Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring.

ABSTRACT:

Background

Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias.

Results

We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting G

SUBMITTER: Chu Y 

PROVIDER: S-EPMC5907144 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature