Abstract
SummaryGenome sequencing projects annotate protein-coding gene models with multiple transcripts, aiming to represent all of the available transcript evidence. However, downstream analyses often operate on only one representative transcript per gene locus, sometimes known as the canonical transcript. To choose canonical transcripts, Transcript Ranking and Canonical Election (TRaCE) holds an ‘election’ in which a set of RNA-seq samples rank transcripts by annotation edit distance. These sample-specific votes are tallied along with other criteria such as protein length and InterPro domain coverage. The winner is selected as the canonical transcript, but the election proceeds through multiple rounds of voting to order all the transcripts by relevance. Based on the set of expression data provided, TRaCE can identify the most common isoforms from a broad expression atlas or prioritize alternative transcripts expressed in specific contexts.Availability and implementationTranscript ranking code can be found on GitHub at {{https://github.com/warelab/TRaCE}}.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Genome sequencing projects often use complex, automated annotation pipelines to build reference sets of gene models
The winner is selected as the canonical transcript, but the election proceeds through multiple rounds of voting to order all the transcripts by relevance
Before a project releases a set of high-confidence gene models, additional filtering steps may remove transcript models that lack homology or are subject to nonsensemediated degradation (NMD)
Summary
Genome sequencing projects often use complex, automated annotation pipelines to build reference sets of gene models. These pipelines mask repeats in the assembled genome, align protein and transcript evidence, and build gene models by aggregating overlapping alignments that adhere to known or inferred splice site patterns (Hoff et al 2019; Campbell et al.2014; Haas et al 2003). 2003); and new sequencing technology such as PacBio IsoSeq can capture splice variants at an unprecedented scale (Wang et al 2016; Zhang et al 2019; Bruijnesteijn et al 2018). This heightened sensitivity can lead to the detection of transcriptional noise, which can be misreported by gene builders as biologically. It is possible for partially processed transcripts containing retained introns that neither disrupt the reading frame nor introduce stop codons to be promoted to canonical transcripts (Figure 1)
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