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Bioinformatics. 2017 Oct 15;33(20):3166-3172. doi: 10.1093/bioinformatics/btx401.

BPP: a sequence-based algorithm for branch point prediction.

Author information

1
School of Life Sciences and the State Key Laboratory of Agrobiotechnology.
2
Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.
3
Department of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen 518060, China.
4
First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Abstract

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.

PMID:
28633445
DOI:
10.1093/bioinformatics/btx401
[Indexed for MEDLINE]

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