/Alkaline_Adaptation

comparative genomics study to understand how Tibetan highland fish adapt to highly alkaline environment

Primary LanguageR

Comparative and Evolutionary Genomics of Alkaline Tolerant Fish

  • Author: Chao Tong, Miao Li, Yongtao Tang, Kai Zhao
  • Date: Apri-23, 2021
  • Project description: Comparative genomics study to understand how Tibetan highland fish adapt to extremely alkaline environment
  • Publication: Genomic signature of shifts in selection and alkaline adaptation in highland fish. Genome Biology and Evolution. 2021 link

Assembly :: Transcriptome

  • introduction to Trinity link
  • install Trinity via conda
conda install -c bioconda trinity

start de novo assembly

Trinity \
--trimmomatic \
--seqType fq \
--max_memory 20G \
--left left.fq \
--right right.fq \
--CPU 20 \
--output trinity_output \
--no_bowtie \
--quality_trimming_params "SLIDINGWINDOW:4:20 LEADING:10 TRAILING:10 MINLEN:70"  \
--normalize_reads \
--normalize_max_read_cov 100
  • output files: GPRZ.fa (fasta format)

Removed Redundant Transcripts

  • introduction to CD-HIT link
  • install CD-HIT via conda
conda install -c bioconda cd-hit

run cd-hit

  • input files: GPRZ.fa (nucleotide sequence)
  • output file: GPRZ_0.9.fa
cd-hit -i transcripts.fa -o transcripts_0.9.fa -c 0.9 -n 5 -M 16000 –d 0 -T 8

Protein-Coding Gene Prediction

  • introduction to TransDecoder link
  • install TransDecoder via conda
conda install -c bioconda transdecoder

TransDecoder.LongOrfs -t transcripts_0.9.fa
TransDecoder.Predict -t transcripts_0.9.fa
  • input files: GPRZ_0.9.fa
  • output files:
  1. GPRZ.cds (nucleotide sequence)
  2. GPRZ.pep (protein sequence)

Species phylogeny

  • introduction to R package, phangorn link

define the function pruneTreeFromAln

  • input files:
  • codon/AA alignment file (fasta/phylip format)
  • phylogenetic tree file (nwk format, add label)
Rscript pruned_tree.R

Ortholog identification

  • introduction to OMA link
  • install OMA via conda
conda install -c hcc oma

update the <parameters.drw> file

  • input files:
  • genome data: protein sequences
  • parameters.drw
  • phylogenetic tree: define a outgroup
OMA -n 40

Sequence Alignment

  1. prepare amino acid sequence alignment
  • introduction to MUSCLE link
  • install MUSCLE via conda
conda install -c bioconda muscle
  • input file: {gene}.fas (fasta format)
  • output file: {gene}.aln (fasta format)
snakemake --cores=1 -s snakefile_muscle
  1. prepare codon alignment
  • introduction to pal2nal link
  • install pal2nal via conda
conda install -c bioconda pal2nal
  • input files: {gene}.fa (nucleotide sequence) and {gene}.aln (amino acid sequence)
  • output files: {gene}.pml or {gene}.fas
perl pal2nal.pl $id.fa -output fasta -nogap > $id.fas

Molecular Evolution Analysis

  1. estimate the rate of molecular evolution (dN/dS) for alkaline tolerant and alkaline intolerant fish species
  • introduction to HyPHY link
  • install HyPHY via conda
conda install -c bioconda hyphy

define foreground branches: species {test}

  • input files:
  • codon alignment file (phylip format)
  • phylogenetic tree file (nwk format, add label)

run script with snakemake link

snakemake --cores=1 -s snakefile_relax
  1. Likelihood Ratio Test
  • lnLH1: two discrete ratios of dN/dS
  • lnLH0: one ratio of dN/dS
ΔlnL = 2(lnLH1-lnLH0)

discard the gene with reported LRT P value > 0.05

  1. extract rapidly evolving gene

compare the two ratios of dN/dS, LRT P value < 0.05

  • ω(alkaline tolerant species) > ω(alkaline intolerant species): rapidly evolving genes in alkaline tolerant species

Positive Selection Analysis

  1. detect the signal of positive selection at at least one site on at least one branch of a prori defined branches (e.g. alkaline tolerant fish)
  • input files:
  • codon alignment file (phylip format)
  • phylogenetic tree file (nwk format, add label)
snakemake --cores=1 -s snakefile_busted
  1. extract positively selected genes BUSTED model automatically discard the gene with reported LRT P value > 0.5 further check the reported output files:
  • gene with LRT P value < 0.05: positively selected gene

Gene Ontology (GO) Enrichment Analysis

  1. prepare the background GO dataset for shared orthologs
  • introduction to Trinoate pipeline link
  1. prepare input geneset (e.g. rapidly evolving genes)

  • introduction to R package, topGO link

  • algorithm = "classic"

  • statistic = "fisher"

  • input files: gene.csv and fish_go_annotation.csv

  • output file: gene_classic_fisher_enriched_GO.csv

Rscript topGO_run.R
  1. further execute similarity filter
  • introduction to REVIGO link
  • input files: enriched_GO.txt (paste to blank on the web page)
  • output files: download, or make a Rscript