Department of Pathology & Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), California, USA.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48105, USA.
Brief Bioinform. 2022 Jan 17;23(1). doi: 10.1093/bib/bbab536.
Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).
基序发现和特征描述对于基因调控分析非常重要。缺乏直观和综合的网络服务器会阻碍基序的有效使用。大多数基序发现网络工具要么不是为非专业用户设计的，要么在使用默认设置时缺乏优化步骤。在这里，我们描述了二分图基序学习（BML），这是一个无参数的网络服务器，为在线发现和分析序列基序提供了一个用户友好的门户，使用高通量测序数据作为输入。BML 同时利用位置权重矩阵和二核苷酸权重矩阵，后者能够表达相邻碱基的相互依赖关系。给定基序的输入参数后，BML 实现的基序发现准确性明显高于其他可用工具。当非专业用户没有提供参数时，与其他工具不同，BML 采用学习方法自动识别基序，并实现与设置参数情况下相当的准确性。BML 网络服务器可免费在 http://motif.t-ridership.com/（https://github.com/Mohammad-Vahed/BML）获得。
