Bayesian nonparametric discovery of isoforms and individual specific quantification

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

doi:10.1038/s41467-018-03402-w

Nature Communications (Apr 2018)

Bayesian nonparametric discovery of isoforms and individual specific quantification

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

Affiliations

Derek Aguiar: Department of Computer Science, Princeton University
Li-Fang Cheng: Department of Electrical Engineering, Princeton University
Bianca Dumitrascu: Lewis-Sigler Institute, Princeton University
Fantine Mordelet: Institute for Genome Sciences and Policy, Duke University
Athma A. Pai: Department of Biology, Massachusetts Institute of Technology
Barbara E. Engelhardt: Department of Computer Science, Princeton University

DOI: https://doi.org/10.1038/s41467-018-03402-w
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 12

Abstract

Read online

Alternative splicing leads to transcript isoform diversity. Here, Aguiar et al. develop biisq, a Bayesian nonparametric approach to discover and quantify isoforms from RNA-seq data.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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