Bayesian nonparametric discovery of isoforms and individual specific quantification View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Derek Aguiar, Li-Fang Cheng, Bianca Dumitrascu, Fantine Mordelet, Athma A. Pai, Barbara E. Engelhardt

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. More... »

PAGES

1681

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-018-03402-w

    DOI

    http://dx.doi.org/10.1038/s41467-018-03402-w

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103613886

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29703885


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