BRIE: transcriptome-wide splicing quantification in single cells

Y Huang, G Sanguinetti - Genome biology, 2017 - Springer
Genome biology, 2017Springer
Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity
in transcription, but the limitations of the technology have prevented its application to dissect
variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian
regression for isoform estimation), a Bayesian hierarchical model that resolves these
problems by learning an informative prior distribution from sequence features. We show that
BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an …
Abstract
Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing.
Springer
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