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About BRIE

Welcome to the new BRIE2 (Bayesian regression for isoform estimate, v2), a scalable Bayesian method to robustly identify splicing phenotypes in single cells RNA-seq designs and accurately estimate isoform proportions and its uncertainty.

BRIE2 supports isoform quantification for different needs:

  1. cell features: informative prior is learned from shared cell processes. It also allows to effectively detect splicing phenotypes by using Evidence Lower Bound gain, an approximate of Bayes factor.
  2. gene features: informative prior is learned from shared gene regulatory features, e.g., sequences and RNA protein binding
  3. no feature: use zero-mean logit-normal as uninformative prior, namely merely data deriven

Note, BRIE1 CLI is still available in this version but changed to brie1 and brie1-diff.

Questions or Bugs

If you find any error or suspicious bug, we will appreciate your report. Please write them in the github issues:

If you have questions on using BRIE, feel free get in touch with us: yuanhua <at>


Yuanhua Huang and Guido Sanguinetti. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biology, 2017; 18(1):123.