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2023.R
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###########################################################
# #
# Sequence reads assessment
# #
# #
# #
###########################################################
# Analisis de genomas y transcriptomas con plataforma NGS
# Master Biotecnologia
# APR 2022
# Jose V. Die, Dep Genetics, UCO
#BiocManager::install("ShortRead")
# Dependencies
library(ShortRead)
# Download files
download.file("https://www.dropbox.com/s/6un05pdvx2join5/Saccharomyces_cerevisiae_genome.fa.gz?dl=1",
destfile = "Saccharomyces_cerevisiae_genome.fa.gz")
download.file(url = "https://www.dropbox.com/s/svch71j19zfhntm/SRR11397715.fastq.gz?dl=1",
destfile = "SRR11397715.fastq.gz")
# Uncompress fasta.gz & fastq.gz files
#Rsamtools::gunzip("SRR11397715.fastq.gz", remove = FALSE)
system("time gunzip -k SRR11397715.fastq.gz")
system("time gunzip Saccharomyces_cerevisiae_genome.fa.gz")
# make some tidy
dir.create("mv *.fa data/")
system("mv *.fastq.gz *.fastq data")
# ## ---fasta------------------------------------------------------------
# # Read fasta : Saccharomyces genome
# fa_sample <- readFasta(dirPath = "data", pattern = "genome")
#
# # print sample
# fa_sample
#
# # methods accessors
methods(class = "ShortRead")
id(fa_sample)
length(fa_sample)
width(fa_sample)
sread(fa_sample)
#
# Extract sequence from DNAStringSet object
#toString(sread(fa_sample)[1])
#as.character(sread(fa_sample)[1])
# # Write a ShortRead object
# system("mkdir results")
# writeFasta(object = fa_sample, file = "results/my_chromosomes.fasta")
## ---fastq---------------------------------------------------------------------
# read fastq -------------------------------------------------------------------
fqsample <- readFastq(dirPath = "data", pattern = "fastq")
fqsample <- readFastq(dirPath = "data/SRR11397715.fastq")
# print fqsample
fqsample
## accesors : fastq ------------------------------------------------------------
methods(class = "ShortReadQ")
length(fqsample)
sread(fqsample)[1:20]
width(fqsample)[1:20]
sum(width(fqsample)) / 1e6 # comparar con slide "entrez" : 96.3M
hist(width(fqsample))
abline(v = 150, col = "red", lwd = 2)
fqsample150 <- fqsample[width(fqsample) >= 150]
hist(width(fqsample150))
length(fqsample) / 1e3
length(fqsample150) / 1e3
# # Write a ShortRead object
writeFasta(object = fqsample150, file = "results/reads150.fastq")
## subset from a fastq file -----------------------------------------------------
set.seed(2022)
fqsampler <- FastqSampler("data/SRR11397715.fastq", 300000)
# extract the sample from the stored file
fqsubset <- yield(fqsampler)
#Warning message:
# cerrando la conenexion 3 (reads/SRR11397715.fastq) que no esta siendo utilizada
close(fqsampler)
length(fqsample)
length(fqsubset)
width(fqsubset)[1:20]
sread(fqsubset)[1:20]
sum(width(fqsubset))/1e6 # N Mb representados en ese subset
# otra forma : filtro 500 al azar del set de reads con 150 bp.
length(fqsample150)
idx = sample(1:length(fqsample150), 500, replace = FALSE)
fq_sample150_500 <- fqsample150[idx]
length(fq_sample150_500)
sread(fq_sample150_500)
# ## convertQuality---------------------------------------------------------------
qreads <- quality(fqsubset) # quality encoded into ASCII characters
qreads
encoding(qreads)
dim(as(qreads , "matrix"))
as(qreads , "matrix")[1:6, 1:15]
sort(encoding(qreads)[c("A", "E", "/", "<")])
# PhredQuality instance
pq <- PhredQuality(qreads)
# transform the encoding into scores
qs <- as(pq, "IntegerList")
qs[[1]]
qs[1]
#
#
# # Remember : a score of 30 is considered a good quality as it means
# # the accuracy of base call is 99.9%
# table(qs[[1]] >= 30)
#
# # Quaility assesment & summary
# qaSummary <- qa(fqsubset, lane = 1)
#
# class(qaSummary)
# methods(class = "ShortReadQQA")
# names(qaSummary)
#
# # qa elements are accessed with qa[["name"]]
# qaSummary[["readCounts"]]
# qaSummary[["baseCalls"]]
# qaSummary[["adapterContamination"]]
#
#
# # get html report
# browseURL(report(qaSummary))
## Duplicates ---------------------------------------------------------------
table(srduplicated(fqsample)) # counts of the duplicated reads
# Cleaning reads from duplicates
uniqueReads <- fqsample[srduplicated(fqsample) == FALSE]
table(srduplicated(uniqueReads))
## Write a ShortRead object ----------------------------------------------------
# system("mkdir results")
# writeFasta(object = fq_sample150_500, file = "results/my_fasta.fasta")
# writeFastq(object = fq_sample150_500, file = "results/fqsample.fastq.gz")
###########################################################
# #
# Mapping Reads #
# #
# #
###########################################################
# Mappig Reads to a Genome
# Análisis de genomas y transcriptomas con plataforma NGS
# Master Biotecnologia
# APR 2023
# Jose V. Die, Dept. Genetics, UCO
# Paired-end reads
# FFFF_1.fastq.gz
# FFFF_2.fastq.gz
# .FASTQ (.FQ): short-reads (eg. illumina)
# .FASTA (.FA,.FNA): biological sequences (eg. chromosomes)
# .GFF (.GFF3, .GTF): genomic elements coordinates (eg. genes)
# Experiment:
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133136
# Dependencies
library(Rbowtie2)
library(Rsamtools)
# Organize the Project
# dir.create("data") #(To create a directory/folder in the indicated path)
# dir("data") #(To see files and folders in the indicated path)
# Download data ----------------------------------------------------------------
file.exists("NGS_MB_SRR9336468_1.fastq.gz")
file.exists("NGS_MB_SRR9336468_2.fastq.gz")
file.exists("Saccharomyces_cerevisiae_genome.gff3.gz")
file.exists("Saccharomyces_cerevisiae_genome.fa.gz")
download.file("https://www.dropbox.com/s/v06um7vt9ojdf42/NGS_MB_SRR9336468_1.fastq.gz?dl=1",
destfile = "data/1MB_SRR9336468_1.fastq.gz")
download.file("https://www.dropbox.com/s/kgxfth8ra675ccu/NGS_MB_SRR9336468_2.fastq.gz?dl=1",
destfile = "data/1MB_SRR9336468_2.fastq.gz")
download.file("https://www.dropbox.com/s/qtaret1hrbvw2xb/Saccharomyces_cerevisiae_genome.gff3.gz?dl=1",
destfile = "data/Saccharomyces_cerevisiae_genome.gff3.gz")
download.file("https://www.dropbox.com/s/4ft480eky7kghzw/Saccharomyces_cerevisiae_genome.fa.gz?dl=1",
destfile = "data/Saccharomyces_cerevisiae_genome.fa.gz")
# # descarga original CSIC :
#
# download.file(url = "https://bioinfogp.cnb.csic.es/courses/quedateencasa/23_03_2020/1M_SRR9336468_1.fastq.gz",
# destfile = "alignment/data/1MB_SRR9336468_1.fastq.gz")
#
# download.file(url = "https://bioinfogp.cnb.csic.es/courses/quedateencasa/23_03_2020/1M_SRR9336468_2.fastq.gz",
# destfile = "alignment/data/1MB_SRR9336468_2.fastq.gz")
#
# download.file(url = "https://bioinfogp.cnb.csic.es/courses/quedateencasa/23_03_2020/Saccharomyces_cerevisiae.R64-1-1.dna.toplevel.fa.gz",
# destfile = "alignment/data/Saccharomyces_cerevisiae_genome.fa.gz")
#
# download.file(url = "https://bioinfogp.cnb.csic.es/courses/quedateencasa/23_03_2020/Saccharomyces_cerevisiae.R64-1-1.99.gff3.gz",
# destfile = "alignment/data/Saccharomyces_cerevisiae_genome.gff3.gz")
system("ls -lh")
dir()
list.files()
# Uncompress genome fastq gz files with "gunzip"
#Rsamtools::gunzip("Saccharomyces_cerevisiae_genome.gff3.gz", remove = FALSE)
system("time gunzip -k data/Saccharomyces_cerevisiae_genome.fa.gz")
system("time gunzip -k data/Saccharomyces_cerevisiae_genome.gff3.gz")
# Uncompress reads fastq gz files with "gunzip"
system("time gunzip -k data/NGS*.fastq.gz")
# Uncompress all fastq gz files with "gunzip"
system("time gunzip -k data/*.fastq.gz")
# Make some tidy
# dir.create("data/chromosomes")
# dir.create("data/fastq")
system("mkdir data/chromosomes")
system("mkdir data/fastq")
system("mkdir results")
system("mv data/NGS*.fastq data/fastq/")
system("mv data/Saccharomyces_cerevisiae_genome.fa data/chromosomes/")
system("mv data/Saccharomyces_cerevisiae_genome.gff3 data/chromosomes/")
#system("rm *.gz")
system("ls -lh")
system("ls -lh data/chromosomes")
system("ls -lh data/fastq")
## GFF3 & FASTQ files ----------------------------------------------------------
# Look at the genomic annotation file (gff3)
file.show("data/chromosomes/Saccharomyces_cerevisiae_genome.gff3")
system("less -S data/chromosomes/Saccharomyces_cerevisiae_genome.gff3")
# Look at one of those two .fastq files
system("wc -l data/fastq/1MB_SRR9336468_1.fastq")
system("less -S fastq/1MB_SRR9336468_1.fastq")
# Look at the two reads from the same cDNA fragment
system("head -n 4 data/fastq/1MB_SRR9336468_1.fastq")
system("head -n 4 data/fastq/1MB_SRR9336468_2.fastq")
# Notice :
# name 1st sequence in the 1st file = name 1st seq. in the 2nd file, except
# 1/2 and 1/2 => it is the 1st pair and the 2nd pair of the same cDNA fragment.
# remember : the reads can contain junctions.
# muy buena explicacion Michael Love min 5 "First look at a FASTQ file" video
# ahi explica q el mismo nombre de seq aparece en los 2 ficheros
# preguntas 2ejercicio clase : "FASTQ assessment" EDx
## BLAST genome ------------------------------------------------------------
# blast genome : saccharomices
# https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch&USER_FORMAT_DEFAULTS=on&SET_SAVED_SEARCH=true&PAGE=MegaBlast&PROGRAM=blastn&GAPCOSTS=0%200&MATCH_SCORES=1,-2&BLAST_SPEC=Assembly&DATABASE=genomic/559292/GCF_000146045.2&BLAST_PROGRAMS=megaBlast&MAX_NUM_SEQ=100&SHORT_QUERY_ADJUST=on&EXPECT=0.05&WORD_SIZE=28&REPEATS=4932&TEMPLATE_TYPE=0&TEMPLATE_LENGTH=0&FILTER=L&FILTER=R&FILTER=m&EQ_MENU=Enter%20organism%20name%20or%20id--completions%20will%20be%20suggested&PROG_DEFAULTS=on&SHOW_OVERVIEW=on&SHOW_LINKOUT=on&ALIGNMENT_VIEW=Pairwise&MASK_CHAR=2&MASK_COLOR=1&GET_SEQUENCE=on&NUM_OVERVIEW=100&DESCRIPTIONS=100&ALIGNMENTS=100&FORMAT_OBJECT=Alignment&FORMAT_TYPE=HTML
# blast result analysis : hacer primero @SRR9336468.1 1/2
# @SRR9336468.1 1/2 : https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Get&RID=6ZKY810D013
# read mapea sitio único XV : genome data viewer
# mapea : 780 - 928
# orientación : forward
# 1a base de esa read mapea en la coord. 780 del chr XVI
# @SRR9336468.1 1/1 : https://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Get&RID=6ZKPUBMW013
# read mapea sitio único XV : genome data viewer
# mapea : 803 - 951
# orientación : reverse
# 1a base de esa read mapea en la coord. 951 del chr XVI
## Genome alignment ------------------------------------------------------------
#bowtie2 paper : https://www.nature.com/articles/nmeth.1923
#bowtie2 software homepage : http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
#BiocManager::install("Rbowtie2")
#library(Rbowtie2)
# STEP 1: Create genome index for bowtie2 with "bowtie2_build":
start_time = Sys.time()
bowtie2_build(references = "data/chromosomes/Saccharomyces_cerevisiae_genome.fa",
bt2Index = "data/chromosomes/Scerevisiae_genome",
overwrite = TRUE)
end_time = Sys.time()
end_time - start_time
# Time difference of 18.45692 secs
# bowtie2 no me genera el archivo de indice .fai que deberia haber en chromosomes/
# pero encuentro como hacerlo con Rsamtools
# samtools faidx fastafile # samtools en linux
indexFa("data/chromosomes/Saccharomyces_cerevisiae_genome.fa") # crea el .fai
# STEP 2: Align FASTQ files against indexed genome with "bowtie2":
start_time = Sys.time()
bowtie2(bt2Index = "data/chromosomes/Scerevisiae_genome",
samOutput = "SRR9336468.sam",
seq1 = "data/fastq/1MB_SRR9336468_1.fastq",
seq2 = "data/fastq/1MB_SRR9336468_2.fastq",
overwrite = TRUE,
"--threads=3")
end_time = Sys.time()
end_time - start_time
# Time difference of 4.716987 mins ##"--threads=2" en despacho
# Time difference of 2.249662 mins ##"--threads=3" en despacho
# Time difference of 1.685986 mins ##"--threads=3" en casa
# STEP 3 : Convert SAM files into BAM (and indexes .bai) with "asBam"
# converting SAM file into BAM file (more compact and indexed)
#BiocManager::install("Rsamtools")
#library(Rsamtools)
start_time = Sys.time()
asBam("SRR9336468.sam")
end_time = Sys.time()
end_time - start_time
# Time difference of 1.345935 mins
# make some tidy
rm(end_time, start_time)
# una vez se tiene el BAM se puede borrar el SAM porque son los mas prescindibles y los que mas ocupan
file.remove("SRR9336468.sam")
# Nota para mi (Curso con Esteban): GenomicAlignments package.
# READING FROM A BAM FILE
# part 1 : https://www.youtube.com/watch?v=2KqBSbkfhRo
# part 2 : https://www.youtube.com/watch?v=3PK_jx44QTs&t=140s
# library(GenomicAlignments)
# gal <- readGAlignments("SRR9336468.bam")
# gal
# length(gal)
# table(strand(gal))
## IGV ----
#ver video Michael Love
# IGV software homepage : https://www.broadinstitute.org/igv/
# https://igv.org/
# Load genome : Genome / Local File / chromosomes / cargar a la vez el .fa + .fai
# Load genes : Tracks / Local File (gff3)
#View options :
#Set track height / 100
#Expanded / Collapse
# Load reads : Tracks / Load File / select bam + index (bai)
# select chr1
# - note you have reads where you have genes.
# - note some genes have more reads than others = in the experimental conditions ,
# some genes are highly expressed + some genes are not expressed.
# - explicar mistmach
# - small variation related to the reference genome
# - (n. mismatch are allowed during alignment step)
# - if you find a consistent mistmatch (several times on the same column) :
# - the reference genome has error
# - the cell contains a mutation (SNP)
# - 57,300 - 58,000 : YAL045C
# color by / read strand
# especificidad de hebra : color by / first-of-pair strand
# 5'-3' : azul
# 3'-5' : naranja
# preguntas 2ejercicio clase : "IGV assessment"
## Filtering reads by mapping quality
# ------------------------------------------------------------------------------
bamfile <- BamFile("SRR9336468.bam")
bamfile
seqinfo(bamfile)
# set the filter condition
# build the filtered : .bam + .bai files
param = ScanBamParam(mapqFilter = 35) # min=35 *
dest = filterBam(file = bamfile, destination = "results/mapq.bam", param = param)
#Tb puedo controlar la calidad del alineamiento despues en Deseq2 con la
#function 'featureCounts' y la variable minMQS
#dest <- BamFile("results/mapq.bam")
countBam(bamfile)
countBam(dest)
#scanBam generates a list (length = 1) with 13 elements
aln <- scanBam(bamfile)
aln35 = scanBam(dest)
#names(aln[[1]])
aln[[1]]$seq[1:10]
aln[[1]]$mapq[1:10]
summary(aln[[1]]$mapq)
summary(aln[[1]]$mapq)
summary(aln35[[1]]$mapq)
sum(aln[[1]]$mapq < 35, na.rm = TRUE)
sum(aln35[[1]]$mapq < 35, na.rm = TRUE)
# FIN CLASE
# Nota para mi : No sé filtrar duplicados PCR/opticos porque si
# defino el filtro expresamente, el numero de lecturas es el mismo que si no
# lo hago. Y si pongo TRUE sigue saliendo el mismo n. lecturas
param2 = ScanBamParam(mapqFilter = 35,
flag = scanBamFlag(isDuplicate = FALSE))
dest2 = filterBam(file = bamfile,
destination = "results/mapqNOdup.bam",
param = param2)
#dest2 <- BamFile("results/mapqNOdup.bam")
countBam(bamfile)
countBam(dest)
countBam(dest2)
# IGV only with reads >= 35 mapping quality score
# comparar diferencias : chr1 _ 78kb _ FUN12
###########################################################
# #
# Differential gene expression analysis (RNA-seq) with R #
# #
# #
# #
###########################################################
# Análisis de genomas y transcriptomas con plataforma NGS
# Master Biotecnología
# APR 2023
# Jose V. Die, Dep Genetics, UCO
# Paper :
# "Physiological responses of Saccharomyces cerevisiae to industrially
# relevant conditions: Slow growth, low pH, and high CO2 levels"
# Hakkaart X, Liu Y, Hulst M, El Masoudi A, Peuscher E, Pronk J, van Gulik W, Daran-Lapujade P.
# Biotechnol Bioeng. 2020 Mar;117(3):721-735. doi: 10.1002/bit.27210. Epub 2020 Jan 22.
# https://www.ncbi.nlm.nih.gov/pubmed/31654410
# sample table :
# https://github.com/jdieramon/master_biotec_NGS/blob/main/sample_table.md
# SampleID Description Replicate
# SRR9336468 Chemostat pH5 0.04% CO2 R1
# SRR9336469 Chemostat pH5 0.04% CO2 R2
# SRR9336470 Chemostat pH5 0.04% CO2 R3
# SRR9336471 Chemostat pH5 50% CO2 R1
# SRR9336472 Chemostat pH5 50% CO2 R2
# SRR9336473 Chemostat pH5 50% CO2 R3
# SRR9336474 Chemostat pH3 0.04% CO2 R1
# SRR9336475 Chemostat pH3 0.04% CO2 R2
# SRR9336476 Chemostat pH3 0.04% CO2 R3
# install.packages("BiocManager") # <- Bioconductor manager
# Install R packages (from Bioconductor):
# BiocManager::install("Rbowtie2") # <- align FASTQ files
# BiocManager::install("Rsamtools") # <- manipulate SAM files
# BiocManager::install("Rsubread") # <- quantify alignments
# BiocManager::install("DESeq2") # <- differential gene expression
# load all libraries:
#library("R.utils")
library("Rbowtie2")
library("Rsamtools")
library("Rsubread")
library("DESeq2")
## STEP 1: Download gz files
# -----------------------------------------------------------------------------
# Download genome data
download.file("https://www.dropbox.com/s/4ft480eky7kghzw/Saccharomyces_cerevisiae_genome.fa.gz?dl=1",
destfile = "Saccharomyces_cerevisiae_genome.fa.gz")
download.file("https://www.dropbox.com/s/qtaret1hrbvw2xb/Saccharomyces_cerevisiae_genome.gff3.gz?dl=1",
destfile = "Saccharomyces_cerevisiae_genome.gff3.gz")
# Download FASTQ files (HUGE!!!)
# https://www.ebi.ac.uk/ena/data/view/SRR9336468
# https://www.ebi.ac.uk/ena/data/view/SRR9336469
# https://www.ebi.ac.uk/ena/data/view/SRR9336470
# https://www.ebi.ac.uk/ena/data/view/SRR9336471
# https://www.ebi.ac.uk/ena/data/view/SRR9336472
# https://www.ebi.ac.uk/ena/data/view/SRR9336473
# https://www.ebi.ac.uk/ena/data/view/SRR9336474
# https://www.ebi.ac.uk/ena/data/view/SRR9336475
# https://www.ebi.ac.uk/ena/data/view/SRR9336476
# fastq.gz = 1.6 GB
# fastq = 7.4 GB
#forma 1 (fichero original ; me da error)
download.file("ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR933/008/SRR9336468/SRR9336468_1.fastq.gz",
destfile = "SRR9336468_1.fastq.gz")
#forma2 (fichero original)
url = "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR933/008/SRR9336468/SRR9336468_1.fastq.gz"
curl::curl_download(url, destfile = "SRR9336468_1.fastq.gz")
# library(ShortRead)
#
# # read fastq ------------------------------------------------------------------
# start_time = Sys.time()
# faq_sample <- readFastq(dirPath = "SRR9336468_1.fastq")
# end_time = Sys.time()
# end_time - start_time
# #Time difference of 4.461314 mins
#
# faq_sample
#
# ## accesorFastq ---------------------------------------------------------------
# methods(class = "ShortReadQ")
# id(faq_sample)
# length(faq_sample)
# sread(faq_sample)
# width(faq_sample)
# table(width(faq_sample))
# rm(faq_sample)
# dropbox (1M reads) - en clase descargar este.
download.file("https://www.dropbox.com/s/v06um7vt9ojdf42/NGS_MB_SRR9336468_1.fastq.gz?dl=1",
destfile = "1M_SRR9336468_1.fastq.gz")
download.file("https://www.dropbox.com/s/kgxfth8ra675ccu/NGS_MB_SRR9336468_2.fastq.gz?dl=1",
destfile = "1M_SRR9336468_2.fastq.gz")
# no es necesario que descarguen el resto de fastq.gz files porque les dare
# despues los bam files
## STEP 2: Uncompress all gz files with "gunzip"
# -----------------------------------------------------------------------------
# Uncompress genome
#Rsamtools::gunzip("Saccharomyces_cerevisiae_genome.gff3.gz", remove = FALSE)
system("time gunzip -k Saccharomyces_cerevisiae_genome.fa.gz")
system("time gunzip -k Saccharomyces_cerevisiae_genome.gff3.gz")
dir.create("genes")
system("mv Saccharomyces_cerevisiae_genome.* genes")
# Uncompress reads
dir.create("fastq")
system("time gunzip -k *.fastq.gz")
system("mv *.fastq *.gz fastq/")
## STEP 3: Create genome index for bowtie2 with "bowtie2_build":
# -----------------------------------------------------------------------------
bowtie2_build(references = "genes/Saccharomyces_cerevisiae_genome.fa",
bt2Index = "genes/Sc_genome")
# bowtie2 no me genera el archivo de indice .fai
# pero encuentro como hacerlo con Rsamtools
# samtools faidx some.fasta # samtools en linux
indexFa("genes/Saccharomyces_cerevisiae_genome.fa") # crea el .fai
# STEP 4: Align FASTQ files against indexed genome with "bowtie2"
# -----------------------------------------------------------------------------
dir.create("bam")
start_time = Sys.time()
bowtie2(bt2Index = "genes/Sc_genome",
samOutput = "bam/1M68_pH5_0.04CO2_R1.sam",
seq1 = "fastq/1M_SRR9336468_1.fastq",
seq2 = "fastq/1M_SRR9336468_2.fastq", "--threads=3")
end_time = Sys.time()
end_time - start_time
# Time difference of 2.249662 mins ##"--threads=3" en despacho
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M69_pH5_0.04CO2_R2.sam", seq1 = "fastq/1M_SRR9336469_1.fastq", seq2 = "fastq/1M_SRR9336469_2.fastq", "--threads=3")
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M70_pH5_0.04CO2_R3.sam", seq1 = "fastq/1M_SRR9336470_1.fastq", seq2 = "fastq/1M_SRR9336470_2.fastq", "--threads=3")
#
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M71_pH5_5CO2_R1.sam", seq1 = "fastq/1M_SRR9336471_1.fastq", seq2 = "fastq/1M_SRR9336471_2.fastq", "--threads=3")
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M72_pH5_5CO2_R2.sam", seq1 = "fastq/1M_SRR9336472_1.fastq", seq2 = "fastq/1M_SRR9336472_2.fastq", "--threads=3")
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M73_pH5_5CO2_R3.sam", seq1 = "fastq/1M_SRR9336473_1.fastq", seq2 = "fastq/1M_SRR9336473_2.fastq", "--threads=3")
#
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M74_pH3_0.04CO2_R1.sam", seq1 = "fastq/1M_SRR9336474_1.fastq", seq2 = "fastq/1M_SRR9336474_2.fastq", "--threads=3")
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M75_pH3_0.04CO2_R2.sam", seq1 = "fastq/1M_SRR9336475_1.fastq", seq2 = "fastq/1M_SRR9336475_2.fastq", "--threads=3")
# bowtie2(bt2Index = "genes/Sc_genome", samOutput = "bam/1M76_pH3_0.04CO2_R3.sam", seq1 = "fastq/1M_SRR9336476_1.fastq", seq2 = "fastq/1M_SRR9336476_2.fastq", "--threads=3")
# # STEP 5: Convert SAM files into BAM (and indexes .bai) with "asBam"
# -----------------------------------------------------------------------------
asBam(file = "bam/1M68_pH5_0.04CO2_R1.sam", destination = "bam/basal_1")
# asBam("bam/1M69_pH5_0.04CO2_R2.sam", "bam/basal_2")
# asBam("bam/1M70_pH5_0.04CO2_R3.sam", "bam/basal_3")
#
# asBam("bam/1M71_pH5_5CO2_R1.sam", "bam/indust1_1")
# asBam("bam/1M72_pH5_5CO2_R2.sam", "bam/indust1_2")
# asBam("bam/1M73_pH5_5CO2_R3.sam", "bam/indust1_3")
#
# asBam("bam/1M74_pH3_0.04CO2_R1.sam", "bam/indust2_1")
# asBam("bam/1M75_pH3_0.04CO2_R2.sam", "bam/indust2_2")
# asBam("bam/1M76_pH3_0.04CO2_R3.sam", "bam/indust2_3")
## una vez se tiene el BAM se puede borrar el SAM porque son los mas prescindibles y los que mas ocupan
junk <- list.files(path = "bam", pattern = "*.sam", full.names = TRUE)
file.remove(junk)
# download the rest of *.bam files
start_time = Sys.time()
download.file("https://www.dropbox.com/s/pr3vqfj9r2c35xg/bam_files.zip?dl=1",
destfile = "bam/bam_files.zip")
end_time = Sys.time()
end_time - start_time
# Time difference of 1.000254 mins (casa)
# Time difference of 2.195933 mins (despacho)
# uncompress bam files
unzip(zipfile = "bam/bam_files.zip", exdir = "bam")
# si da error, es porque la descarga del fichero .zip no ha funcionado bien.
# el tamaño del archivo correcto es 871MB
# si se repite y sigue sin funcionar, que copien la dirección en el navegador internet
# se descarga el archivo y lo mueven a la carpeta
file.remove("bam/bam_files.zip")
## STEP 7: store all paths of bam files into "bamFiles" R object (use "list.files"):
# -----------------------------------------------------------------------------
list.files(path = "bam", pattern = "*.bam$", full.names = TRUE)
bamFiles <- list.files(path="bam", pattern="*.bam$", full.names=TRUE)
# STEP 5: Create metadata file for the Saccharomyces count matrix
# -----------------------------------------------------------------------------
# Create sample vector
sampleID = paste0("SRR93364", 68:76)
# Create condition vector
conditions = rep(c("basal", "indust1", "indust2"), each = 3)
# Create description vector
description = rep(c("pH5_0.04CO2", "pH5_5CO2", "pH3_0.04CO2"), each = 3)
# Create data frame
saccha_metadata <- data.frame(sampleID, conditions, description)
# Assign the row names of the data frame
rownames(saccha_metadata) = paste(conditions, 1:3, sep = "_")
# Save the df into a csv file
dir.create("data")
write.csv(saccha_metadata, file = "data/saccha_metadata.csv")
# # STEP 8: quantify "genes" with "featureCounts" and store results in R object "data"
# -----------------------------------------------------------------------------
# # [OPTIONAL] To check the content of gff3 file:
#file.show("genes/Saccharomyces_cerevisiae_genome.gff3")
start_time = Sys.time()
data <- featureCounts(
files = bamFiles,
annot.ext = "genes/Saccharomyces_cerevisiae_genome.gff3",
isGTFAnnotation = TRUE,
GTF.featureType = "gene",
GTF.attrType = "ID",
isPairedEnd = TRUE,
requireBothEndsMapped = TRUE,
minMQS = 30,
strandSpecific = 2,
nthreads = 4 # by default = 1.
)
end_time = Sys.time()
end_time - start_time
# Time difference of 11.27765 secs (threads = 3)
# Time difference of 44.48049 secs (threads = 3)
# class(data)
# data$counts[1:10, 1:3]
# data$counts = raw_counts
dir.create("results")
write.table(data$counts, file = "results/raw_counts" , sep = "\t")
# .tsv means "tab separated values"
raw_counts = read.table("results/raw_counts")
# trabajar con la clase los siguientes conceptos :
# mapping gen YAL063C in the last 4 samples
raw_counts["gene:YAL063C", 6:9]
# does the mapping match what I see in IGV ?
# open IGV : https://igv.org/
# load Saccharomices genome
# search : YAL063C (v. desktop no permite busqueda ; app sí lo encuentra y
# reconoce que el nombre biologico del gen YAL063C es FLO9)
# v. desktop : buscar por : chrI:16,439-36,407
# load samples (bam) 73-74-75-76 (f. ex.)
# color by first-pair-strand
# set track height : 200
# # STEP 9: Prepare data for the DESeq2 workflow
# -----------------------------------------------------------------------------
# Organize the data
# DESeq2 requires the sample names in the metadata and counts dataset to be in the
# same order. Therefore, the row names in the metadata need to be in the same order
# as the column names of the count data (= mismo orden que los ficheros bam).
# - raw counts of the number of reads aligning each gene
# - associated sample metadata
raw_counts = read.table("results/raw_counts")
samples <- read.csv("data/saccha_metadata.csv", row.names = 1)
colnames(raw_counts)
rownames(samples)
all(paste0(rownames(samples), ".bam") == colnames(raw_counts))
identical(paste0(rownames(samples), ".bam") , colnames(raw_counts))
# STEP 10: Use "DESeqDataSetFromMatrix" to create a DESeq2 object
# -----------------------------------------------------------------------------
dds <- DESeqDataSetFromMatrix(countData = raw_counts,
colData = samples,
design = ~ conditions)
save(dds, file = "results/dds.rda")
#load(file = "results/dds.rda")
#methods(class = "DESeqDataSet")
#levels(dds$conditions)
# STEP 11: Normalize raw counts for library size
# -----------------------------------------------------------------------------
# The raw counts for each sample are divided by the associated sample-specific
# size factor for normalization. To view the size factors used for normalization,
# we can use the sizeFactors() function.
dds <- estimateSizeFactors(dds)
sizeFactors(dds)
# colSums(counts(dds))
#
# plot(sizeFactors(dds),colSums(counts(dds)))
# abline(lm(colSums(counts(dds)) ~ sizeFactors(dds) + 0))
#
# rs <- rowSums(counts(dds))
# boxplot(log2(counts(dds)[rs > 0,] + 1)) # not normalized
# log.norm.counts <- log2(counts(dds, normalized=TRUE) + 1)
# boxplot(log.norm.counts[rs > 0,]) # normalized
#scatterplot of log normalized counts against each other.
# Note the fanning out of the points in the lower left corner, for points less
#than 2^5=32.
plot(log.norm.counts[,1:2], cex=.1)
abline(0,1, col = "red")
plot(log.norm.counts[,c(1,7)], cex=.1)
abline(0,1, col = "red")
# Once the size factors have been calculated and added to the DESeq2 object,
# the normalized counts can be extracted.
# If the default was left as normalized = FALSE, then we would extract the raw
# counts from the object.
norm_counts <- counts(dds, normalized = TRUE)
#boxplot(log2(norm_counts+1))
write.csv(norm_counts, file = "results/norm_counts.csv")
# STEP 12: Unsupervised Clustering Analyses
# -----------------------------------------------------------------------------
# Transform the normalized counts : VST-transformed normalized counts
vst_counts <- vst(dds, blind = TRUE)
# Extract the matrix of transformed counts
vsd <- assay(vst_counts)
# Compute the correlation values between samples
vsd_cor <- cor(vsd)
#View(vsd_cor)
# Hierarchical analysis
#BiocManager::install("pheatmap")
#samples <- read.csv("data/saccha_metadata.csv", row.names = 1)
pheatmap::pheatmap(vsd_cor, annotation = dplyr::select(samples, conditions))
# hemos hecho subset de 1M
# si tuvieramos las lecturas completas, puede que se hubieran agrupado bien
# hay que decidir si se deja fuera una muestra, si se repite, ...
# ex. : simulate other metadata such as the sample collection date
#samples$collection <- c(rep("April", 2), rep("June", 2), rep("April", 5))
#pheatmap::pheatmap(vsd_cor, annotation = dplyr::select(samples, collection))
# Plot PCA
plotPCA(vst_counts, intgroup = "conditions")
#This is great since it seems that a lot of variation in gene expression in the
#data set can likely be explained by the differences between sample groups.
# STEP 13: Run the DESeq2 analysis : model fitting for gene expression data
# -----------------------------------------------------------------------------
dds <- DESeq(dds)
#Now that we have run the DE analysis, we could explore our results.
#However, before proceeding, we should explore how well our data fit the model.
#datacamp : deseq2model video
# Plot dispersions
plotDispEsts(dds)