  Most human protein-coding genes can be transcribed into multiple possible
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 exist in
tissue- and sample-specific frequencies. Here, we develop BIISQ, a Bayesian
nonparametric model to discover Isoforms and Individual Specific Quantification
from RNA-seq data. BIISQ does not require known isoform reference sequences but
instead estimates isoform composition directly with an isoform catalog shared
across samples. We develop a stochastic variational inference approach for
efficient and robust posterior inference and demonstrate superior precision and
recall for short read RNA-seq simulations and simulated short read data from
PacBio long read sequencing when compared to state-of-the-art isoform
reconstruction methods. BIISQ achieves the most significant gains for longer
(in terms of exons) isoforms and isoforms that are lowly expressed (over 500%
more transcripts correctly inferred at low coverage in simulations). Finally,
we estimate isoforms in the GEUVADIS RNA-seq data, identify genetic variants
that regulate transcript ratios, and demonstrate variant enrichment in
functional elements related to mRNA splicing regulation.
