RNA-SSNV: A Reliable Somatic Single Nucleotide Variant Identification Framework for Bulk RNA-Seq Data.

ABSTRACT: The usage of expressed somatic mutations may have a unique advantage in identifying active cancer driver mutations. However, accurately calling mutations from RNA-seq data is difficult due to confounding factors such as RNA-editing, reverse transcription, and gap alignment. In the present study, we proposed a framework (named RNA-SSNV, https://github.com/pmglab/RNA-SSNV) to call somatic single nucleotide variants (SSNV) from tumor bulk RNA-seq data. Based on a comprehensive multi-filtering strategy and a machine-learning classification model trained with comprehensively curated features, RNA-SSNV achieved the best precision-recall rate (0.880-0.884) in a testing dataset and robustly retained 0.94 AUC for the precision-recall curve in three validation adult-based TCGA (The Cancer Genome Atl

SUBMITTER: Long Q 

PROVIDER: S-EPMC9279659 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature