ArticleNature communications2018
Bayesian nonparametric discovery of isoforms and individual specific quantification.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bayesian reconstruction and differential testing of excised introns.Bioinformatics (Oxford, England) · 2025Article
- Discovery of Novel Protein-Coding and Long Non-coding Transcripts in Distinct Regions of the Human Brain.Journal of molecular neuroscience : MN · 2025Article
- Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures.Bioinformatics (Oxford, England) · 2023Article
- Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data.Nature biotechnology · 2022Article
- IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.Bioinformatics (Oxford, England) · 2021Article
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Authors and funding
6 authors.
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Abstract
Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop BIISQ, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. BIISQ does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. BIISQ shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios.
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Registered trials
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