a-phylogeny-in-r.html", "Chapter 18 “Worked example: Building a phylogeny in R” 18.1 Introduction 18.2 Software Preliminaires 18.3 Downloading macro-molecular sequences 18.4 Prepping macromolecular sequences 18.5 Aligning sequences 18.6 The shroom family of genes 18.7 Downloading multiple sequences 18.8 Multiple sequence alignment 18.9 Genetic distance. 18.10 Phylognetic trees (finally!)", " Chapter 18 “Worked example: Building a phylogeny in R” 18.1 Introduction Phylogenies play an important role in computational biology and bioinformatics. Phylogenetics itself is an obligatly computational field that only began rapid growth when computational power allowed the many algorithms it relies on to be done rapidly. Phylogeneies of species, genes and proteins are used to address many biological issues, including Patterns of protein evolution Origin and evolution of phenotypic traits Origin and progression of epidemics Origin of evolution of diseases (eg, zooenoses) Prediction of protein function from its sequence … and many more The actual building of a phylogeny is a computationally intensive task; moreover, there are many bioinformatics and computational tasks the precede the construction of a phylogeny: genome sequencing and assembly computational gene prediction and annotation database searching and results screening pairwise sequence alignment data organization and cleaning multiple sequence alignment evaluation and validation of alignment accuracy Once all of these steps have been carried out, the building of a phylogeny involves picking a model of sequence evolution or other description of evolution picking a statistical approach to tree construction evaluating uncertainty in the final tree In this chapter we will work through many of these steps. In most cases we will pick the easiest or fastest option; in later chapters we will unpack the various options. This chapter is written as an interactive R sessions. You can follow along by opening the .Rmd file of the chapter or typing the appropriate commands into your own script. I assume that all the necessary packages have been installed and they only need to be loaded into R using the library() command. This lesson walks you through and entire workflow for a bioinformatics, including obtaining FASTA sequences cleaning sequences creating alignments creating distance a distance matrix building a phylogenetic tree We’ll examine the Shroom family of genes, which produces Shroom proteins essential for tissue formation in many multicellular eukaryotes, including neural tube formation in vertebrates. We’ll examine shroom in several very different organism, including humans, mice and sea urchins. There is more than one type of shroom in vertebrates, and we’ll also look at two different Shroom genes: shroom 1 and shroom 2. This lesson draws on skills from previous sections of the book, but is written to act as a independent summary of these activities. There is therefore review of key aspects of R and bioinformatics throughout it. 18.2 Software Preliminaires 18.2.1 Vocab argument function list named list vector named vector for() loop R console 18.2.2 R functions library() round() plot() mtext() nchar() rentrez::entrez_fetch() compbio4all::entrez_fetch_list() compbio4all::print_msa() (Coghlan 2011) Biostrings::AAStringSet() msa::msa() msa::msaConvert() msa::msaPrettyPrint() seqinr::dist.alignment() ape::nj() A few things need to be done to get started with our R session. 18.2.3 Download necessary packages Many R sessions begin by downloading necessary software packages to augment R’s functionality. If you don’t have them already, you’ll need the following packages from CRAN: ape seqinr rentrez devtools The CRAN packages can be loaded with install.packages(). You’ll also need these packages from Bioconductor: msa Biostrings For installing packages from Bioconductor, see the chapter at the beginning of this book on this process. Finally, you’ll need this package from GitHub compbio4all ggmsa To install packages from GitHub you can use the code devtools::install_github(\"brouwern/combio4all\") and devtools::install_github(\"brouwern/ggmsa\") 18.2.4 Load packages into memory We now need to load up all our bioinformatics and phylogenetics software into R. This is done with the library() command. To run this code just clock on the sideways green triangle all the way to the right of the code. NOTE: You’ll likely see some red code appear on your screen. No worries, totally normal! # github packages library(compbio4all) # CRAN packages library(rentrez) library(seqinr) library(ape) # Bioconductor packages library(msa) library(Biostrings) 18.3 Downloading macro-molecular sequences We’re going to explore some sequences. First we need to download them. To do this we’ll use a function, entrez_fretch(), which accesses the Entrez system of database (ncbi.nlm.nih.gov/search/). This function is from the rentrez package, which stands for “R-Entrez.” We need to tell entrez_fetch() several things db = ... the type of entrez database. id = ... the accession (ID) number of the sequence rettype = ... file type what we want the function to return. Formally, these things are called arguments by R. We’ll use these settings: db = \"protein\" to access the Entrez database of protein sequences rettype = \"fasta\", which is a standard file format for nucleic acid and protein sequences We’ll set id = ... to sequences whose accession numbers are: NP_065910: Human shroom 3 AAF13269: Mouse shroom 3a CAA58534: Human shroom 2 XP_783573: Sea urchin shroom There are two highly conserved regions of shroom 3 1. ASD 1: aa 884 to aa 1062 in hShroom3 1. ASD 2: aa 1671 to aa 1955 in hShroom3 Normally we’d have to download these sequences by hand through pointing and clicking on GeneBank records on the NCBI website. In R we can do it automatically; this might take a second. All the code needed is this: # Human shroom 3 (H. sapiens) hShroom3 <- entrez_fetch(db = "protein", id = "NP_065910", rettype = "fasta") The output is in FASTA format; we’ll use the cat() to do a little formatting for us: cat(hShroom3) ## >NP_065910.3 protein Shroom3 [Homo sapiens] ## MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKL ## QAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAA ## WSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYL ## RRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGR ## ERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNI ## PRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSL ## GSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGM ## LYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFC ## SVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDF ## QEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTK ## AEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQ ## GFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEP ## LGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGS ## PESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPH ## TPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFP ## ESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAY ## LQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLS ## GANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSG ## FGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTP ## CPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGP ## RGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTS ## DPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPV ## YSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEA ## ESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDK ## SLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYY ## SVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEA ## LISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHED ## ARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDF ## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL Note the initial >, then the header line of NP_065910.3 protein Shroom3 [Homo sapiens]. After that is the amino acid sequence. The underlying data also includes the newline character \\n to designate where each line of amino acids stops. We can get the rest of the data by just changing the id = ... argument: # Mouse shroom 3a (M. musculus) mShroom3a <- entrez_fetch(db = "protein", id = "AAF13269", rettype = "fasta") # Human shroom 2 (H. sapiens) hShroom2 <- entrez_fetch(db = "protein", id = "CAA58534", rettype = "fasta") # Sea-urchin shroom sShroom <- entrez_fetch(db = "protein", id = "XP_783573", rettype = "fasta") I’m going to check about how long each of these sequences is - each should have an at least slightly different length. If any are identical, I might have repeated an accession name or re-used an object name. The function nchar() counts of the number of characters in an R object. nchar(hShroom3) ## [1] 2070 nchar(mShroom3a) ## [1] 2083 nchar(sShroom) ## [1] 1758 nchar(hShroom2) ## [1] 1673 18.4 Prepping macromolecular sequences “90% of data analysis is data cleaning” (-Just about every data analyst and data scientist on twitter) We have our sequences, but the current format isn’t directly usable for us yet because there are several things that aren’t sequence information metadata (the header) page formatting information (the newline character) We can remove this non-sequence information using a function I wrote called fasta_cleaner(), which is in the compbio4all package. The function uses regular expressions to remove the info we don’t need. ASIDE: If we run the name of the command with out any quotation marks we can see the code: fasta_cleaner ## function(fasta_object, parse = TRUE){ ## ## fasta_object <- sub("^(>)(.*?)(\\\\n)(.*)(\\\\n\\\\n)","\\\\4",fasta_object) ## fasta_object <- gsub("\\n", "", fasta_object) ## ## if(parse == TRUE){ ## fasta_object <- stringr::str_split(fasta_object, ## pattern = "", ## simplify = FALSE) ## } ## ## return(fasta_object[[1]]) ## } ## <bytecode: 0x7fcccbb38a70> ## <environment: namespace:compbio4all> End ASIDE Now use the function to clean our sequences; we won’t worry about what pare = ... is for. hShroom3 <- fasta_cleaner(hShroom3, parse = F) mShroom3a <- fasta_cleaner(mShroom3a, parse = F) hShroom2 <- fasta_cleaner(hShroom2, parse = F) sShroom <- fasta_cleaner(sShroom, parse = F) Now let’s take a peek at what our sequences look like: hShroom3 ## [1] "MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKLQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAAWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYLRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGRERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNIPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSLGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGMLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFCSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDFQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTKAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEPLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGSPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPHTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFPESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAYLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLSGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSGFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTPCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGPRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTSDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPVYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEAESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDKSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYYSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEALISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHEDARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDFIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL" 18.5 Aligning sequences We can do a global alignment of one sequence against another using the pairwiseAlignment() function from the Bioconductor package Biostrings (note that capital “B” in Biostrings; most R package names are all lower case, but not this one). Let’s align human versus mouse shroom: align.h3.vs.m3a <- Biostrings::pairwiseAlignment( hShroom3, mShroom3a) We can peek at the alignment align.h3.vs.m3a ## Global PairwiseAlignmentsSingleSubject (1 of 1) ## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL ## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL ## score: 2189.934 The score tells us how closely they are aligned; higher scores mean the sequences are more similar. Its hard to interpret the number on its own so we can get the percent sequence identity (PID) using the pid() function. Biostrings::pid(align.h3.vs.m3a) ## [1] 70.56511 So, shroom3 from humans and shroom3 from mice are ~71% similar (at least using this particular method of alignment, and there are many ways to do this!) What about human shroom 3 and sea-urchin shroom? align.h3.vs.h2 <- Biostrings::pairwiseAlignment( hShroom3, hShroom2) First check out the score using score(), which accesses it directly without all the other information. score(align.h3.vs.h2) ## [1] -5673.853 Now the percent sequence alignment with pid(): Biostrings::pid(align.h3.vs.h2) ## [1] 33.83277 So Human shroom 3 and Mouse shroom 3 are 71% identical, but Human shroom 3 and human shroom 2 are only 34% similar? How does it work out evolutionary that a human and mouse gene are more similar than a human and a human gene? What are the evolutionary relationships among these genes within the shroom gene family? 18.6 The shroom family of genes I’ve copied a table from a published paper which has accession numbers for 15 different Shroom genes. shroom_table <- c("CAA78718" , "X. laevis Apx" , "xShroom1", "NP_597713" , "H. sapiens APXL2" , "hShroom1", "CAA58534" , "H. sapiens APXL", "hShroom2", "ABD19518" , "M. musculus Apxl" , "mShroom2", "AAF13269" , "M. musculus ShroomL" , "mShroom3a", "AAF13270" , "M. musculus ShroomS" , "mShroom3b", "NP_065910", "H. sapiens Shroom" , "hShroom3", "ABD59319" , "X. laevis Shroom-like", "xShroom3", "NP_065768", "H. sapiens KIAA1202" , "hShroom4a", "AAK95579" , "H. sapiens SHAP-A" , "hShroom4b", #"DQ435686" , "M. musculus KIAA1202" , "mShroom4", "ABA81834" , "D. melanogaster Shroom", "dmShroom", "EAA12598" , "A. gambiae Shroom", "agShroom", "XP_392427" , "A. mellifera Shroom" , "amShroom", "XP_783573" , "S. purpuratus Shroom" , "spShroom") #sea urchin I’ll do a bit of formatting; you can ignore these details if you want # convert to matrix shroom_table_matrix <- matrix(shroom_table, byrow = T, nrow = 14) # convert to dataframe shroom_table <- data.frame(shroom_table_matrix, stringsAsFactors = F) # name columns names(shroom_table) <- c("accession", "name.orig","name.new") # Create simplified species names shroom_table$spp <- "Homo" shroom_table$spp[grep("laevis",shroom_table$name.orig)] <- "Xenopus" shroom_table$spp[grep("musculus",shroom_table$name.orig)] <- "Mus" shroom_table$spp[grep("melanogaster",shroom_table$name.orig)] <- "Drosophila" shroom_table$spp[grep("gambiae",shroom_table$name.orig)] <- "mosquito" shroom_table$spp[grep("mellifera",shroom_table$name.orig)] <- "bee" shroom_table$spp[grep("purpuratus",shroom_table$name.orig)] <- "sea urchin" Take a look: shroom_table ## accession name.orig name.new spp ## 1 CAA78718 X. laevis Apx xShroom1 Xenopus ## 2 NP_597713 H. sapiens APXL2 hShroom1 Homo ## 3 CAA58534 H. sapiens APXL hShroom2 Homo ## 4 ABD19518 M. musculus Apxl mShroom2 Mus ## 5 AAF13269 M. musculus ShroomL mShroom3a Mus ## 6 AAF13270 M. musculus ShroomS mShroom3b Mus ## 7 NP_065910 H. sapiens Shroom hShroom3 Homo ## 8 ABD59319 X. laevis Shroom-like xShroom3 Xenopus ## 9 NP_065768 H. sapiens KIAA1202 hShroom4a Homo ## 10 AAK95579 H. sapiens SHAP-A hShroom4b Homo ## 11 ABA81834 D. melanogaster Shroom dmShroom Drosophila ## 12 EAA12598 A. gambiae Shroom agShroom mosquito ## 13 XP_392427 A. mellifera Shroom amShroom bee ## 14 XP_783573 S. purpuratus Shroom spShroom sea urchin 18.7 Downloading multiple sequences Instead of getting one sequence at a time we can download several by accessing the “accession” column from the table shroom_table$accession ## [1] "CAA78718" "NP_597713" "CAA58534" "ABD19518" "AAF13269" "AAF13270" ## [7] "NP_065910" "ABD59319" "NP_065768" "AAK95579" "ABA81834" "EAA12598" ## [13] "XP_392427" "XP_783573" We can give this whole set of accessions to entrez_fetch(): shrooms <- entrez_fetch(db = "protein", id = shroom_table$accession, rettype = "fasta") We can look at what we got here with cat() (I won’t display this because it is very long!) cat(shrooms) The current format of these data is a single, long set of data. This is a standard way to store, share and transmit FASTA files, but in R we’ll need a slightly different format. We’ll download all of the sequences again, this time using a function from compbio4all called entrez_fetch_list() which is a wrapper function I wrote to put the output of entrez_fetch() into an R data format called a list. shrooms_list <- entrez_fetch_list(db = "protein", id = shroom_table$accession, rettype = "fasta") Now we have a list which as 14 elements, one for each sequence in our table. length(shrooms_list) ## [1] 14 We now need to clean up each one fo these sequences. We can do that using a simple for() loop: for(i in 1:length(shrooms_list)){ shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F) } Second to last step: we need to take each one of our sequences from our list and put it into a vector, in particular a named vector # make a vector to store output shrooms_vector <- rep(NA, length(shrooms_list)) # run the loop for(i in 1:length(shrooms_vector)){ shrooms_vector[i] <- shrooms_list[[i]] } # name the vector names(shrooms_vector) <- names(shrooms_list) Now the final step: we need to convert our named vector to a string set using Biostrings::AAStringSet(). Note the _ss tag at the end of the object we’re assigning the output to, which designates this as a string set. shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector) 18.8 Multiple sequence alignment We must align all of the sequences we downloaded and use that alignment to build a phylogenetic tree. This will tell us how the different genes, both within and between species, are likely to be related. 18.8.1 Building an Multiple Sequence Alignment (MSA) We’ll use the software msa, which implements the ClustalW multiple sequence alignment algorithm. Normally we’d have to download the ClustalW program and either point-and-click our way through it or use the command line*, but these folks wrote up the algorithm in R so we can do this with a line of R code. This will take a second or two. shrooms_align <- msa(shrooms_vector_ss, method = "ClustalW") ## use default substitution matrix 18.8.2 Viewing an MSA Once we build an MSA we need to visualize it. 18.8.2.1 Viewing an MSA in R We can look at the output from msa(), but its not very helpful shrooms_align ## CLUSTAL 2.1 ## ## Call: ## msa(shrooms_vector_ss, method = "ClustalW") ## ## MsaAAMultipleAlignment with 14 rows and 2252 columns ## aln names ## [1] -------------------------...------------------------- NP_065768 ## [2] -------------------------...------------------------- AAK95579 ## [3] -------------------------...SVFGGVFPTLTSPL----------- AAF13269 ## [4] -------------------------...SVFGGVFPTLTSPL----------- AAF13270 ## [5] -------------------------...CTFSGIFPTLTSPL----------- NP_065910 ## [6] -------------------------...NKS--LPPPLTSSL----------- ABD59319 ## [7] -------------------------...------------------------- CAA58534 ## [8] -------------------------...------------------------- ABD19518 ## [9] -------------------------...LT----------------------- NP_597713 ## [10] -------------------------...------------------------- CAA78718 ## [11] -------------------------...------------------------- EAA12598 ## [12] -------------------------...------------------------- ABA81834 ## [13] MTELQPSPPGYRVQDEAPGPPSCPP...------------------------- XP_392427 ## [14] -------------------------...AATSSSSNGIGGPEQLNSNATSSYC XP_783573 ## Con -------------------------...------------------------- Consensus A function called print_msa() (Coghlan 2011) which I’ve put intocombio4all can give us more informative output by printing out the actual alignment into the R console. To use print_msa() We need to make a few minor tweaks though first. These are behind the scenes changes so don’t worry about the details right now. We’ll change the name to shrooms_align_seqinr to indicate that one of our changes is putting this into a format defined by the bioinformatics package seqinr. class(shrooms_align) <- "AAMultipleAlignment" shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment") I won’t display the output from shrooms_align_seqinr because its very long; we have 14 shroom genes, and shroom happens to be a rather long gene. print_msa(alignment = shrooms_align_seqinr, chunksize = 60) 18.8.2.2 Displaying an MSA as an R plot I’m going to just show about 100 amino acids near the end of the alignment, where there is the most overlap across all of the sequences. This is set with the start = ... and end = ... arguments. Note that we’re using the shrooms_align object. ggmsa::ggmsa(shrooms_align, # shrooms_align, NOT shrooms_align_seqinr start = 2000, end = 2100) ## Registered S3 methods overwritten by 'ggalt': ## method from ## grid.draw.absoluteGrob ggplot2 ## grobHeight.absoluteGrob ggplot2 ## grobWidth.absoluteGrob ggplot2 ## grobX.absoluteGrob ggplot2 ## grobY.absoluteGrob ggplot2 18.8.2.3 Saving an MSA as PDF We can take a look at the alignment in PDF format if we want. I this case I’m going to just show about 100 amino acids near the end of the alignment, where there is the most overlap across all of the sequences. This is set with the y = c(...) argument. msaPrettyPrint(shrooms_align, # alignment file = "shroom_msa.pdf", # file name y=c(2000, 2100), # range askForOverwrite=FALSE) You can see where R is saving things by running getwd() getwd() ## [1] "/Users/nlb24/OneDrive - University of Pittsburgh/0-books/lbrb-bk/lbrb" On a Mac you can usually find the file by searching in Finder for the file name, which I set to be “shroom_msa.pdf” using the file = ... argument above. 18.9 Genetic distance. Next need to first get an estimate of how similar each sequences is. The more amino acids that are identical to each other, the more similar. Instead of similarity, we usually work in terms of difference or genetic distance (a.k.a. evolutionary distance). This is done with the dist.alignment() function. shrooms_dist <- seqinr::dist.alignment(shrooms_align_seqinr, matrix = "identity") We’ve made a matrix using dist.alignment(); let’s round it off so its easier to look at using the round() function. shrooms_dist_rounded <- round(shrooms_dist, digits = 3) Now let’s look at it shrooms_dist_rounded ## NP_065768 AAK95579 AAF13269 AAF13270 NP_065910 ABD59319 CAA58534 ## AAK95579 0.000 ## AAF13269 0.884 0.917 ## AAF13270 0.897 0.917 0.000 ## NP_065910 0.878 0.912 0.533 0.536 ## ABD59319 0.893 0.921 0.783 0.783 0.782 ## CAA58534 0.872 0.908 0.838 0.849 0.840 0.864 ## ABD19518 0.866 0.912 0.834 0.846 0.838 0.855 0.548 ## NP_597713 0.916 0.939 0.903 0.903 0.902 0.904 0.896 ## CAA78718 0.925 0.955 0.896 0.895 0.893 0.893 0.898 ## EAA12598 0.914 0.947 0.899 0.899 0.902 0.897 0.891 ## ABA81834 0.938 0.943 0.935 0.934 0.936 0.940 0.935 ## XP_392427 0.936 0.963 0.935 0.934 0.938 0.941 0.938 ## XP_783573 0.940 0.958 0.942 0.939 0.942 0.935 0.942 ## ABD19518 NP_597713 CAA78718 EAA12598 ABA81834 XP_392427 ## AAK95579 ## AAF13269 ## AAF13270 ## NP_065910 ## ABD59319 ## CAA58534 ## ABD19518 ## NP_597713 0.900 ## CAA78718 0.891 0.919 ## EAA12598 0.896 0.920 0.922 ## ABA81834 0.935 0.932 0.946 0.882 ## XP_392427 0.934 0.927 0.947 0.878 0.923 ## XP_783573 0.946 0.947 0.941 0.925 0.954 0.943 18.10 Phylognetic trees (finally!) We got our sequence, built multiple sequence alignment, and calculated the genetic distance between sequences. Now we are - finally - ready to build a phylogenetic tree. First, we let R figure out the structure of the tree. There are MANY ways to build phylogenetic trees. We’ll use a common one used for exploring sequences called neighbor joining algorithm via the function nj(). Neighbor joining uses genetic distances to cluster sequences into clades. tree <- nj(shrooms_dist) 18.10.1 Plotting phylogenetic trees Now we’ll make a quick plot of our tree using plot() (and add a little label using a function called mtext()). # plot tree plot.phylo (tree, main="Phylogenetic Tree", type = "unrooted", use.edge.length = F) # add label mtext(text = "Shroom family gene tree - unrooted, no branch lengths") This is an **unrooted tree*. For the sake of plotting we’ve also ignored the evolutionary distance between the sequences. To make a rooted tree we remove type = \"unrooted. # plot tree plot.phylo (tree, main="Phylogenetic Tree", use.edge.length = F) # add label mtext(text = "Shroom family gene tree - rooted, no branch lenths") We can include information about branch length by setting use.edge.length = ... to T. # plot tree plot.phylo (tree, main="Phylogenetic Tree", use.edge.length = T) # add label mtext(text = "Shroom family gene tree - rooted, with branch lenths") Some of the branches are now very short, but most are very long, indicating that these genes have been evolving independently for many millions of years. Let’s make a fancier plot. Don’t worry about all the steps; I’ve added some more code to add some annotations on the right-hand side to help us see what’s going on. plot(tree, main="Phylogenetic Tree") mtext(text = "Shroom family gene tree") x <- 0.551 x2 <- 0.6 # label Shrm 3 segments(x0 = x, y0 = 1, x1 = x, y1 = 4, lwd=2) text(x = x*1.01, y = 2.5, "Shrm 3",adj = 0) segments(x0 = x, y0 = 5, x1 = x, y1 = 6, lwd=2) text(x = x*1.01, y = 5.5, "Shrm 2",adj = 0) segments(x0 = x, y0 = 7, x1 = x, y1 = 9, lwd=2) text(x = x*1.01, y = 8, "Shrm 1",adj = 0) segments(x0 = x, y0 = 10, x1 = x, y1 = 13, lwd=2) text(x = x*1.01, y = 12, "Shrm ?",adj = 0) segments(x0 = x, y0 = 14, x1 = x, y1 = 15, lwd=2) text(x = x*1.01, y = 14.5, "Shrm 4",adj = 0) segments(x0 = x2, y0 = 1, x1 = x2, y1 = 6, lwd=2) segments(x0 = x2, y0 = 7, x1 = x2, y1 = 9, lwd=2) segments(x0 = x2, y0 = 10, x1 = x2, y1 = 15, lwd=2) "],["logarithms-in-r.html", "Chapter 19 Logarithms in R", " Chapter 19 Logarithms in R By Nathan Brouwer Logging splits up multiplication into addition. So, log(m*n) is the same as log(m) + log(n) You can check this m<-10 n<-11 log(m*n) ## [1] 4.70048 log(m)+log(n) ## [1] 4.70048 log(m*n) == log(m)+log(n) ## [1] TRUE Exponentiation undos logs exp(log(m*n)) ## [1] 110 m*n ## [1] 110 The key equation in BLAST’s E values is u = ln(Kmn)/lambda This can be changed to [ln(K) + ln(mn)]/lambda We can check this K <- 1 m <- 10 n <- 11 lambda <- 110 log(K*m*n)/lambda ## [1] 0.04273164 (log(K) + log(m*n))/lambda ## [1] 0.04273164 log(K*m*n)/lambda == (log(K) + log(m*n))/lambda ## [1] TRUE "],["appendix-01-getting-access-to-r.html", "Appendix 01: Getting access to R 19.1 Getting Started With R and RStudio", " Appendix 01: Getting access to R 19.1 Getting Started With R and RStudio R is a piece of software that does calculations and makes graphs. RStudio is a GUI (graphical user interface) that acts as a front-end to R Your can use R directly, but most people use a GUI of some kind RStudio has become the most popular GUI The following instructions will lead you click by click through downloading R and RStudio and starting an initial session. If you have trouble with downloading either program go to YouTube and search for something like “Downloading R” or “Installing RStudio” and you should be able to find something helpful, such as “How to Download R for Windows”. 19.1.1 RStudio Cloud TODO: Add RStudio cloud 19.1.2 Getting R onto your own computer To get R on to your computer first go to the CRAN website at https://cran.r-project.org/ (CRAN stands for “comprehensive R Archive Network”). At the top of the screen are three bullet points; select the appropriate one (or click the link below) Download R for Linux Download R for (Mac) OS X Download R for Windows Each page is formatted slightly differently. For a current Mac, click on the top link, which as of 8/16/2018 was “R-3.5.1.pkg” or click this link. If you have an older Mac you might have to scroll down to find your operating system under “Binaries for legacy OS X systems.” For PC select “base” or click this link. When its downloaded, run the installer and accept the defaults. 19.1.3 Getting RStudio onto your computer RStudio is an R interface developed by a company of the same name. RStudio has a number of commercial products, but much of their portfolio is freeware. You can download RStudio from their website www.rstudio.com/ . The download page (www.rstudio.com/products/rstudio/download/) is a bit busy because it shows all of their commercial products; the free version is on the far left side of the table of products. Click on the big green DOWNLOAD button under the column on the left that says “RStudio Desktop Open Source License” (or click on this link ). This will scroll you down to a list of downloads titled “Installers for Supported Platforms.” Windows users can select the top option RStudio 1.1.456 - Windows Vista/7/8/10 and Mac the second option RStudio 1.1.456 - Mac OS X 10.6+ (64-bit). (Versions names are current of 8/16/2018). Run the installer after it downloads and accept the default. RStudio will automatically link up with the most current version of R you have on your computer. Find the RStudio icon on your desktop or search for “RStudio” from your task bar and you’ll be read to go. 19.1.4 Keep R and RStudio current Both R and RStudio undergo regular updates and you will occasionally have to re-download and install one or both of them. In practice I probably do this about every 6 months. "],["getting-started-with-r-itself-or-not.html", "Getting started with R itself (or not) Vocabulary R commands 19.2 Help! 19.3 Other features of RStudio 19.4 Practice (OPTIONAL)", " Getting started with R itself (or not) Vocabulary console script editor / source viewer interactive programming scripts / script files .R files text files / plain text files command execution / execute a command from script editor comments / code comments commenting out / commenting out code stackoverflow.com the rstats hashtag R commands c(…) mean(…) sd(…) ? read.csv(…) This is a walk-through of a very basic R session. It assumes you have successfully installed R and RStudio onto your computer, and nothing else. Most people who use R do not actually use the program itself - they use a GUI (graphical user interface) “front end” that make R a bit easier to use. However, you will probably run into the icon for the underlying R program on your desktop or elsewhere on your computer. It usually looks like this: ADD IMAGE HERE The long string of numbers have to do with the version and whether is 32 or 64 bit (not important for what we do). If you are curious you can open it up and take a look - it actually looks a lot like RStudio, where we will do all our work (or rather, RStudio looks like R). Sometimes when people are getting started with R they will accidentally open R instead of RStudio; if things don’t seem to look or be working the way you think they should, you might be in R, not RStudio 19.1.4.1 R’s console as a scientific calculator You can interact with R’s console similar to a scientific calculator. For example, you can use parentheses to set up mathematical statements like 5*(1+1) ## [1] 10 Note however that you have to be explicit about multiplication. If you try the following it won’t work. 5(1+1) R also has built-in functions that work similar to what you might have used in Excel. For example, in Excel you can calculate the average of a set of numbers by typing “=average(1,2,3)” into a cell. R can do the same thing except The command is “mean” You don’t start with “=” You have to package up the numbers like what is shown below using “c(…)” mean(c(1,2,3)) ## [1] 2 Where “c(…)” packages up the numbers the way the mean() function wants to see them. If you just do the following R will give you an answer, but its the wrong one mean(1,2,3) This is a common issue with R – and many programs, really – it won’t always tell you when somethind didn’t go as planned. This is because it doesn’t know something didn’t go as planned; you have to learn the rules R plays by. 19.1.4.2 Practice: math in the console See if you can reproduce the following results Division 10/3 ## [1] 3.333333 The standard deviation sd(c(5,10,15)) # note the use of "c(...)" ## [1] 5 19.1.4.3 The script editor While you can interact with R directly within the console, the standard way to work in R is to write what are known as scripts. These are computer code instructions written to R in a script file. These are save with the extension .R but area really just a form of plain text file. To work with scripts, what you do is type commands in the script editor, then tell R to excute the command. This can be done several ways. First, you tell RStudio the line of code you want to run by either * Placing the cursor at the end a line of code, OR * Clicking and dragging over the code you want to run in order highlight it. Second, you tell RStudio to run the code by * Clicking the “Run” icon in the upper right hand side of the script editor (a grey box with a green error emerging from it) * pressing the control key (“ctrl)” and then then enter key on the keyboard The code you’ve chosen to run will be sent by RStudio from the script editor over to the console. The console will show you both the code and then the output. You can run several lines of code if you want; the console will run a line, print the output, and then run the next line. First I’ll use the command mean(), and then the command sd() for the standard deviation: mean(c(1,2,3)) ## [1] 2 sd(c(1,2,3)) ## [1] 1 19.1.4.4 Comments One of the reasons we use script files is that we can combine R code with comments that tell us what the R code is doing. Comments are preceded by the hashtag symbol #. Frequently we’ll write code like this: #The mean of 3 numbers mean(c(1,2,3)) If you highlight all of this code (including the comment) and then click on “run”, you’ll see that RStudio sends all of the code over console. ## [1] 2 Comments can also be placed at the end of a line of code mean(c(1,2,3)) #Note the use of c(...) Sometimes we write code and then don’t want R to run it. We can prevent R from executing the code even if its sent to the console by putting a “#” infront of the code. If I run this code, I will get just the mean but not the sd. mean(c(1,2,3)) #sd(c(1,2,3)) Doing this is called commenting out a line of code. 19.2 Help! There are many resource for figuring out R and RStudio, including R’s built in “help” function Q&A websites like stackoverflow.com twitter, using the hashtag #rstats blogs online books and course materials 19.2.1 Getting “help” from R If you are using a function in R you can get info about how it works like this ?mean In RStudio the help screen should appear, probably above your console. If you start reading this help file, though, you don’t have to go far until you start seeing lots of R lingo, like “S3 method”,“na.rm”, “vectors”. Unfortunately, the R help files are usually not written for beginners, and reading help files is a skill you have to acquire. For example, when we load data into R in subsequent lessons we will use a function called “read.csv” Access the help file by typing “?read.csv” into the console and pressing enter. Surprisingly, the function that R give you the help file isn’t what you asked for, but is read.table(). This is a related function to read.csv, but when you’re a beginner thing like this can really throw you off. Kieran Healy as produced a great cheatsheet for reading R’s help pages as part of his forthcoming book. It should be available online at http://socviz.co/appendix.html#a-little-more-about-r 19.2.2 Getting help from the internet The best way to get help for any topic is to just do an internet search like this: “R read.csv”. Usually the first thing on the results list will be the R help file, but the second or third will be a blog post or something else where a usually helpful person has discussed how that function works. Sometimes for very basic R commands like this might not always be productive but its always work a try. For but things related to stats, plotting, and programming there is frequently lots of information. Also try searching YouTube. 19.2.3 Getting help from online forums Often when you do an internet search for an R topic you’ll see results from the website www.stackoverflow.com, or maybe www.crossvalidated.com if its a statistics topic. These are excellent resources and many questions that you may have already have answers on them. Stackoverflow has an internal search function and also suggests potentially relevant posts. Before posting to one of these sites yourself, however, do some research; there is a particular type and format of question that is most likely to get a useful response. Sadly, people new to the site often get “flamed” by impatient pros. 19.2.4 Getting help from twitter Twitter is a surprisingly good place to get information or to find other people knew to R. Its often most useful to ask people for learning resources or general reference, but you can also post direct questions and see if anyone responds, though usually its more advanced users who engage in twitter-based code discussion. A standard tweet might be “Hey #rstats twitter, am knew to #rstats and really stuck on some of the basics. Any suggestions for good resources for someone starting from scratch?” 19.3 Other features of RStudio 19.3.1 Ajusting pane the layout You can adjust the location of each of RStudio 4 window panes, as well as their size. To set the pane layout go to 1. ”Tools” on the top menu 1. ”Global options” 1. “Pane Layout” Use the drop-down menus to set things up. I recommend 1. Lower left: “Console”” 1. Top right: “Source” 1. Top left: “Plot, Packages, Help Viewer” 1. This will leave the “Environment…” panel in the lower right. 19.3.2 Adjusting size of windows You can clicked on the edge of a pane and adjust its size. For most R work we want the console to be big. For beginners, the “Environment, history, files” panel can be made really small. 19.4 Practice (OPTIONAL) Practice the following operations. Type the directly into the console and execute them. Also write them in a script in the script editor and run them. Square roots sqrt(42) ## [1] 6.480741 The date Some functions in R can be executed within nothing in the parentheses. date() ## [1] "Sat Sep 18 01:23:04 2021" Exponents The ^ is used for exponents 42^2 ## [1] 1764 A series of numbers A colon between two numbers creates a series of numbers. 1:42 ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ## [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 logs The default for the log() function is the natural log. log(42) ## [1] 3.73767 log10() gives the base-10 log. log10(42) ## [1] 1.623249 exp() raises e to a power exp(3.73767) ## [1] 42.00002 Multiple commands can be nested sqrt(42)^2 log(sqrt(42)^2) exp(log(sqrt(42)^2)) "]] diff --git a/docs/vectors-in-R.html b/docs/vectors-in-R.html index 98a4fda..2552d76 100644 --- a/docs/vectors-in-R.html +++ b/docs/vectors-in-R.html @@ -265,10 +265,12 @@
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      diff --git a/docs/worked-example-building-a-phylogeny-in-r.html b/docs/worked-example-building-a-phylogeny-in-r.html index 476c0ce..0c5ddb5 100644 --- a/docs/worked-example-building-a-phylogeny-in-r.html +++ b/docs/worked-example-building-a-phylogeny-in-r.html @@ -265,10 +265,12 @@
  • 12 NCBI: The National Center for Biotechnology Information
  • 13 Introduction to biological sequences databases
      @@ -494,17 +496,17 @@

      18.2.4 Load packages into memory<

      We now need to load up all our bioinformatics and phylogenetics software into R. This is done with the library() command.

      To run this code just clock on the sideways green triangle all the way to the right of the code.

      NOTE: You’ll likely see some red code appear on your screen. No worries, totally normal!

      -
      # github packages
-library(compbio4all)
-
-# CRAN packages
-library(rentrez)
-library(seqinr)
-library(ape)
-
-# Bioconductor packages
-library(msa)
-library(Biostrings)
      +
      # github packages
+library(compbio4all)
+
+# CRAN packages
+library(rentrez)
+library(seqinr)
+library(ape)
+
+# Bioconductor packages
+library(msa)
+library(Biostrings)
      @@ -536,12 +538,12 @@

      18.3 Downloading macro-molecular 1. ASD 2: aa 1671 to aa 1955 in hShroom3

      Normally we’d have to download these sequences by hand through pointing and clicking on GeneBank records on the NCBI website. In R we can do it automatically; this might take a second.

      All the code needed is this:

      -
      # Human shroom 3 (H. sapiens)
-hShroom3 <- entrez_fetch(db = "protein", 
-                          id = "NP_065910", 
-                          rettype = "fasta")
      +
      # Human shroom 3 (H. sapiens)
+hShroom3 <- entrez_fetch(db = "protein", 
+                          id = "NP_065910", 
+                          rettype = "fasta")

      The output is in FASTA format; we’ll use the cat() to do a little formatting for us:

      -
      cat(hShroom3)
      +
      cat(hShroom3)
      ## >NP_065910.3 protein Shroom3 [Homo sapiens]
 ## MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKL
 ## QAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAA
@@ -574,29 +576,29 @@ 
      18.3 Downloading macro-molecular
 ## IPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL

      Note the initial >, then the header line of NP_065910.3 protein Shroom3 [Homo sapiens]. After that is the amino acid sequence. The underlying data also includes the newline character \n to designate where each line of amino acids stops.

      We can get the rest of the data by just changing the id = ... argument:

      -
      # Mouse shroom 3a (M. musculus)
-mShroom3a <- entrez_fetch(db = "protein", 
-                          id = "AAF13269", 
-                          rettype = "fasta")
-
-# Human shroom 2 (H. sapiens)
-hShroom2 <- entrez_fetch(db = "protein", 
-                          id = "CAA58534", 
-                          rettype = "fasta")
-
-
-# Sea-urchin shroom
-sShroom <- entrez_fetch(db = "protein", 
-                          id = "XP_783573", 
-                          rettype = "fasta")
      +
      # Mouse shroom 3a (M. musculus)
+mShroom3a <- entrez_fetch(db = "protein", 
+                          id = "AAF13269", 
+                          rettype = "fasta")
+
+# Human shroom 2 (H. sapiens)
+hShroom2 <- entrez_fetch(db = "protein", 
+                          id = "CAA58534", 
+                          rettype = "fasta")
+
+
+# Sea-urchin shroom
+sShroom <- entrez_fetch(db = "protein", 
+                          id = "XP_783573", 
+                          rettype = "fasta")

      I’m going to check about how long each of these sequences is - each should have an at least slightly different length. If any are identical, I might have repeated an accession name or re-used an object name. The function nchar() counts of the number of characters in an R object.

      -
      nchar(hShroom3)
      +
      nchar(hShroom3)
      ## [1] 2070
      -
      nchar(mShroom3a)
      +
      nchar(mShroom3a)
      ## [1] 2083
      -
      nchar(sShroom)
      +
      nchar(sShroom)
      ## [1] 1758
      -
      nchar(hShroom2)
      +
      nchar(hShroom2)
      ## [1] 1673

      @@ -611,7 +613,7 @@

      18.4 Prepping macromolecular sequ

      We can remove this non-sequence information using a function I wrote called fasta_cleaner(), which is in the compbio4all package. The function uses regular expressions to remove the info we don’t need.

      ASIDE: If we run the name of the command with out any quotation marks we can see the code:

      -
      fasta_cleaner
      +
      fasta_cleaner
      ## function(fasta_object, parse = TRUE){
 ## 
 ##   fasta_object <- sub("^(>)(.*?)(\\n)(.*)(\\n\\n)","\\4",fasta_object)
@@ -625,87 +627,87 @@ 
      18.4 Prepping macromolecular sequ
 ## 
 ##   return(fasta_object[[1]])
 ## }
-## <bytecode: 0x7f81ca9a24b8>
+## <bytecode: 0x7fcccbb38a70>
 ## <environment: namespace:compbio4all>

      End ASIDE

      Now use the function to clean our sequences; we won’t worry about what pare = ... is for.

      -
      hShroom3  <- fasta_cleaner(hShroom3,  parse = F)
-mShroom3a <- fasta_cleaner(mShroom3a, parse = F)
-hShroom2  <- fasta_cleaner(hShroom2,  parse = F)
-sShroom   <- fasta_cleaner(sShroom,   parse = F)
      +
      hShroom3  <- fasta_cleaner(hShroom3,  parse = F)
+mShroom3a <- fasta_cleaner(mShroom3a, parse = F)
+hShroom2  <- fasta_cleaner(hShroom2,  parse = F)
+sShroom   <- fasta_cleaner(sShroom,   parse = F)

      Now let’s take a peek at what our sequences look like:

      -
      hShroom3
      +
      hShroom3
      ## [1] "MMRTTEDFHKPSATLNSNTATKGRYIYLEAFLEGGAPWGFTLKGGLEHGEPLIISKVEEGGKADTLSSKLQAGDEVVHINEVTLSSSRKEAVSLVKGSYKTLRLVVRRDVCTDPGHADTGASNFVSPEHLTSGPQHRKAAWSGGVKLRLKHRRSEPAGRPHSWHTTKSGEKQPDASMMQISQGMIGPPWHQSYHSSSSTSDLSNYDHAYLRRSPDQCSSQGSMESLEPSGAYPPCHLSPAKSTGSIDQLSHFHNKRDSAYSSFSTSSSILEYPHPGISGRERSGSMDNTSARGGLLEGMRQADIRYVKTVYDTRRGVSAEYEVNSSALLLQGREARASANGQGYDKWSNIPRGKGVPPPSWSQQCPSSLETATDNLPPKVGAPLPPARSDSYAAFRHRERPSSWSSLDQKRLCRPQANSLGSLKSPFIEEQLHTVLEKSPENSPPVKPKHNYTQKAQPGQPLLPTSIYPVPSLEPHFAQVPQPSVSSNGMLYPALAKESGYIAPQGACNKMATIDENGNQNGSGRPGFAFCQPLEHDLLSPVEKKPEATAKYVPSKVHFCSVPENEEDASLKRHLTPPQGNSPHSNERKSTHSNKPSSHPHSLKCPQAQAWQAGEDKRSSRLSEPWEGDFQEDHNANLWRRLEREGLGQSLSGNFGKTKSAFSSLQNIPESLRRHSSLELGRGTQEGYPGGRPTCAVNTKAEDPGRKAAPDLGSHLDRQVSYPRPEGRTGASASFNSTDPSPEEPPAPSHPHTSSLGRRGPGPGSASALQGFQYGKPHCSVLEKVSKFEQREQGSQRPSVGGSGFGHNYRPHRTVSTSSTSGNDFEETKAHIRFSESAEPLGNGEQHFKNGELKLEEASRQPCGQQLSGGASDSGRGPQRPDARLLRSQSTFQLSSEPEREPEWRDRPGSPESPLLDAPFSRAYRNSIKDAQSRVLGATSFRRRDLELGAPVASRSWRPRPSSAHVGLRSPEASASASPHTPRERHSVTPAEGDLARPVPPAARRGARRRLTPEQKKRSYSEPEKMNEVGIVEEAEPAPLGPQRNGMRFPESSVADRRRLFERDGKACSTLSLSGPELKQFQQSALADYIQRKTGKRPTSAAGCSLQEPGPLRERAQSAYLQPGPAALEGSGLASASSLSSLREPSLQPRREATLLPATVAETQQAPRDRSSSFAGGRRLGERRRGDLLSGANGGTRGTQRGDETPREPSSWGARAGKSMSAEDLLERSDVLAGPVHVRSRSSPATADKRQDVLLGQDSGFGLVKDPCYLAGPGSRSLSCSERGQEEMLPLFHHLTPRWGGSGCKAIGDSSVPSECPGTLDHQRQASRTPCPRPPLAGTQGLVTDTRAAPLTPIGTPLPSAIPSGYCSQDGQTGRQPLPPYTPAMMHRSNGHTLTQPPGPRGCEGDGPEHGVEEGTRKRVSLPQWPPPSRAKWAHAAREDSLPEESSAPDFANLKHYQKQQSLPSLCSTSDPDTPLGAPSTPGRISLRISESVLRDSPPPHEDYEDEVFVRDPHPKATSSPTFEPLPPPPPPPPSQETPVYSMDDFPPPPPHTVCEAQLDSEDPEGPRPSFNKLSKVTIARERHMPGAAHVVGSQTLASRLQTSIKGSEAESTPPSFMSVHAQLAGSLGGQPAPIQTQSLSHDPVSGTQGLEKKVSPDPQKSSEDIRTEALAKEIVHQDKSLADILDPDSRLKTTMDLMEGLFPRDVNLLKENSVKRKAIQRTVSSSGCEGKRNEDKEAVSMLVNCPAYYSVSAPKAELLNKIKEMPAEVNEEEEQADVNEKKAELIGSLTHKLETLQEAKGSLLTDIKLNNALGEEVEALISELCKPNEFDKYRMFIGDLDKVVNLLLSLSGRLARVENVLSGLGEDASNEERSSLYEKRKILAGQHEDARELKENLDRRERVVLGILANYLSEEQLQDYQHFVKMKSTLLIEQRKLDDKIKLGQEQVKCLLESLPSDFIPKAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL"

      18.5 Aligning sequences

      We can do a global alignment of one sequence against another using the pairwiseAlignment() function from the Bioconductor package Biostrings (note that capital “B” in Biostrings; most R package names are all lower case, but not this one).

      Let’s align human versus mouse shroom:

      -
      align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
-                  hShroom3,
-                  mShroom3a)
      +
      align.h3.vs.m3a <- Biostrings::pairwiseAlignment(
+                  hShroom3,
+                  mShroom3a)

      We can peek at the alignment

      -
      align.h3.vs.m3a
      +
      align.h3.vs.m3a
      ## Global PairwiseAlignmentsSingleSubject (1 of 1)
 ## pattern: MMRTTEDFHKPSATLN-SNTATKGRYIYLEAFLE...KAGALALPPNLTSEPIPAGGCTFSGIFPTLTSPL
 ## subject: MK-TPENLEEPSATPNPSRTPTE-RFVYLEALLE...KAGAISLPPALTGHATPGGTSVFGGVFPTLTSPL
 ## score: 2189.934

      The score tells us how closely they are aligned; higher scores mean the sequences are more similar. Its hard to interpret the number on its own so we can get the percent sequence identity (PID) using the pid() function.

      -
      Biostrings::pid(align.h3.vs.m3a)
      +
      Biostrings::pid(align.h3.vs.m3a)
      ## [1] 70.56511

      So, shroom3 from humans and shroom3 from mice are ~71% similar (at least using this particular method of alignment, and there are many ways to do this!)

      What about human shroom 3 and sea-urchin shroom?

      -
      align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
-                  hShroom3,
-                  hShroom2)
      +
      align.h3.vs.h2 <- Biostrings::pairwiseAlignment(
+                  hShroom3,
+                  hShroom2)

      First check out the score using score(), which accesses it directly without all the other information.

      -
      score(align.h3.vs.h2)
      +
      score(align.h3.vs.h2)
      ## [1] -5673.853

      Now the percent sequence alignment with pid():

      -
      Biostrings::pid(align.h3.vs.h2)
      +
      Biostrings::pid(align.h3.vs.h2)
      ## [1] 33.83277

      So Human shroom 3 and Mouse shroom 3 are 71% identical, but Human shroom 3 and human shroom 2 are only 34% similar? How does it work out evolutionary that a human and mouse gene are more similar than a human and a human gene? What are the evolutionary relationships among these genes within the shroom gene family?

      18.6 The shroom family of genes

      I’ve copied a table from a published paper which has accession numbers for 15 different Shroom genes.

      -
      shroom_table <- c("CAA78718" , "X. laevis Apx" ,         "xShroom1",
-            "NP_597713" , "H. sapiens APXL2" ,     "hShroom1",
-            "CAA58534" , "H. sapiens APXL",        "hShroom2",
-            "ABD19518" , "M. musculus Apxl" ,      "mShroom2",
-            "AAF13269" , "M. musculus ShroomL" ,   "mShroom3a",
-            "AAF13270" , "M. musculus ShroomS" ,   "mShroom3b",
-            "NP_065910", "H. sapiens Shroom" ,     "hShroom3",
-            "ABD59319" , "X. laevis Shroom-like",  "xShroom3",
-            "NP_065768", "H. sapiens KIAA1202" ,   "hShroom4a",
-            "AAK95579" , "H. sapiens SHAP-A" ,     "hShroom4b",
-            #"DQ435686" , "M. musculus KIAA1202" ,  "mShroom4",
-            "ABA81834" , "D. melanogaster Shroom", "dmShroom",
-            "EAA12598" , "A. gambiae Shroom",      "agShroom",
-            "XP_392427" , "A. mellifera Shroom" ,  "amShroom",
-            "XP_783573" , "S. purpuratus Shroom" , "spShroom") #sea urchin
      +
      shroom_table <- c("CAA78718" , "X. laevis Apx" ,         "xShroom1",
+            "NP_597713" , "H. sapiens APXL2" ,     "hShroom1",
+            "CAA58534" , "H. sapiens APXL",        "hShroom2",
+            "ABD19518" , "M. musculus Apxl" ,      "mShroom2",
+            "AAF13269" , "M. musculus ShroomL" ,   "mShroom3a",
+            "AAF13270" , "M. musculus ShroomS" ,   "mShroom3b",
+            "NP_065910", "H. sapiens Shroom" ,     "hShroom3",
+            "ABD59319" , "X. laevis Shroom-like",  "xShroom3",
+            "NP_065768", "H. sapiens KIAA1202" ,   "hShroom4a",
+            "AAK95579" , "H. sapiens SHAP-A" ,     "hShroom4b",
+            #"DQ435686" , "M. musculus KIAA1202" ,  "mShroom4",
+            "ABA81834" , "D. melanogaster Shroom", "dmShroom",
+            "EAA12598" , "A. gambiae Shroom",      "agShroom",
+            "XP_392427" , "A. mellifera Shroom" ,  "amShroom",
+            "XP_783573" , "S. purpuratus Shroom" , "spShroom") #sea urchin

      I’ll do a bit of formatting; you can ignore these details if you want

      -
      # convert to matrix
-shroom_table_matrix <- matrix(shroom_table,
-                                  byrow = T,
-                                  nrow = 14)
-# convert to dataframe
-shroom_table <- data.frame(shroom_table_matrix, 
-                     stringsAsFactors = F)
-
-# name columns
-names(shroom_table) <- c("accession", "name.orig","name.new")
-
-# Create simplified species names
-shroom_table$spp <- "Homo"
-shroom_table$spp[grep("laevis",shroom_table$name.orig)] <- "Xenopus"
-shroom_table$spp[grep("musculus",shroom_table$name.orig)] <- "Mus"
-shroom_table$spp[grep("melanogaster",shroom_table$name.orig)] <- "Drosophila"
-shroom_table$spp[grep("gambiae",shroom_table$name.orig)] <- "mosquito"
-shroom_table$spp[grep("mellifera",shroom_table$name.orig)] <- "bee"
-shroom_table$spp[grep("purpuratus",shroom_table$name.orig)] <- "sea urchin"
      +
      # convert to matrix
+shroom_table_matrix <- matrix(shroom_table,
+                                  byrow = T,
+                                  nrow = 14)
+# convert to dataframe
+shroom_table <- data.frame(shroom_table_matrix, 
+                     stringsAsFactors = F)
+
+# name columns
+names(shroom_table) <- c("accession", "name.orig","name.new")
+
+# Create simplified species names
+shroom_table$spp <- "Homo"
+shroom_table$spp[grep("laevis",shroom_table$name.orig)] <- "Xenopus"
+shroom_table$spp[grep("musculus",shroom_table$name.orig)] <- "Mus"
+shroom_table$spp[grep("melanogaster",shroom_table$name.orig)] <- "Drosophila"
+shroom_table$spp[grep("gambiae",shroom_table$name.orig)] <- "mosquito"
+shroom_table$spp[grep("mellifera",shroom_table$name.orig)] <- "bee"
+shroom_table$spp[grep("purpuratus",shroom_table$name.orig)] <- "sea urchin"

      Take a look:

      -
      shroom_table
      +
      shroom_table
      ##    accession              name.orig  name.new        spp
 ## 1   CAA78718          X. laevis Apx  xShroom1    Xenopus
 ## 2  NP_597713       H. sapiens APXL2  hShroom1       Homo
@@ -725,41 +727,41 @@ 
      18.6 The shroom family of genes
 
      18.7 Downloading multiple sequences
      
 
      Instead of getting one sequence at a time we can download several by accessing the “accession” column from the table
      
-
      shroom_table$accession
      
+
      shroom_table$accession
      
 
      ##  [1] "CAA78718"  "NP_597713" "CAA58534"  "ABD19518"  "AAF13269"  "AAF13270" 
 ##  [7] "NP_065910" "ABD59319"  "NP_065768" "AAK95579"  "ABA81834"  "EAA12598" 
 ## [13] "XP_392427" "XP_783573"
      
 
      We can give this whole set of accessions to entrez_fetch():
      
-
      shrooms <- entrez_fetch(db = "protein", 
-                          id = shroom_table$accession, 
-                          rettype = "fasta")
      
+
      shrooms <- entrez_fetch(db = "protein", 
+                          id = shroom_table$accession, 
+                          rettype = "fasta")
      
 
      We can look at what we got here with cat() (I won’t display this because it is very long!)
      
-
      cat(shrooms)
      
+
      cat(shrooms)
      
 
      The current format of these data is a single, long set of data. This is a standard way to store, share and transmit FASTA files, but in R we’ll need a slightly different format.
      
 
      We’ll download all of the sequences again, this time using a function from compbio4all called entrez_fetch_list() which is a wrapper function I wrote to put the output of entrez_fetch() into an R data format called a list.
      
-
      shrooms_list <- entrez_fetch_list(db = "protein", 
-                          id = shroom_table$accession, 
-                          rettype = "fasta")
      
+
      shrooms_list <- entrez_fetch_list(db = "protein", 
+                          id = shroom_table$accession, 
+                          rettype = "fasta")
      
 
      Now we have a list which as 14 elements, one for each sequence in our table.
      
-
      length(shrooms_list)
      
+
      length(shrooms_list)
      
 
      ## [1] 14
      
 
      We now need to clean up each one fo these sequences. We can do that using a simple for() loop:
      
-
      for(i in 1:length(shrooms_list)){
-  shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F)
-}
      
+
      for(i in 1:length(shrooms_list)){
+  shrooms_list[[i]] <- fasta_cleaner(shrooms_list[[i]], parse = F)
+}
      
 
      Second to last step: we need to take each one of our sequences from our list and put it into a vector, in particular a named vector
      
-
      # make a vector to store output
-shrooms_vector <- rep(NA, length(shrooms_list))
-
-# run the loop
-for(i in 1:length(shrooms_vector)){
-  shrooms_vector[i] <- shrooms_list[[i]]
-}
-
-# name the vector
-names(shrooms_vector) <- names(shrooms_list)
      
+
      # make a vector to store output
+shrooms_vector <- rep(NA, length(shrooms_list))
+
+# run the loop
+for(i in 1:length(shrooms_vector)){
+  shrooms_vector[i] <- shrooms_list[[i]]
+}
+
+# name the vector
+names(shrooms_vector) <- names(shrooms_list)
      
 
      Now the final step: we need to convert our named vector to a string set using Biostrings::AAStringSet(). Note the _ss tag at the end of the object we’re assigning the output to, which designates this as a string set.
      
-
      shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector)
      
+
      shrooms_vector_ss <- Biostrings::AAStringSet(shrooms_vector)

      18.8 Multiple sequence alignment

      @@ -767,8 +769,8 @@

      18.8 Multiple sequence alignment<

      18.8.1 Building an Multiple Sequence Alignment (MSA)

      We’ll use the software msa, which implements the ClustalW multiple sequence alignment algorithm. Normally we’d have to download the ClustalW program and either point-and-click our way through it or use the command line*, but these folks wrote up the algorithm in R so we can do this with a line of R code. This will take a second or two.

      -
      shrooms_align <- msa(shrooms_vector_ss,
-                     method = "ClustalW")
      +
      shrooms_align <- msa(shrooms_vector_ss,
+                     method = "ClustalW")
      ## use default substitution matrix
      @@ -777,7 +779,7 @@

      18.8.2 Viewing an MSA

      18.8.2.1 Viewing an MSA in R

      We can look at the output from msa(), but its not very helpful

      -
      shrooms_align
      +
      shrooms_align
      ## CLUSTAL 2.1  
 ## 
 ## Call:
@@ -802,19 +804,19 @@ 
      18.8.2.1 Viewing an MSA in R
      
 ##  Con -------------------------...------------------------- Consensus

      A function called print_msa() (Coghlan 2011) which I’ve put intocombio4all can give us more informative output by printing out the actual alignment into the R console.

      To use print_msa() We need to make a few minor tweaks though first. These are behind the scenes changes so don’t worry about the details right now. We’ll change the name to shrooms_align_seqinr to indicate that one of our changes is putting this into a format defined by the bioinformatics package seqinr.

      -
      class(shrooms_align) <- "AAMultipleAlignment"
-shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")
      +
      class(shrooms_align) <- "AAMultipleAlignment"
+shrooms_align_seqinr <- msaConvert(shrooms_align, type = "seqinr::alignment")

      I won’t display the output from shrooms_align_seqinr because its very long; we have 14 shroom genes, and shroom happens to be a rather long gene.

      -
      print_msa(alignment = shrooms_align_seqinr, 
-          chunksize = 60)
      +
      print_msa(alignment = shrooms_align_seqinr, 
+          chunksize = 60)

      18.8.2.2 Displaying an MSA as an R plot

      I’m going to just show about 100 amino acids near the end of the alignment, where there is the most overlap across all of the sequences. This is set with the start = ... and end = ... arguments. Note that we’re using the shrooms_align object.

      -
      ggmsa::ggmsa(shrooms_align,   # shrooms_align, NOT shrooms_align_seqinr
-      start = 2000, 
-      end = 2100)
      +
      ggmsa::ggmsa(shrooms_align,   # shrooms_align, NOT shrooms_align_seqinr
+      start = 2000, 
+      end = 2100) 
      ## Registered S3 methods overwritten by 'ggalt':
 ##   method                  from   
 ##   grid.draw.absoluteGrob  ggplot2
@@ -822,17 +824,17 @@ 
      18.8.2.2 Displaying an MSA as an
 ##   grobWidth.absoluteGrob  ggplot2
 ##   grobX.absoluteGrob      ggplot2
 ##   grobY.absoluteGrob      ggplot2
      -

      +

      18.8.2.3 Saving an MSA as PDF

      We can take a look at the alignment in PDF format if we want. I this case I’m going to just show about 100 amino acids near the end of the alignment, where there is the most overlap across all of the sequences. This is set with the y = c(...) argument.

      -
      msaPrettyPrint(shrooms_align,             # alignment
-               file = "shroom_msa.pdf",   # file name
-               y=c(2000, 2100),           # range
-               askForOverwrite=FALSE)
      +
      msaPrettyPrint(shrooms_align,             # alignment
+               file = "shroom_msa.pdf",   # file name
+               y=c(2000, 2100),           # range
+               askForOverwrite=FALSE)

      You can see where R is saving things by running getwd()

      -
      getwd()
      +
      getwd()
      ## [1] "/Users/nlb24/OneDrive - University of Pittsburgh/0-books/lbrb-bk/lbrb"

      On a Mac you can usually find the file by searching in Finder for the file name, which I set to be “shroom_msa.pdf” using the file = ... argument above.

      @@ -842,13 +844,13 @@

      18.8.2.3 Saving an MSA as PDF

      18.9 Genetic distance.

      Next need to first get an estimate of how similar each sequences is. The more amino acids that are identical to each other, the more similar.

      Instead of similarity, we usually work in terms of difference or genetic distance (a.k.a. evolutionary distance). This is done with the dist.alignment() function.

      -
      shrooms_dist <- seqinr::dist.alignment(shrooms_align_seqinr, 
-                                       matrix = "identity")
      +
      shrooms_dist <- seqinr::dist.alignment(shrooms_align_seqinr, 
+                                       matrix = "identity")

      We’ve made a matrix using dist.alignment(); let’s round it off so its easier to look at using the round() function.

      -
      shrooms_dist_rounded <- round(shrooms_dist,
-                              digits = 3)
      +
      shrooms_dist_rounded <- round(shrooms_dist,
+                              digits = 3)

      Now let’s look at it

      -
      shrooms_dist_rounded
      +
      shrooms_dist_rounded
      ##           NP_065768 AAK95579 AAF13269 AAF13270 NP_065910 ABD59319 CAA58534
 ## AAK95579      0.000                                                       
 ## AAF13269      0.884    0.917                                              
@@ -882,84 +884,84 @@ 
      18.9 Genetic distance.
      
 
      18.10 Phylognetic trees (finally!)
      
 
      We got our sequence, built multiple sequence alignment, and calculated the genetic distance between sequences. Now we are - finally - ready to build a phylogenetic tree.
      
 
      First, we let R figure out the structure of the tree. There are MANY ways to build phylogenetic trees. We’ll use a common one used for exploring sequences called neighbor joining algorithm via the function nj(). Neighbor joining uses genetic distances to cluster sequences into clades.
      
-
      tree <- nj(shrooms_dist)
      
+
      tree <- nj(shrooms_dist)
      
 
      
 
      18.10.1 Plotting phylogenetic trees
      
 
      Now we’ll make a quick plot of our tree using plot() (and add a little label using a function called mtext()).
      
-
      # plot tree
-plot.phylo (tree, main="Phylogenetic Tree", 
-            type = "unrooted", 
-            use.edge.length = F)
-
-# add label
-mtext(text = "Shroom family gene tree - unrooted, no branch lengths")
      
-
      
-
      This is an **unrooted tree*. For the sake of plotting we’ve also ignored the evolutionary distance between the sequences.
      
-
      To make a rooted tree we remove type = "unrooted.
      
-
      # plot tree
-plot.phylo (tree, main="Phylogenetic Tree", 
-            use.edge.length = F)
-
-# add label
-mtext(text = "Shroom family gene tree - rooted, no branch lenths")
      
-
      
-
      We can include information about branch length by setting use.edge.length = ... to T.
      
 
      # plot tree
 plot.phylo (tree, main="Phylogenetic Tree", 
-            use.edge.length = T)
-
-# add label
-mtext(text = "Shroom family gene tree - rooted, with branch lenths")
      
+            type = "unrooted", 
+            use.edge.length = F)
+
+# add label
+mtext(text = "Shroom family gene tree - unrooted, no branch lengths")

      +

      This is an **unrooted tree*. For the sake of plotting we’ve also ignored the evolutionary distance between the sequences.

      +

      To make a rooted tree we remove type = "unrooted.

      +
      # plot tree
+plot.phylo (tree, main="Phylogenetic Tree", 
+            use.edge.length = F)
+
+# add label
+mtext(text = "Shroom family gene tree - rooted, no branch lenths")
      +

      +

      We can include information about branch length by setting use.edge.length = ... to T.

      +
      # plot tree
+plot.phylo (tree, main="Phylogenetic Tree", 
+            use.edge.length = T)
+
+# add label
+mtext(text = "Shroom family gene tree - rooted, with branch lenths")
      +

      Some of the branches are now very short, but most are very long, indicating that these genes have been evolving independently for many millions of years.

      Let’s make a fancier plot. Don’t worry about all the steps; I’ve added some more code to add some annotations on the right-hand side to help us see what’s going on.

      -
      plot(tree, main="Phylogenetic Tree")
-mtext(text = "Shroom family gene tree")
-
-x <- 0.551
-x2 <- 0.6
-
-# label Shrm 3
-segments(x0 = x, y0 = 1, 
-         x1 = x, y1 = 4,
-         lwd=2)
-text(x = x*1.01, y = 2.5, "Shrm 3",adj = 0)
-
-segments(x0 = x, y0 = 5, 
-         x1 = x, y1 = 6,
-         lwd=2)
-text(x = x*1.01, y = 5.5, "Shrm 2",adj = 0)
-
-segments(x0 = x, y0 = 7, 
-         x1 = x, y1 = 9,
-         lwd=2)
-text(x = x*1.01, y = 8, "Shrm 1",adj = 0)
-
-segments(x0 = x, y0 = 10, 
-         x1 = x, y1 = 13,
-         lwd=2)
-text(x = x*1.01, y = 12, "Shrm ?",adj = 0)
-
-
-segments(x0 = x, y0 = 14, 
-         x1 = x, y1 = 15,
-         lwd=2)
-text(x = x*1.01, y = 14.5, "Shrm 4",adj = 0)
-
-
-segments(x0 = x2, y0 = 1, 
-         x1 = x2, y1 = 6,
-         lwd=2)
-
-segments(x0 = x2, y0 = 7, 
-         x1 = x2, y1 = 9,
-         lwd=2)
-
-segments(x0 = x2, y0 = 10, 
-         x1 = x2, y1 = 15,
-         lwd=2)
      -

      +
      plot(tree, main="Phylogenetic Tree")
+mtext(text = "Shroom family gene tree")
+
+x <- 0.551
+x2 <- 0.6
+
+# label Shrm 3
+segments(x0 = x, y0 = 1, 
+         x1 = x, y1 = 4,
+         lwd=2)
+text(x = x*1.01, y = 2.5, "Shrm 3",adj = 0)
+
+segments(x0 = x, y0 = 5, 
+         x1 = x, y1 = 6,
+         lwd=2)
+text(x = x*1.01, y = 5.5, "Shrm 2",adj = 0)
+
+segments(x0 = x, y0 = 7, 
+         x1 = x, y1 = 9,
+         lwd=2)
+text(x = x*1.01, y = 8, "Shrm 1",adj = 0)
+
+segments(x0 = x, y0 = 10, 
+         x1 = x, y1 = 13,
+         lwd=2)
+text(x = x*1.01, y = 12, "Shrm ?",adj = 0)
+
+
+segments(x0 = x, y0 = 14, 
+         x1 = x, y1 = 15,
+         lwd=2)
+text(x = x*1.01, y = 14.5, "Shrm 4",adj = 0)
+
+
+segments(x0 = x2, y0 = 1, 
+         x1 = x2, y1 = 6,
+         lwd=2)
+
+segments(x0 = x2, y0 = 7, 
+         x1 = x2, y1 = 9,
+         lwd=2)
+
+segments(x0 = x2, y0 = 10, 
+         x1 = x2, y1 = 15,
+         lwd=2)
      +

      diff --git a/lbrb.Rmd b/lbrb.Rmd index 8de713f..6669f0e 100644 --- a/lbrb.Rmd +++ b/lbrb.Rmd @@ -1957,6 +1957,15 @@ Original content by OpenStax (CC BY 4.0; Download for free at **NOTE:** The following material was partially adapted by N. Brouwer from [Wikipedia](https://en.wikipedia.org/wiki/National_Center_for_Biotechnology_Information). See the underlying .Rmd file for information on specific paragraphs from Wikipedia. +## Key concepts + +* NCBI +* Rentrez +* accession numbers +* BLAST + +## NCBI + The **National Center for Biotechnology Information (NCBI)** is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It was founded in 1988 and is approved and funded by the government of the United States. The NCBI houses a series of **databases** relevant to the basic and applied life sciences and is an important resource for **bioinformatics** tools and services. Major databases include **GenBank** for DNA sequences and **PubMed**, a **bibliographic** database for biomedical literature. All these databases are available online through the **Entrez** search engine. @@ -1967,7 +1976,7 @@ In this chapter we'll briefly discuss the major databases, following up with spe The GenBank sequence database is an open access collection of publicly available DNA and protein sequences. If you've work with sequence data, you'll work with GenBank. GenBank is the actual database, and it can be searched several ways. For example, you can search for a sequence by its ID number (**accession number**) if you know it, or do a **BLAST search** using an actual sequence to look for similar sequences. -A key component of GenBank are the **GeneBank Records**, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene [pallysin](https://www.ncbi.nlm.nih.gov/protein/AAC65720) from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides **metadata**, such as the scientific name of the organism (*Treponema pallidum*), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene. +A key component of GenBank are the **GeneBank Records**, which are annotated summaries of sequences in the databases. For example, below is shown record for a gene [pallysin](https://www.ncbi.nlm.nih.gov/protein/AAC65720) https://www.ncbi.nlm.nih.gov/protein/AAC65720 from syphilis In addition to the actual A, T, C, and Gs of the sequences, the record provides **metadata**, such as the scientific name of the organism (*Treponema pallidum*), who did the sequencing, the name of the paper where the sequence was published, and important features of the gene. A key feature of PubMed records is that they are **hyperlinked** to other NCBI databases. For example, you can click link under the name of the paper which reported the sequence of the gene and it will take you to the PubMed record for that paper (see below). You can also click the "Run BLAST" link and you can search the database for similar sequences. This protein coded for by this particular gene has had its structured solved using x-ray crystallography, and you can see these results under "Protein 3D Structure." In a later chapter we'll get to know these records in further detail. @@ -1984,21 +1993,19 @@ knitr::include_graphics("images/genbank_record.png") ## Entrez -https://en.wikipedia.org/wiki/Entrez - + -"Entrez is a federated search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name "Entrez" (a greeting meaning "Come in" in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI." +Entrez is a search engine and web portal that allows users to search many discrete health sciences databases of the NCBI website. The name "Entrez" (a greeting meaning "Come in" in French) was chosen to reflect the spirit of welcoming the public to search the content available from NCBI. -"Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system." +Entrez Global Query is an integrated search and retrieval system that provides access to all databases simultaneously with a single query and user interface. Entrez can efficiently retrieve related sequences, structures, and references. The Entrez system can provide views of gene and protein sequences and chromosome maps. Some textbooks are also available online through the Entrez system. ## BLAST -https://en.wikipedia.org/wiki/BLAST_(biotechnology) - + -"BLAST (basic local alignment search tool)is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence." +BLAST (Basic Local Alignment Search Tool) is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences. A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query) with a library or database of sequences, and identify database sequences that resemble the query sequence above a certain threshold. For example, following the discovery of a previously unknown gene in the mouse, a scientist will typically perform a BLAST search of the human genome to see if humans carry a similar gene; BLAST will identify sequences in the human genome that resemble the mouse gene based on similarity of sequence. @@ -2037,7 +2044,7 @@ library(compbio4all) ## Introduction -**NCBI** is the National Center for Biotechnology Information. The [NCBI Webiste](www.ncbi.nlm.nih.gov/) is the entry point to a large number of databases giving access to **biological sequences** (DNA, RNA, protein) and biology-related publications. +**NCBI** is the National Center for Biotechnology Information. The [NCBI Webiste](https://www.ncbi.nlm.nih.gov/) www.ncbi.nlm.nih.gov/ is the entry point to a large number of databases giving access to **biological sequences** (DNA, RNA, protein) and biology-related publications. When scientists sequence DNA, RNA and proteins they typically publish their data via databases with the NCBI. Each is given a unique identification number known as an **accession number**. For example, each time a unique human genome sequence is produced it is uploaded to the relevant databases, assigned a unique **accession**, and a website created to access it. Sequence are also cross-referenced to related papers, so you can start with a sequence and find out what scientific paper it was used in, or start with a paper and see if any sequences are associated with it. @@ -2053,7 +2060,7 @@ In this chapter we'll typically refer generically to "NCBI data" and "NCBI datab Almost published biological sequences are available online, as it is a requirement of every scientific journal that any published DNA or RNA or protein sequence must be deposited in a public database. The main resources for storing and distributing sequence data are three large databases: -1. USA: **[NCBI database](www.ncbi.nlm.nih.gov/)** (www.ncbi.nlm.nih.gov/) +1. USA: **[NCBI database](https://www.ncbi.nlm.nih.gov/)** (www.ncbi.nlm.nih.gov/) 1. Europe: **European Molecular Biology Laboratory (EMBL)** database (https://www.ebi.ac.uk/ena) 1. Japan: **DNA Database of Japan (DDBJ)** database (www.ddbj.nig.ac.jp/). These databases collect all publicly available DNA, RNA and protein sequence data and make it available for free. They exchange data nightly, so contain essentially the same data. The redundancy among the databases allows them to serve different communities (e.g. native languages), provide different additional services such as tutorials, and assure that the world's scientists have their data backed up in different physical locations -- a key component of good data management! @@ -2093,7 +2100,7 @@ As mentioned above, for each sequence the NCBI database stores some extra inform To view the GenBank entry for the DEN-1 Dengue virus, follow these steps: -1. Go to the [NCBI website](www.ncbi.nlm.nih.gov) (www.ncbi.nlm.nih.gov). +1. Go to the [NCBI website](https://www.ncbi.nlm.nih.gov) (www.ncbi.nlm.nih.gov). 1. Search for the accession number NC_001477. 1. Since we searched for a particular accession we are only returned a single main result which is titled "NUCLEOTIDE SEQUENCE: Dengue virus 1, complete genome." 1. Click on "Dengue virus 1, complete genome" to go to the GenBank entry. @@ -2273,7 +2280,7 @@ The following chapter was originally written by Avril Coghlan. It provides brie ## Retrieving genome sequence data via the NCBI website -You can easily retrieve DNA or protein sequence data by hand from the [NCBI](www.ncbi.nlm.nih.gov) Sequence Database via its website www.ncbi.nlm.nih.gov. +You can easily retrieve DNA or protein sequence data by hand from the [NCBI](https://www.ncbi.nlm.nih.gov/) Sequence Database via its website www.ncbi.nlm.nih.gov. Dengue DEN-1 DNA is a viral DNA sequence and its NCBI **accession number** is NC_001477. To retrieve the DNA sequence for the Dengue DEN-1 virus from NCBI, go to the NCBI website, type “NC_001477” in the Search box at the top of the webpage, and press the “Search” button beside the Search box. @@ -2320,7 +2327,7 @@ In a previous vignette you learned how to retrieve sequences from the NCBI datab As mentioned previously, a subsection of the NCBI database called **RefSeq** consists of high quality DNA and protein sequence data. Furthermore, the NCBI entries for the RefSeq sequences have been **manually curated**, which means that expert biologists employed by NCBI have added additional information to the NCBI entries for those sequences, such as details of scientific papers that describe the sequences. -Another extremely important manually curated database is [**UniProt**](www.uniprot.org), which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. +Another extremely important manually curated database is [UniProt](https://www.uniprot.org/) www.uniprot.org, which focuses on protein sequences. UniProt aims to contains manually curated information on all known protein sequences. While many of the protein sequences in UniProt are also present in RefSeq, the amount and quality of manually curated information in UniProt is much higher than that in RefSeq. For each protein in UniProt, the UniProt curators read all the scientific papers that they can find about that protein, and add information from those papers to the protein’s UniProt entry. For example, for a human protein, the UniProt entry for the protein usually includes information about the biological function of the protein, in what human tissues it is expressed, whether it interacts with other human proteins, and much more. All this information has been manually gathered by the UniProt curators from scientific papers, and the papers in which the found the information are always listed in the UniProt entry for the protein. @@ -2340,7 +2347,7 @@ This tells us that *Mycobacterium* is a species of bacteria, which belongs to a Back up at the top under "organism" is says "Status", which tells us the **annotation score** is 2 out of 5, that it is a "Protein inferred from homology", which means what we know about it is derived from bioinformatics and computational tools, not lab work. -Beside the heading “Function”, it says that the function of this protein is that it “Removes the pyruvyl group from chorismate to provide 4-hydroxybenzoate (4HB)”. This tells us this protein is an enzyme (a protein that increases the rate of a specific biochemical reaction), and tells us what is the particular biochemical reaction that this enzyme is involved in. At the end of this info it says "By similarity", which again indicates that what we know about this protein comes from bioinformatics, not lab work. +Beside the heading “Function”, it says that the function of this protein is that it “Removes the pyruvyl group from chorismate to provide 4-hydroxybenzoate (4HB)”. This tells us this protein is an enzyme (a protein that increases the rate of a specific biochemical reaction), and tells us what is the particular biochemical reaction that this enzyme is involved in. At the end of this info it says "By similarity", which again indicates that what we know about this protein comes from bioinformatics, not lab work. ### Protein sequence and size @@ -2386,6 +2393,13 @@ We can confirm str(lepraeseq) ``` +```{r eval = F} + # 'SeqFastadna' chr [1:210] "m" "t" "n" "r" "t" "l" "s" "r" "e" "e" "i" ... + # - attr(*, "name")= chr "sp|Q9CD83|PHBS_MYCLE" + # - attr(*, "Annot")= chr ">sp|Q9CD83|PHBS_MYCLE Chorismate pyruvate-lyase + # OS=Mycobacterium leprae (strain TN) OX=272631 GN=ML0133 PE=3 SV=1" +``` + For the other sequence @@ -2397,6 +2411,16 @@ file.2 <- system.file("./extdata/A0PQ23.fasta", package = "compbio4all") # load fasta ulcerans <- read.fasta(file = file.2) ulceransseq <- ulcerans[[1]] + +str(ulceransseq)[[1]] +``` + + +```{r} + # 'SeqFastadna' chr [1:212] "m" "l" "a" "v" "l" "p" "e" "k" "r" "e" "m" ... + # - attr(*, "name")= chr "tr|A0PQ23|A0PQ23_MYCUA" + # - attr(*, "Annot")= chr ">tr|A0PQ23|A0PQ23_MYCUA Chorismate pyruvate-lyase +# OS=Mycobacterium ulcerans (strain Agy99) OX=362242 GN=MUL_2003 PE=4 SV=1" ``` diff --git a/lbrb.log b/lbrb.log index 24eb8b2..a815718 100644 --- a/lbrb.log +++ b/lbrb.log @@ -1,4 +1,4 @@ -This is XeTeX, Version 3.14159265-2.6-0.999992 (TeX Live 2020) (preloaded format=xelatex 2020.4.7) 18 SEP 2021 00:33 +This is XeTeX, Version 3.14159265-2.6-0.999992 (TeX Live 2020) (preloaded format=xelatex 2020.4.7) 18 SEP 2021 01:24 entering extended mode restricted \write18 enabled. %&-line parsing enabled. @@ -1123,23 +1123,33 @@ t;! ] Chapter 12. -Underfull \hbox (badness 10000) in paragraph at lines 2355--2356 +Underfull \hbox (badness 2376) in paragraph at lines 2373--2374 +[]\TU/lmr/m/n/10 A key component of GenBank are the \TU/lmr/bx/n/10 GeneBank Re +cords\TU/lmr/m/n/10 , which are annotated summaries + [] + + +Underfull \hbox (badness 10000) in paragraph at lines 2373--2374 [] +[65] File: images/genbank_record.png Graphic file (type bmp) -Overfull \hbox (338.73183pt too wide) in paragraph at lines 2359--2360 +Overfull \hbox (338.73183pt too wide) in paragraph at lines 2377--2378 [][] [] -[65] [66] [67] [68 -] +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[66] [67] [68] Chapter 13. -[69] [70] [71] [72] [73] -Underfull \hbox (badness 1540) in paragraph at lines 2642--2642 +[69 + +] [70] [71] [72] [73] +Underfull \hbox (badness 1540) in paragraph at lines 2656--2656 []\TU/lmr/bx/n/14.4 Example: finding the sequences published in Nature [] @@ -1151,14 +1161,14 @@ Chapter 14. Chapter 15. [77 -] [78] [79] [80 - -] +] [78] [79] [80] Chapter 16. Underfull \vbox (badness 1838) has occurred while \output is active [] -[81] [82] [83] [84] +[81 + +] [82] [83] [84] Chapter 17. [85 @@ -1167,43 +1177,43 @@ Chapter 17. ] Chapter 18. [87] [88] -Underfull \hbox (badness 10000) in paragraph at lines 3221--3223 +Underfull \hbox (badness 10000) in paragraph at lines 3251--3253 [] -Underfull \hbox (badness 10000) in paragraph at lines 3236--3238 +Underfull \hbox (badness 10000) in paragraph at lines 3266--3268 [] [89] [90] [91] -Overfull \hbox (11526.3668pt too wide) in paragraph at lines 3428--3429 +Overfull \hbox (11526.3668pt too wide) in paragraph at lines 3458--3459 [][] [] [92] [93] [94] -Underfull \hbox (badness 1824) in paragraph at lines 3601--3602 +Underfull \hbox (badness 1824) in paragraph at lines 3631--3632 []\TU/lmr/m/n/10 We’ll download all of the sequences again, this time using a f unction from [][][][] called [] [95] [96] -File: lbrb_files/figure-latex/unnamed-chunk-157-1.pdf Graphic file (type pdf) - +File: lbrb_files/figure-latex/unnamed-chunk-159-1.pdf Graphic file (type pdf) + [97] [98] -File: lbrb_files/figure-latex/unnamed-chunk-164-1.pdf Graphic file (type pdf) - -File: lbrb_files/figure-latex/unnamed-chunk-165-1.pdf Graphic file (type pdf) - +File: lbrb_files/figure-latex/unnamed-chunk-166-1.pdf Graphic file (type pdf) + +File: lbrb_files/figure-latex/unnamed-chunk-167-1.pdf Graphic file (type pdf) + Underfull \vbox (badness 10000) has occurred while \output is active [] [99] -File: lbrb_files/figure-latex/unnamed-chunk-166-1.pdf Graphic file (type pdf) - +File: lbrb_files/figure-latex/unnamed-chunk-168-1.pdf Graphic file (type pdf) + [100] -File: lbrb_files/figure-latex/unnamed-chunk-167-1.pdf Graphic file (type pdf) - +File: lbrb_files/figure-latex/unnamed-chunk-169-1.pdf Graphic file (type pdf) + Underfull \vbox (badness 10000) has occurred while \output is active [] @@ -1218,28 +1228,28 @@ Chapter 19. ] [109] [110] [111 ] [112] [113] -Overfull \hbox (0.29388pt too wide) in paragraph at lines 4345--4346 +Overfull \hbox (0.29388pt too wide) in paragraph at lines 4375--4376 \TU/lmr/m/n/10 Often when you do an internet search for an R topic you’ll see r esults from the website www.stackoverflow.com, [] [114] No file lbrb.bbl. -Package atveryend Info: Empty hook `BeforeClearDocument' on input line 4471. +Package atveryend Info: Empty hook `BeforeClearDocument' on input line 4501. 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