School of Life Sciences and the State Key Laboratory of Agrobiotechnology.
Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.
Bioinformatics. 2017 Oct 15;33(20):3166-3172. 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.
Supplementary data are available at Bioinformatics online.
尽管已经提出了高通量测序方法来鉴定人类基因组中的剪接分支点,但这些方法只能检测到测序深度、实验成本和 mRNA 表达水平所限制的一小部分分支点。因此,准确的分支点预测计算模型是人类基因组研究中的一个持续目标。
我们在这里提出了一种新的分支点预测算法,该算法利用了分支点序列和多嘧啶 tract 的信息。使用经过实验验证的数据,我们证明了我们提出的方法优于现有方法。
https://github.com/zhqingit/BPP。
补充数据可在 Bioinformatics 在线获取。