How to know synonymous and non-synonymous SNPs from transcriptome sequencing

Question

[smallfish]

I obtained a SNP list (VCF format) from a sample's transcriptome (assembled by Trinity). ORFs were identified using TransDecoder. How I classify these SNPs? How to calculate ka/ks？

Answer 1

[Philipp Bayer]

You can annotate VCFs using SnpEFf, but you will first have to build the database: http://snpeff.sourceforge.net/SnpEff_manual.html#databases

This will (in the end) make a new VCF with non-synonymous SNPs and their severity (introduces STOP codons, changes AA, changes nothing etc.). SNPEff's report will also report the total number of Ka and Ks, if I remember correctly.

If you want to calculate Ka/Ks on a more detailed scale, use this script: https://github.com/MerrimanLab/selectionTools/blob/master/extrascripts/kaks.py which takes SNPEff's output and gives out a Ka/Ks table per gene

[smallfish]

Thanks very much. It's a good idea.

[lessismore]

Dear Philipp, I am dealing with an analysis on a vcf that I gave as input to snpeff (from a population of more than 400 closely related individuals (among varieties and species - also polyploids)). Then I've used this script you shared to calculate Ka/Ks table per gene. This analysis is resulting in a very broad ka/ks distribution which goes from 0 to hundreds. Any comment or tip on this?

[Philipp Bayer]

Most papers I've seen filter out 'extreme' Ks values, here's a totally random paper that removes Ks > 3 https://onlinelibrary.wiley.com/doi/full/10.1111/mec.15275 Your niche must have something similar to cite

[lessismore]

Thanks for your answer. It looks weird to me as the kaks.txt file after running

grep '^#\|missense_variant\|synonymous_variant' annotated_vcf.vcf > mis_syn.txt 
python kaks.py mis_syn.txt > kaks.txt

gives me these quantiles for ks column (similar quantiles for ka). Is that maybe a too heterogeneous panel ? I am working with cereals.

0%   25%   50%   75%  100% 
0     6    42   178   13976 

while for ka/ks+1 i get

  0%          25%          50%          75%         100% 
   0          0.17        1.07          5.28        2192 

Answer 2

[jfo]

can we use SNPMeta for this? https://www.ncbi.nlm.nih.gov/pubmed/24237904