EDIT: I am updating this question to make it more specific to my issue.
For context - original question prior to edit:
How do I obtain a deamination metric when doing the variant calling using the IonTorrent variant caller, and secondly, how do I correct my called variants for deamination to ensure that these don't provide false positives for the downstream analysis?
I'm calling variants using the IonTorrent TorrentSuite on DNA which has been sequenced from formalin-fixed paraffin-embedded (FFPE) tissue. This has a major issue in that without addition of uracil-N-glycosylase, some of the Ts in the original DNA are deaminated to Cs, which upon sequencing and calling variants can show up as mutations, either as T>C transitions or G>A (from the opposite strand, due to PCR in the library prep). I do not have any idea how long these samples were stored without UNG before sequencing.
TVC (Torrent Variant Caller) gives a deamination metric (essentially, sum of T>C and G>A variants over all variants called), and for our samples, the highest value seen is ~0.92. Naively postprocessing the variants show that for these samples, C>T/T>C transitions overwhelm the remaining variants among my samples.
My use case is this: these are medical samples, which have been inspected by a pathologist (hence the FFPE treatment), and I want to determine which variants are predictive of outcome, hence I have two potentially contradictory goals: reduce false positives and capture the rarer variants which may hold predictive power.
- Given the IonTorrent variant calling pipeline (sequencing > BAM file > TVC > VCF file with deamination statistic):
- (Q1) Is there a way of correcting the output VCF to remove transition and deamination errors?
- (Q2) Alternatively, is there a set of filters to use in bcftools to reduce the effect of the errors on how variants are called?