RNA-seq SNP calling softwore

[huangjun]

Hello everyone,
Recently, i began to working on RNA-seq analysis. To one species, if i have some assembled transcripts, how can i use this data to SNP calling, could you give me some suggestions or which softwore should i choose?
Thank you!

[edge]

Hi huangjun,

Do you figure out the solution for your doubt?
Currently I'm facing the same problem as well.
I have a Illumina RNA-seq pair-end read, reference transcriptome.
However, I have no idea how to get the SNP result from my data set.
Thanks for any advice.

[figo1019]

Hi huangjun,

Do you figure out the solution for your doubt?
Currently I'm facing the same problem as well.
I have a Illumina RNA-seq pair-end read, reference transcriptome.
However, I have no idea how to get the SNP result from my data set.
Thanks for any advice.

Hi edge

Have you figured it out that how to get the SNP result from RNA seq data. I also want to know.

regards

[bruce01]

I have done this with both GATK and SAMtools mpileup (also using VarScan). There is a lot of good help available for each method here on seqAnswers and on the GATK website. GATK is quite fiddly to run as I remember, the SAMtools/VarScan way was easier but does not address the potential issue of realigning data once quality scores have been used to remove data. This is particularly necessary as 'SNPs' may just be bad sequence, or indeed RNA-editing, although the scale of RNA-editing is still not really known...

For what its worth I did not find much that was useful (I had ~40m reads per sample, ref genome ~3GB). I think looking at alternative splicing might be more worthwhile.

[kopi-o]

There is no really good solution for SNP calling from RNA-seq data at this time, as far as I know.

EDIT: I mean, samtools pileup and GATK can be used, but they were developed for genomic resequencing. RNA-seq shows a much higher variability in read counts from locus to locus, and SNV calling for it is complicated by things like RNA editing (as bruce01 mentioned).

[[email protected]]

Hi,

Were you aware that Partek Flow can call SNPs from RNA-seq data BOTH by comparing against a reference AND across patient samples!?
Furthermore Partek can combine SNV detection with expression and perform allele-specific expression.

Would this help you? You can get your free temporary copy of Partek Flow here:

http://www.partek.com/freetrial

[raving]

I have done this with both GATK and SAMtools mpileup (also using VarScan). There is a lot of good help available for each method here on seqAnswers and on the GATK website. GATK is quite fiddly to run as I remember, the SAMtools/VarScan way was easier but does not address the potential issue of realigning data once quality scores have been used to remove data. This is particularly necessary as 'SNPs' may just be bad sequence, or indeed RNA-editing, although the scale of RNA-editing is still not really known...

For what its worth I did not find much that was useful (I had ~40m reads per sample, ref genome ~3GB). I think looking at alternative splicing might be more worthwhile.

did you mean that realign is necessary for SNPs calling? why?

[bruce01]

Yes, recalibration and realignment is necessary using GATK. This is because if a SNP occurs that is not in your reference genome the alignment score will be lower due to an apparent mismatch. However if you recalibrate alignment scores based on a SNP flatfile (vcf) then you can realign and the alignment will have a higher score. The same principle applies not just for SNPs but for indels, rearrangements. That is the theory anyway, and it is also why GATK is the 'golden standard' for SNP calling.

[BobbyKing]

Tutorial website

Hi,

I have put together a tutorial website with four core tutorials on it, RNA-Seq, ChIP-Seq, Genome assembly, and SNP calling that may be of use to you.

This website was created to share bioinformatics tutorials and create a dynamic learning environment that does not become dated, PDF contributions welcome and there are four core tutorials available. We would be interested to get some feedback.

http://elvis.ccc.cranfield.ac.uk/CUBELP/faces/login-page.xhtml