Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls.

ABSTRACT: Sequencing errors are a major issue for several next-generation sequencing-based applications such as de novo assembly and single nucleotide polymorphism detection. Several error-correction methods have been developed to improve raw data quality. However, error-correction performance is hard to evaluate because of the lack of a ground truth. In this study, we propose a novel approach which using ERCC RNA spike-in controls as the ground truth to facilitate error-correction performance evaluation. After aligning raw and corrected RNA-seq data, we characterized the quality of reads by three metrics: mismatch patterns (i.e., the substitution rate of A to C) of reads aligned with one mismatch, mismatch patterns of reads aligned with two mismatches and the percentage increase of reads aligned to

SUBMITTER: Tong L 

PROVIDER: S-EPMC4983418 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature