RNA Therapeutics Institute
Bioinformatics | Computational Biology | Genetic Phenomena | Statistics and Probability
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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© The Author(s) 2018. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
DOI of Published Version
Nat Commun. 2018 Apr 27;9(1):1681. doi: 10.1038/s41467-018-03402-w. Link to article on publisher's site
Aguiar D, Cheng L, Dumitrascu B, Mordelet F, Pai AA, Engelhardt BE. (2018). Bayesian nonparametric discovery of isoforms and individual specific quantification. Open Access Publications by UMass Chan Authors. https://doi.org/10.1038/s41467-018-03402-w. Retrieved from https://escholarship.umassmed.edu/oapubs/3431
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.