Abstract
BackgroundIn eukaryotes, most genes code for multiple transcript isoforms that are generated through the complex and tightly regulated process of RNA splicing. Despite arising from identical precursor transcripts, alternatively spliced RNAs can have dramatically different functions. Transcriptome complexity is elevated further by the production of circular RNAs (circRNAs), another class of mature RNA that results from the splicing of a downstream splice donor to an upstream splice acceptor. While there has been a rapid expansion of circRNA catalogs in the last few years through the utilization of next generation sequencing approaches, our understanding of the mechanisms and regulation of circular RNA biogenesis, the impact that circRNA generation has on parental transcript processing, and the functions carried out by circular RNAs remains limited.ResultsHere, we present a visualization and analysis tool, SpliceV, that rapidly plots all relevant forward- and back-splice data, with exon and single nucleotide level coverage information from RNA-seq experiments in a publication quality format. SpliceV also integrates analysis features that assist investigations into splicing regulation and transcript functions through the display of predicted RNA binding protein sites and the configuration of repetitive elements along the primary transcript.ConclusionsSpliceV is an easy-to-use splicing visualization tool, compatible with both Python 2.7 and 3+, and distributed under the GNU Public License. The source code is freely available for download at https://github.com/flemingtonlab/SpliceV and can be installed from PyPI using pip.
Highlights
In eukaryotes, most genes code for multiple transcript isoforms that are generated through the complex and tightly regulated process of RNA splicing
The growing precursor RNA is sequentially bound by a myriad of RNA binding proteins (RNABPs) and small nucleolar RNAs as the exon-intron boundaries become defined through these specific ribonucleoprotein complex interactions
In an effort to guide interpretation of gene specific splicing patterns, predicted or empirically determined RNA binding protein binding sites can be added to the plots (Fig. 1b-c; a stepwise tutorial to reproduce these figures is outlined in Additional file 2) by supplying a list of coordinates or utilizing the consensus binding sequences determined by Ray et al [27]
Summary
Multiple computational pipelines have been developed to detect and quantify circRNAs from high throughput RNA sequencing data ([13, 22, 40]; X.-O. [8, 41]). To add utility to SpliceV in transcript biogenesis and isoform function analyses, we incorporated the display of RNA binding protein predictions (Fig. 1b) based on empirically determined binding motifs (Ray et al) and user supplied ALU element sites (Fig. 1c) These features can assist the user in assessing the mechanisms of forward splicing, back splicing, alternative splicing, intron retention, etc. Evident in sample BR-8483, based on the single nucleotide coverage line graph, is extensive intron retention, likely causing the resulting intron retained transcript to be subjected to non-sense mediated RNA decay In both of these cases, SpliceV was able to assist in determining the negative impact of these two mutations on TP53 function, findings that are otherwise opaque to the user
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