Abstract
BackgroundDespite the importance of alternative poly-adenylation and 3′ UTR length for a variety of biological phenomena, there are limited means of detecting UTR changes from standard transcriptomic data.ResultsWe present the diffUTR Bioconductor package which streamlines and improves upon differential exon usage (DEU) analyses, and leverages existing DEU tools and alternative poly-adenylation site databases to enable differential 3′ UTR usage analysis. We demonstrate the diffUTR features and show that it is more flexible and more accurate than state-of-the-art alternatives, both in simulations and in real data.ConclusionsdiffUTR enables differential 3′ UTR analysis and more generally facilitates DEU and the exploration of their results.
Highlights
Despite the importance of alternative poly-adenylation and 3′ Untranslated region (UTR) length for a variety of biological phenomena, there are limited means of detecting untranslated regions (UTRs) changes from standard transcriptomic data
Discussion diffUTRstreamlines differential exon usage (DEU) analysis and outperforms alternative methods in inferring UTR changes, which demonstrates the utility of harnessing powerful, well-established frameworks for new ends
It must be noted that the way in which the simulation was performed, i.e. elongating transcripts to the polyA site(s), is similar to the way diffUTRdisjoins the annotation into bins, which could cause a bias towards against methods that do not rely on alternative polyA sites, such as DaPars
Summary
Despite the importance of alternative poly-adenylation and 3′ UTR length for a variety of biological phenomena, there are limited means of detecting UTR changes from standard transcriptomic data. A number of methods for 3′ end sequencing have been developed with the goal to map APA sites [4, 9,10,11,12,13,14], leading to the development of atlases such as PolyASite [15] or PolyA_DB [16]. As such methods are only marginally used, it would be beneficial to leverage the widespread availability of traditional RNA-seq for the purpose of identifying changes in 3′ UTR usage. Methods like DaPars [18] and APAtrap [19] try to infer new polyA sites from read coverage changes
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