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. While 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.
Researchers from Duke University show how to augment splice graphs with additional information reflecting the biology of transcription, splicing, and translation, to produce what they call an ORF (open reading frame) graph. They show how ORF graphs can be used to produce isoform predictions with higher accuracy than current state-of-the-art approaches.
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