DIpartite: A tool for detecting bipartite motifs by considering base interdependencies
DIpartite: A tool for detecting bipartite motifs by considering base interdependencies
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DOI：
10.1371/journal.pone.0220207
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发表时间：
2019-08-30
期刊：
影响因子：
3.7
通讯作者：
Takahashi, Hiroki
中科院分区：
文献类型：
--
作者：
Vahed, Mohammad;Ishihara, Jun-ichi;Takahashi, Hiroki
鉴定转录因子结合位点(TFBs)非常重要。一些TFBS被认为是由不同长度的间隙序列分隔的二分基序，称为两块基序。位置权重矩阵(PWM)通常用于TFBs的表示和预测，而二核苷酸权重矩阵(DWM)可以表达相邻碱基之间的相互依赖关系。通过将离散小波变换引入二分基序的检测，我们利用Gibbs抽样策略和Shannon‘s熵最小化的思想，开发了一种新的从头计算基序检测工具DIparte(基于二核苷酸权重矩阵的二分基序检测工具)。DIparte通过考虑相邻位置的相互依赖关系来预测二分基序，即DWM。我们通过使用测试数据集，即大肠杆菌中的CRP、枯草杆菌中的西格玛因子和人类中的启动子序列，将DIparte与其他可用的替代方案进行了比较。我们已经开发了DIparte来检测TFBS，特别是二分基序。DIparte不仅基于PWM，而且基于DWM，能够从头开始预测保守的基序。我们通过将DIparte与免费的工具如Meme、Bio-Prospector、BiPad和AMD进行比较来评估DIparte的性能。综上所述，DIparte的性能与其他工具相当或更好，特别是在检测具有可变间隙的二分基序方面。DIparte要求用户指定基模长度、间隙长度和PWM或DWM。DIparte可在https://github.com/Mohammad-Vahed/DIpartite.上使用
It is extremely important to identify transcription factor binding sites (TFBSs). Some TFBSs are proposed to be bipartite motifs known as two-block motifs separated by gap sequences with variable lengths. While position weight matrix (PWM) is commonly used for the representation and prediction of TFBSs, dinucleotide weight matrix (DWM) enables expression of the interdependencies of neighboring bases. By incorporating DWM into the detection of bipartite motifs, we have developed a novel tool for ab initio motif detection, DIpartite (bipartite motif detection tool based on dinucleotide weight matrix) using a Gibbs sampling strategy and the minimization of Shannon's entropy. DIpartite predicts the bipartite motifs by considering the interdependencies of neighboring positions, that is, DWM. We compared DIpartite with other available alternatives by using test datasets, namely, of CRP in E. coli, sigma factors in B. subtilis, and promoter sequences in humans. We have developed DIpartite for the detection of TFBSs, particularly bipartite motifs. DIpartite enables ab initio prediction of conserved motifs based on not only PWM, but also DWM. We evaluated the performance of DIpartite by comparing it with freely available tools, such as MEME, Bio-Prospector, BiPad, and AMD. Taken the obtained findings together, DIpartite performs equivalently to or better than these other tools, especially for detecting bipartite motifs with variable gaps. DIpartite requires users to specify the motif lengths, gap length, and PWM or DWM. DIpartite is available for use at https://github.com/Mohammad-Vahed/DIpartite.