I would like to phase pairs of closely situated somatic mutations. Basically, I want to grab the sequencing read IDs supporting the variant allele for each mutation, and compare to see if the same reads report nearby mutations in the pair.
I know that I can view short segments of bam files centered at each pair in IGViewer or other similar tools, and visually verify this. But I am looking for a more systematic way.
Please excuse me if this is too obvious of a question. Any pointers will be appreciated.
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written
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Noushin N
3 answers
The simplest solution would be to use samtools phase. If you only have a few regions of interest (ROIs), then it'll be more efficient to pipe a subset of your original alignment into samtools. This can be done with:
samtools view -bL ROIs.bed alignments.bam | samtools phase [OPTIONS] -
Note that the last - isn't a typo, but rather designates to read from standard input.
If I understand correctly, you want to phase the heterogygous SNPs that are spanned by long reads/ paired-end data.
You can try ReadBackedPhasing from GATK or a recent one, phaser.
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Have you tried
samtools phase?Thank you. I took a quick look and seems relevant. But I am not really interested in doing so on a large scale; and I do not see an option specifying the regions of interest from the bam file. So, I assume I will have to make small bam files around my regions of interest. Is this right?
You might be able to make a BED file with the regions of interest in it and
samtools view -bL regions.bed something.bam | samtools phase ... -. Hopefully that works since it'd be simpler.Thanks for the great suggestion. I am going to give it a try. If It achieves what I am looking for, you should move your comment to an answer :)
It indeed achieves what I need! You have a correct answer here.
Glad it's doing what you need. I made that an answer for the sake of posterity (and so others don't feel compelled to see if this still needs answering).
@Noushin N: Do you have an example script? I tried samtools phase with a bed file of about 3 SNPs, and it resulted in a Segmentation fault :(