Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification.

ABSTRACT: Because of incomplete reference transcriptomes, incomplete sequencing bias models, or other modeling defects, algorithms to infer isoform expression from RNA sequencing (RNA-seq) sometimes do not accurately model expression. We present a computational method to detect instances where a quantification algorithm could not completely explain the input reads. Our approach identifies regions where the read coverage significantly deviates from expectation. We call these regions "expression anomalies." We further present a method to attribute their cause to either the incompleteness of the reference transcriptome or algorithmic mistakes. We detect anomalies for 30 GEUVADIS and 16 Human Body Map samples. By correcting anomalies when possible, we reduce the number of falsely predicted instances of

SUBMITTER: Ma C 

PROVIDER: S-EPMC6938679 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature