Efficient and accurate detection of splice junctions from RNAseq with Portcullis

Next generation sequencing (NGS) technologies enable rapid and cheap genome-wide transcriptome analysis, providing vital information about gene structure, transcript expression and alternative splicing. Key to this is the the accurate identification of exon-exon junctions from RNA sequenced (RNA-seq) reads. A number of RNA-seq aligners capable of splitting reads across these splice junctions (SJs) have been developed, however, it has been shown that while they correctly identify most genuine SJs available in a given sample, they also often produce large numbers of incorrect SJs. Herein we describe the extent of this problem using popular RNA-seq mapping tools, and present a new method, called Portcullis, to rapidly filter false SJs junctions from spliced alignments produced by any RNA-seq mapper capable of creating SAM/BAM files. We show that Portcullis distinguishes between genuine and false positive junctions to a high-degree of accuracy across different species, samples, expression levels, error profiles and read lengths. Portcullis makes efficient use of memory and threading and, to our knowledge, is currently the only SJ prediction tool that reliably scales for use with large RNAseq datasets and large highly fragmented genomes, whilst delivering highly accurate SJs.

Portcullis is available under the GPLv3 license at: http://maplesond.github.io/portcullis/

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Additional Info

Field Value
Author Mapleson, Daniel
Last Updated November 20, 2019, 16:45 (UTC)
Created August 1, 2019, 10:29 (UTC)
Article Host Type publisher
Article Is Open Access true
Article License Type cc-by-nc-nd
Article Version Type publishedVersion
Citation Report https://scite.ai/reports/10.1101/217620
DOI 10.1101/217620
Date Last Updated 2019-06-15T22:03:01.647649
Evidence open (via page says license)
Funder code(s)
Journal Is Open Access false
Open Access Status hybrid
PDF URL https://www.biorxiv.org/content/biorxiv/early/2017/11/10/217620.full.pdf
Publisher URL https://doi.org/10.1101/217620