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:
- 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.
- gene features: informative prior is learned from shared gene regulatory features, e.g., sequences and RNA protein binding
- 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: https://github.com/huangyh09/brie/issues
If you have questions on using BRIE, feel free get in touch with us: yuanhua <at> hku.hk
Code: GitHub latest version https://github.com/huangyh09/brie
Data: splicing events annotations http://sourceforge.net/projects/brie-rna/files/annotation/
All releases https://pypi.org/project/brie/#history
Issue reports https://github.com/huangyh09/brie/issues
Yuanhua Huang and Guido Sanguinetti. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biology, 2017; 18(1):123.