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

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State: Public
Version: Final published version
Serval ID
serval:BIB_2AD9D09B9A6F
Type
Article: article from journal or magazin.
Collection
Publications
Title
Evaluation of tools for long read RNA-seq splice-aware alignment.
Journal
Bioinformatics
Author(s)
Križanovic K., Echchiki A., Roux J., Šikic M.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
01/03/2018
Peer-reviewed
Oui
Volume
34
Number
5
Pages
748-754
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
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.
https://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391.
mile.sikic@fer.hr.
Supplementary data are available at Bioinformatics online.
Keywords
Animals, Drosophila melanogaster/genetics, Gene Expression Profiling/methods, High-Throughput Nucleotide Sequencing/methods, Humans, Saccharomyces cerevisiae/genetics, Sequence Analysis, DNA/methods, Software
Pubmed
Web of science
Create date
05/03/2018 0:52
Last modification date
20/08/2019 14:10
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