ArticleBioinformatics (Oxford, England)2020
Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- The promise of long-read RNA-seq: reducing bias in analyses of allele imbalance.NAR genomics and bioinformatics · 2026Article
- Ontogenetic Sequence of Differential Gene Expression in Predator-Induced Daphnia pulex.Molecular ecology · 2026Article
- Single-cell analysis of signalling and transcriptional responses to type I interferons.EMBO reports · 2026Article
- Isoformic: a workflow for transcript-level RNA-seq interpretation.NAR genomics and bioinformatics · 2025Article
- Tree-based differential testing using inferential uncertainty for RNA-seq.Genome research · 2025Article
- Widespread specific intron retention events in nuclear RNA complexes identified by sedimentation analysis of pluripotent cellular extracts.Genome research · 2025Article
- Tree-based differential testing using inferential uncertainty for RNA-Seq.bioRxiv : the preprint server for biology · 2025Article
- Context-aware transcript quantification from long-read RNA-seq data with Bambu.Nature methods · 2023Article
- SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.Genome biology · 2023Article
- Article
- Deriving Ranges of Optimal Estimated Transcript Expression due to Nonidentifiability.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Compression of quantification uncertainty for scRNA-seq counts.Bioinformatics (Oxford, England) · 2021Article
- Gene Co-Expression Networks Highlight Key Nodes Associated With Ammonium Nitrate in Sugarcane.Physiologia plantarumArticle
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
motivationAdvances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects.
resultsWe introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result. AVAILABILITY AND IMPLEMENTATION: Terminus is implemented in Rust, and is freely available and open source. It can be obtained from https://github.com/COMBINE-lab/Terminus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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