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Bioinformatics. 2018 Mar 1;34(5):748-754. doi: 10.1093/bioinformatics/btx668.

Evaluation of tools for long read RNA-seq splice-aware alignment.

Author information

1
Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia.
2
Département d'Ecologie et d'Evolution, Université de Lausanne, Quartier Sorge, 1015 Lausanne, Switzerland.
3
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
4
Bioinformatics Institute, Singapore 138671, Singapore.

Abstract

Motivation:

High-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads.

Results:

The tools were tested on synthetic and real datasets from two technologies (PacBio and ONT MinION). Alignment quality and resource usage were compared across different aligners. The effect of error correction of long reads was explored, both using self-correction and correction with an external short reads dataset. A tool was developed for evaluating RNA-seq alignment results. This tool can be used to compare the alignment of simulated reads to their genomic origin, or to compare the alignment of real reads to a set of annotated transcripts. Our tests show that while some RNA-seq aligners were unable to cope with long error-prone reads, others produced overall good results. We further show that alignment accuracy can be improved using error-corrected reads.

Availability and implementation:

https://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391.

Contact:

mile.sikic@fer.hr.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29069314
PMCID:
PMC6192213
[Available on 2019-03-01]
DOI:
10.1093/bioinformatics/btx668
[Indexed for MEDLINE]

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