Bayesian nonparametric discovery of isoforms and individual specific quantification

Derek Aguiar (), Li-Fang Cheng, Bianca Dumitrascu, Fantine Mordelet, Athma A. Pai and Barbara E. Engelhardt ()
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Derek Aguiar: Princeton University
Li-Fang Cheng: Princeton University
Bianca Dumitrascu: Princeton University
Fantine Mordelet: Duke University
Athma A. Pai: Massachusetts Institute of Technology
Barbara E. Engelhardt: Princeton University

Nature Communications, 2018, vol. 9, issue 1, 1-12

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.

Date: 2018
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DOI: 10.1038/s41467-018-03402-w

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