Abstract:
Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios bet...Show MoreMetadata
Abstract:
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. We found that, in a GC study, the GC contents and read count ratios on SCNA segments present a Log linear biased pattern. However, currently no subclonal inferring tools take into account this information. We provide Pre-SCNAClonal, a comprehensive GC bias correction tool for inferring tumor subclonal populations based on SCNAs. Results show that Pre-SCNAClonal could effectively and robustly correct the GC bias and improve the performance of the SCNAs based tumor subclonal population inferring tools. Pre-SCNAClonal could be strung together with the SCNAs based subclonal population inferring tool as a pipeline or run individually as needed. Pre-SCNAClonal is publicly available as a Python package: https://github.com/dustincys/Pre-SCNAClonal
Date of Conference: 13-16 November 2017
Date Added to IEEE Xplore: 18 December 2017
ISBN Information: