Rna-Seq Data Variant Calling

Question: Rna-Seq Data Variant Calling

6.5 years ago by

learnerforever • 510 wrote:

Has anyone tried calling variants from RNA-seq data and comparing those with WGS/Exome sequencing variant calls in coding regions? I was curious to know if the same variant callers can be used on RNA-seq alignment (say TopHat alignments). Also, if there are tools that can predict RNA-editing or similar events.

tophat rna-seq • 10k views

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modified 6.4 years ago by Obi Griffith ♦ 17k • written 6.5 years ago by learnerforever • 510

If you're interested in inferring RNA-editing from RNA-seq, you should be sure to read the responses to the Li et al Science paper on that topic published recently in Science and commentary on that topic published on the Genomes Unzipped blog.

ADD REPLY • link written 6.5 years ago by David Quigley ♦ 11k

I don't believe any of us should be encouraging variant calling from RNA-seq. Here is why:

A: Inferring genotype based on RNA sequnces

ADD REPLY • link modified 16 days ago • written 16 days ago by Kevin Blighe ♦ 28k

6.5 years ago by

Obi Griffith ♦ 17k

Washington University, St Louis, USA

Obi Griffith ♦ 17k wrote:

You should check out the SNVMix papers here and here. They developed and used their method on RNA-seq tumor data and compared to "ground-truth" of genotype arrays and WGS. They also showed their approach could identify RNA-editing events. And, they have a follow-up method for matched tumor-normal samples called JointSNVMix. Although I think the latter was developed more for exome-seq.

ADD COMMENT • link written 6.5 years ago by Obi Griffith ♦ 17k

6.5 years ago by

Vitis • 1.6k

New York

Vitis • 1.6k wrote:

We've done quite a few variant calling from mRNA-Seq data for EMS mutant identifications. But we haven't compared with WGS/Exom yet. We used BWA for mapping, and samtools as well as GATK pipeline for variant calling. Both yielded pretty consistent results. One thing turned out to be very important for our purpose, i. e. detecting high quality SNPs in the coding regions, is that you have to trim aggressively to remove bases of bad quality, even at the cost of losing coverage in some areas. With really stringent quality trimming, we've successfully identified several mutant alleles that can be verified by Sanger sequencing or restriction enzyme genotyping.

ADD COMMENT • link written 6.5 years ago by Vitis • 1.6k

Thanks, we are following a similar approach as well so good to know we are not alone :). What reference did you use for bwa alignments? Custom transcriptome using known transcripts (>150,000?) Or some trick to use spliced alignments using bwa?

ADD REPLY • link written 6.5 years ago by learnerforever • 510

For the mapping question, it is probably worth looking at: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btr427v1

ADD REPLY • link written 6.5 years ago by Sean Davis ♦ 25k

We've been using predicted CDSs as references, because our system was not highly annotated, we ignored alternative transcription for the moment. I tried genome mapping, too, and got very similar results as you'll lose 5% junction reads.

ADD REPLY • link written 6.5 years ago by Vitis • 1.6k

what would you call consistent results? We routinely see over-representation of FPs near the splice junctions for rnaSeq SNV calls. And this is comparing the data to dna-seq making sure variants have good enough coverage of reads for a confident SNV call

ADD REPLY • link written 6.5 years ago by Bioinfosm • 610

Well, in our system, or more properly, evolutionary scale, CNVs are very rare. SNPs and indels are the vast majority of variant types. I have no idea and experience of CNVs.

ADD REPLY • link written 6.5 years ago by Vitis • 1.6k

do we not need to go for any normalization method before calling variations on mRNA Seq data

ADD REPLY • link written 6.1 years ago by bharati.mehani • 0

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