Journal Article

Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads

Song, Li, Florea, Liliana

2015

Overview

Abstract Background Next-generation sequencing of cellular RNA (RNA-seq) is rapidly becoming the cornerstone of transcriptomic analysis. However, sequencing errors in the already short RNA-seq reads complicate bioinformatics analyses, in particular alignment and assembly. Error correction methods have been highly effective for whole-genome sequencing (WGS) reads, but are unsuitable for RNA-seq reads, owing to the variation in gene expression levels and alternative splicing. Findings We developed a k-mer based method, Rcorrector, to correct random sequencing errors in Illumina RNA-seq reads. Rcorrector uses a De Bruijn graph to compactly represent all trusted k-mers in the input reads. Unlike WGS read correctors, which use a global threshold to determine trusted k-mers, Rcorrector computes a local threshold at every position in a read. Conclusions Rcorrector has an accuracy higher than or comparable to existing methods, including the only other method (SEECER) designed for RNA-seq reads, and is more time and memory efficient. With a 5 GB memory footprint for 100 million reads, it can be run on virtually any desktop or server. The software is available free of charge under the GNU General Public License from https://github.com/mourisl/Rcorrector/.

• Publisher: Oxford University Press,BioMed Central
• Subject: Alternative splicing / Bioinformatics / Biomedical and Life Sciences / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Data Mining and Knowledge Discovery / Error analysis / Error correction / Error correction & detection / Gene expression / Gene sequencing / Graph theory / Human Genetics / Life Sciences / Next-generation sequencing / Proteomics / Ribonucleic acid / RNA / RNA - genetics / Sequence Analysis, RNA - methods / Technical Note / Transcriptomics / Whole genome sequencing