MOTIVATION: Although high-throughput sequencing methods have been proposed to 
identify splicing branch points in the human genome, these methods can only 
detect a small fraction of the branch points subject to the sequencing depth, 
experimental cost and the expression level of the mRNA. An accurate 
computational model for branch point prediction is therefore an ongoing 
objective in human genome research.
RESULTS: We here propose a novel branch point prediction algorithm that utilizes 
information on the branch point sequence and the polypyrimidine tract. Using 
experimentally validated data, we demonstrate that our proposed method 
outperforms existing methods. Availability and implementation: 
https://github.com/zhqingit/BPP.
CONTACT: djguo@cuhk.edu.hk.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.