Medical Mycology Research Center, Chiba University, Chiba, Japan.
Molecular Chirality Research Center, Chiba University, Chiba, Japan.
PLoS One. 2019 Aug 30;14(8):e0220207. doi: 10.1371/journal.pone.0220207. eCollection 2019.
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, BioProspector, 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.
识别转录因子结合位点（TFBSs）非常重要。一些 TFBSs 被提议为二分基序，称为由间隔序列分隔的两个块基序，间隔序列的长度可变。虽然位置权重矩阵（PWM）常用于表示和预测 TFBSs，但二核苷酸权重矩阵（DWM）能够表达相邻碱基的相互依存关系。通过将 DWM 纳入二分基序的检测中，我们使用 Gibbs 采样策略和香农熵最小化，开发了一种新的基于二核苷酸权重矩阵的二分基序检测工具 DIpartite（基于二核苷酸权重矩阵的二分基序检测工具）。DIpartite 通过考虑相邻位置的相互依存关系，即 DWM，来预测二分基序。我们通过使用测试数据集，即大肠杆菌中的 CRP、枯草芽孢杆菌中的 sigma 因子和人类中的启动子序列，将 DIpartite 与其他可用的替代方案进行了比较。我们开发了 DIpartite 来检测 TFBSs，特别是二分基序。DIpartite 不仅可以基于 PWM，还可以基于 DWM 进行保守基序的从头预测。我们通过将 DIpartite 与免费提供的工具（如 MEME、BioProspector、BiPad 和 AMD）进行比较来评估其性能。综合这些发现，DIpartite 的表现与其他工具相当或更好，尤其是在检测具有可变间隔的二分基序时。DIpartite 要求用户指定基序长度、间隔长度以及 PWM 或 DWM。DIpartite 可在 https://github.com/Mohammad-Vahed/DIpartite 上使用。
