BPP: a sequence-based algorithm for branch point prediction
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 computationa...
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| Published in | Bioinformatics (Oxford, England) Vol. 33; no. 20; pp. 3166 - 3172 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
15.10.2017
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| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 |
| DOI | 10.1093/bioinformatics/btx401 |
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| Abstract | 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. |
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| AbstractList | 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.MOTIVATIONAlthough 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.RESULTSWe 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.CONTACTdjguo@cuhk.edu.hk.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. 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. |
| Author | Wang, Yejun Sun, Ming-an Fan, Xiaodan Zhang, Qing Shao, Jianlin Guo, Dianjing |
| Author_xml | – sequence: 1 givenname: Qing surname: Zhang fullname: Zhang, Qing – sequence: 2 givenname: Xiaodan surname: Fan fullname: Fan, Xiaodan – sequence: 3 givenname: Yejun surname: Wang fullname: Wang, Yejun – sequence: 4 givenname: Ming-an surname: Sun fullname: Sun, Ming-an – sequence: 5 givenname: Jianlin surname: Shao fullname: Shao, Jianlin – sequence: 6 givenname: Dianjing surname: Guo fullname: Guo, Dianjing |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28633445$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Computational Biology - methods Genome, Human High-Throughput Nucleotide Sequencing - methods Humans RNA Splicing Sequence Analysis, RNA - methods Software |
| Title | BPP: a sequence-based algorithm for branch point prediction |
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