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- W2605455299 abstract "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." @default.
- W2605455299 created "2017-04-28" @default.
- W2605455299 creator A5022187570 @default.
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- W2605455299 creator A5077415818 @default.
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- W2605455299 date "2018-04-27" @default.
- W2605455299 modified "2023-10-16" @default.
- W2605455299 title "Bayesian nonparametric discovery of isoforms and individual specific quantification" @default.
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- W2605455299 doi "https://doi.org/10.1038/s41467-018-03402-w" @default.
- W2605455299 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5923247" @default.
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- W2605455299 hasPublicationYear "2018" @default.
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