Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data. (24th June 2019)

• Record Type: Journal Article
• Authors: Lima, Leandro; Marchet, Camille; Caboche, Ségolène; Da Silva, Corinne; Istace, Benjamin; Aury, Jean-Marc; Touzet, Hélène; Chikhi, Rayan
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 4(2020)
• Page Start: 1164
• Page End: 1181
• Publication Date: 2019-06-24
• Subjects: long reads -- RNA-sequencing -- Nanopore -- error correction -- benchmark; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz058
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. Results: In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. Benchmarking software: https://gitlab.com/leoisl/LR_EC_analyser