BPP: a sequence-based algorithm for branch point prediction

• Published in: Bioinformatics (Oxford, England) Vol. 33; no. 20; pp. 3166 - 3172
• Main Authors: Zhang, Qing, Fan, Xiaodan, Wang, Yejun, Sun, Ming-an, Shao, Jianlin, Guo, Dianjing
• Format: Journal Article
• Language: English
• Published: England 15.10.2017
• Subjects: Algorithms; Computational Biology - methods; Genome, Human; High-Throughput Nucleotide Sequencing - methods; Humans; RNA Splicing; Sequence Analysis, RNA - methods; Software
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btx401

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. 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. djguo@cuhk.edu.hk. Supplementary data are available at Bioinformatics online.