Optimizing error correction of RNAseq reads

Abstract

MotivationThe correction of sequencing errors contained in Illumina reads derived from genomic DNA is a common pre-processing step in many de novo genome assembly pipelines, and has been shown to improved the quality of resultant assemblies. In contrast, the correction of errors in transcriptome sequence data is much less common, but can potentially yield similar improvements in mapping and assembly quality. This manuscript evaluates several popular read-correction tools ability to correct sequence errors commonplace to transcriptome derived Illumina reads.\n\nResultsI evaluated the efficacy of correction of transcriptome derived sequencing reads using using several metrics across a variety of sequencing depths. This evaluation demonstrates a complex relationship between the quality of the correction, depth of sequencing, and hardware availability which results in variable recommendations depending on the goals of the experiment, tolerance for false positives, and depth of coverage. Overall, read error correction is an important step in read quality control, and should become a standard part of analytical pipelines.\n\nAvailabilityResults are non-deterministically repeatable using AMI:ami-3dae4956 (MacManes_EC_2015) and the Makefile available here: https://goo.gl/oVIuE0\n\nContactmatthew.macmanes@unh.edu and @PeroMHC