Improving transcriptome assembly through error correction of high-throughput sequence reads

TL;DR

This paper demonstrates that applying error correction to high-throughput sequencing reads significantly improves transcriptome assembly accuracy, reducing errors by nearly 50%, which is crucial for functional genomics studies.

Contribution

It provides the first detailed analysis of how error correction impacts de novo transcriptome assembly accuracy using simulated data.

Findings

Error correction reduces assembly errors by nearly 50%.

Applying error correction improves the quality of transcriptome assemblies.

Error correction should be standard practice for sequencing datasets.

Abstract

The study of functional genomics--particularly in non-model organisms has been dramatically improved over the last few years by use of transcriptomes and RNAseq. While these studies are potentially extremely powerful, a computationally intensive procedure--the de novo construction of a reference transcriptome must be completed as a prerequisite to further analyses. The accurate reference is critically important as all downstream steps, including estimating transcript abundance are critically dependent on the construction of an accurate reference. Though a substantial amount of research has been done on assembly, only recently have the pre-assembly procedures been studied in detail. Specifically, several stand-alone error correction modules have been reported on, and while they have shown to be effective in reducing errors at the level of sequencing reads, how error correction impacts assembly accuracy is largely unknown. Here, we show via use of a simulated dataset, that applying error correction to sequencing reads has significant positive effects on assembly accuracy, by reducing assembly error by nearly 50%, and therefore should be applied to all datasets.