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, University of Edinburgh researchers present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. They 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.
A cartoon of the BRIE method for isoform estimation
BRIE combines a likelihood computed from RNA-seq data (bottom part) and an informative prior distribution learned from 735 sequence-derived features (top)
Availability – The BRIE model has been implemented as a standard Python package, which is freely available at http://github.com/huangyh09/brie with Apache License 2.0.