PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events

Abstract

BackgroundTranscripts, which have been subject to Post-transcriptional exon shuffling (PTES), have an exon order inconsistent with the underlying genomic sequence. These have been identified in a wide variety of tissues and cell types from many eukaryotes, and are now known to be mostly circular, cytoplasmic, and non-coding. Although there is no uniformly ascribed function, several have been shown to be involved in gene regulation. Accurate identification of these transcripts can, however, be difficult due to artefacts from a wide variety of sources.ResultsHere, we present a computational method, PTESFinder, to identify these transcripts from high throughput RNAseq data. Uniquely, it systematically excludes potential artefacts emanating from pseudogenes, segmental duplications, and template switching, and outputs both PTES and canonical exon junction counts to facilitate comparative analyses. In comparison with four existing methods, PTESFinder achieves highest specificity and comparable sensitivity at a variety of read depths. PTESFinder also identifies between 13 % and 41.6 % more structures, compared to publicly available methods recently used to identify human circular RNAs.ConclusionsWith high sensitivity and specificity, user-adjustable filters that target known sources of false positives, and tailored output to facilitate comparison of transcript levels, PTESFinder will facilitate the discovery and analysis of these poorly understood transcripts.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0881-4) contains supplementary material, which is available to authorized users.

Islet-expressed circular RNAs are associated with type 2 diabetes status in human primary islets and in peripheral blood

“…This study reports more islet circRNAs than identified in our dataset, but this profile is derived from conventional NGS data, with no pre-treatment to remove linear sequences. Back splicing events can be generated from tandem DNA duplications within genes, or from trans-splicing events during linear splicing [26], so it is likely that profiles derived from conventional NGS contain sequences that in fact represent aberrantly spliced linear transcripts rather than genuine circRNAs. Differences will also arise in that this previous circRNA profile derives from isolated α, β and δ cell populations, whereas ours is a profile derived from intact islets.…”

Circular RNAs: A Novel Class of Functional RNA Molecules with a Therapeutic Perspective

“…Several bioinformatic algorithms were developed that allow sequence alignment of reads covering the backsplice site to the reference genome and, thereby, detecting non-linear splice events. Widely used examples include CIRCexplorer, 19 CIRI, 41,42 find_circ, 3 circRNA_finder, 21 MapSplice, 43 DCC, 44 acfs, 35 KNIFE, 45 miARma-Seq, 46 PTESFinder, 47 Segemehl, 48 NCLScan, 49 and UROBORUS. 50 While classic alignment tools reject all reads that do not arise from linear splicing, these circRNA detection tools filter for linear notmappable reads that connect one exon with an upstream exon.…”

circRNAs expressed in human peripheral blood are associated with human aging phenotypes, cellular senescence and mouse lifespan

“…RNase R and mock-treated sequence data were assembled, and putative circular RNAs were identified using PTESFinder (Izuogu et al 2016) with the human genome (hg19) reference files provided with the software, a segment size of 65 and a uniqueness score of 7. The remaining parameters were left to default settings.…”