biostudies-literatureMajoros WHNIGMS NIH HHS1958-64https://www.ebi.ac.uk/biostudies/studies/S-EPMC4288128biostudies-literatureUnknown30(14)High-throughput sequencing of RNA in vivo facilitates many applications, not the least of which is the cataloging of variant splice isoforms of protein-coding messenger RNAs. Although many solutions have been proposed for reconstructing putative isoforms from deep sequencing data, these generally take as their substrate the collective alignment structure of RNA-seq reads and ignore the biological signals present in the actual nucleotide sequence. The majority of these solutions are graph-theoretic, relying on a splice graph representing the splicing patterns and exon expression levels indicated by the spliced-alignment process.We show how to augment splice graphs with additional information reflecting the biology of transcription, splicing and translation, to produce what we call an ORF (open reading frame) graph. We then show how ORF graphs can be used to produce isoform predictions with higher accuracy than current state-of-the-art approaches.RSVP is available as C++ source code under an open-source licence: http://ohlerlab.mdc-berlin.de/software/RSVP/.Bioinformatics (Oxford, England)Improved transcript isoform discovery using ORF graphs.PMC4288128T32 GM071340Ohler ULi SLebeck NMajoros WHfalseImproved transcript isoform discovery using ORF graphs.High-throughput sequencing of RNA in vivo facilitates many applications, not the least of which is the cataloging of variant splice isoforms of protein-coding messenger RNAs. Although many solutions have been proposed for reconstructing putative isoforms from deep sequencing data, these generally take as their substrate the collective alignment structure of RNA-seq reads and ignore the biological signals present in the actual nucleotide sequence. The majority of these solutions are graph-theoretic, relying on a splice graph representing the splicing patterns and exon expression levels indicated by the spliced-alignment process.We show how to augment splice graphs with additional information reflecting the biology of transcription, splicing and translation, to produce what we call an ORF (open reading frame) graph. We then show how ORF graphs can be used to produce isoform predictions with higher accuracy than current state-of-the-art approaches.RSVP is available as C++ source code under an open-source licence: http://ohlerlab.mdc-berlin.de/software/RSVP/.2014-01-01T00:00:00Z2014 Jul2021-02-20T12:40:08Z2019-03-27T01:43:03ZS-EPMC42881282465910610.1093/bioinformatics/btu160