TranSuite: a software suite for accurate translation and characterization of transcripts
Bibliographic record
Abstract
ABSTRACT Protein translation programs often select the longest open reading frame (ORF) in a transcript leading to numerous inaccurate and mis-annotated ORFs in databases. Unproductive transcript isoforms containing premature termination codons (PTCs) are potential substrates for nonsense-mediated decay (NMD). These transcripts often contain truncated ORFs but are incorrectly annotated due to selection of a long ORF beginning at an AUG downstream of the PTC despite the transcript containing the authentic translation start AUG. In gene expression and alternative splicing analyses, it is important to identify transcript isoforms which code for different protein variants and to distinguish these from potential NMD substrates. Here, we present TranSuite, a pipeline of bioinformatics tools that address these challenges by performing accurate translations, characterizing alternative ORFs and identifying NMD and other features of transcripts in newly assembled and existing transcriptomes. Directly comparing ORFs defined by TranSuite and TransDecoder for the Arabidopsis transcriptome AtRTD2 identified ORF mis-calling in over 16k (27%) of transcripts by TransDecoder.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".