Oryctolagus_cuniculus_numts

Patterns of numtogenesis in the rabbit (Oryctolagus cuniculus) genome.

Upon usage, please cite:

@article{biro2022nuclear,
  title={Nuclear mitochondrial DNA sequences in the rabbit genome},
  author={Bir{\'o}, B{\'a}lint and G{\'a}l, Zolt{\'a}n and Schiavo, Giuseppina and Ribari, Anisa and Utzeri, Valerio Joe and Brookman, Michael and Fontanesi, Luca and Hoffmann, Orsolya Ivett},
  journal={Mitochondrion},
  volume={66},
  pages={1--6},
  year={2022},
  publisher={Elsevier}
}

This repository contains all the codes for identifying nuclear insertions with mitochondrial origin in the rabbit (Oryctolagus cuniculus) genome.

Setting up the environment:

mkdir codes data results
conda install -c bioconda last

The alignments were performed with LASTAL. The codes are mainly written in Python (3.7.10).

Used Python packages and external programs (if the module is not built in, the version number and conda installation are provided):

• os
• gzip
• shutil
• pandas (1.2.4) conda install pandas
• urllib.request
• subprocess
• requests (2.26.0) conda install -c anaconda requests
• numpy (1.20.2) conda install numpy
• seaborn (0.11.2) conda install seaborn
• matplotlib (3.4.3) conda install -c conda-forge matplotlib
• scipy (1.6.2) conda install -c anaconda scipy

To annotate the mitochondrion, MITOS server (http://mitos.bioinf.uni-leipzig.de/index.py) was used with the following setting(s):

• Genetic Code: 02 - Vertebrate

RepeatMasker was also run on server (http://genome.ucsc.edu/cgi-bin/hgTables?hgsid=1196065643_I1OZNebCc0toA4hkQLRwVjIcJUTg) with the following setting(s):

• clade: Mammal
• genome: Rabbit
• group: Variation and Repeats
• track: RepeatMasker
• table: rmsk
• define regions: upload the mt BED files
• please note that:
  • the input/output filenames are the followings:
    • repeatmasker_input_downstream.bed --> downstream_repeats.tsv
    • repeatmasker_input_upstream.bead --> upstream_repeats.tsv
    • repeatmasker_input_genomic_samples.bed --> genomic_repeats.tsv
  • for the statistical analysis and visualization, the output files should be placed into the results/ folder

Brief description of the programs:

1️⃣data_acquisition

• download genomic dna and mitochondrial dna in .gz format
• decompress the .gz files

2️⃣reverse_mt

• actual reversal of the mitochondrial dna

3️⃣duplicate_mt_dna

• prepare the duplicated mt dna for the later alignment

4️⃣alignments

• prepare the database with LASTAL from the rabbit genome
• align reverse mt dna with nuclear genome
• align double mt dna with nuclear genome

5️⃣significant_alignments

• get the lowest e value from the reverse mt dna alignment
• use this threshold for masking the double mt dna alignment

6️⃣writing_fasta_files

• process nuclear dna file into a format where the sequence is in one line
• get the ids of the genome parts (chromosome, scaffold) that have significant numts
• write individual FASTA files for the genome parts that have significant numts

7️⃣get_lastal_csv

• create a dataframe from the signifcant alignments (score, eg2_value, e_value, g_id, g_start, mt_start, g_length, mt_length, g_strand, mt_strand, g_size, g_sequence, mt_sequence)
• mask the artifacts that are the results of using double mt dna for the alignment
• write dataframe into a csv

8️⃣add_flankings

• add genomic and mitochondrial flanking regions to the previously defined csv file

9️⃣get_ensemble_data

• get the gene id (if available) of the genomic region where a significant numt is inserted
• get the gene description (if available) of the genomic region where a significant numt is inserted

🔟write_numtless_sequences

• get numt positions
• get the sequences of the genomic parts that contains numts
• write sequences without numts into individual FASTA files

1️⃣:one:gc_content_numts_vs_genome

• sample each genomic parts based on the number and length of the corresponding numts (genomic samples)
• calculate gc contents of the genomic samples
• calculate gc contents of the numts
• comapre the gc contents
• write the output for visualisation

1️⃣:two:gc_content_flankings_vs_genome

• sample each genomic parts based on the number and length of the corresponding numts (genomic samples)
• calculate gc contents of the genomic samples
• get the flankings of the numts
• calculate flankings gc contents
• comapre the gc contents
• write output

1️⃣3️⃣repeatmasker

• sample each genomic parts based on the number and length of the corresponding numts (genomic samples)
• get the flankings of the numts
• prepare input files for RepeatMasker server
  • repeatmasker_input_downstream.bed
  • repeatmasker_input_upstream.bed
  • repeatmsker_input_genomic_samples.bed
• the RepeatMasker output files should be named properly (downstream_repeats.tsv, upstream_repeats.tsv and genomic_repeats.tsv) and placed into the results/ folder!
• compare the frequency of repetitive elements
• write output

⚠️DISCLAIMER! Codes for generating figures are not updated recently!