Patsakis Michail, Provatas Kimonas, Baltoumas Fotis A, Chantzi Nikol, Mouratidis Ioannis, Pavlopoulos Georgios A, Georgakopoulos-Soares Ilias
Institute for Personalized Medicine, Department of Molecular and Precision Medicine, The Pennsylvania State University College of Medicine, Hershey, PA 17033, United States.
Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, United States.
Bioinformatics. 2025 Mar 29;41(4). doi: 10.1093/bioinformatics/btaf125.
Whole Genome and Proteome Alignments, represented by the multiple alignment file format, have become a standard approach in comparative genomics and proteomics. These often require identifying conserved motifs, which is crucial for understanding functional and evolutionary relationships. However, current approaches lack a direct method for motif detection within MAF files. We present MAFin, a novel tool that enables efficient motif detection and conservation analysis in MAF files to address this gap, streamlining genomic and proteomic research.
We developed MAFin, the first motif detection tool for Multiple Alignment Format files. MAFin enables the multithreaded search of conserved motifs using three approaches: (i) using user-specified k-mers to search the sequences. (ii) with regular expressions, in which case one or more patterns are searched, and (iii) with predefined Position Weight Matrices. Once the motif has been found, MAFin detects the motif instances and calculates the conservation across the aligned sequences. MAFin also calculates a conservation percentage, which provides information about the conservation levels of each motif across the aligned sequences, based on the number of matches relative to the length of the motif. A set of statistics enables the interpretation of each motif's conservation level, and the detected motifs are exported in JSON and CSV files for downstream analyses.
MAFin is offered as a Python package under the GPL license as a multi-platform application and is available at: https://github.com/Georgakopoulos-Soares-lab/MAFin.
以多重比对文件格式表示的全基因组和蛋白质组比对已成为比较基因组学和蛋白质组学中的标准方法。这些方法通常需要识别保守基序，这对于理解功能和进化关系至关重要。然而，目前的方法缺乏在MAF文件中直接检测基序的方法。我们提出了MAFin，这是一种新颖的工具，能够在MAF文件中进行高效的基序检测和保守性分析，以填补这一空白，简化基因组学和蛋白质组学研究。
我们开发了MAFin，这是首个用于多重比对格式文件的基序检测工具。MAFin能够使用三种方法对保守基序进行多线程搜索：（i）使用用户指定的k-mer搜索序列。（ii）使用正则表达式，在这种情况下搜索一个或多个模式，以及（iii）使用预定义的位置权重矩阵。一旦找到基序，MAFin会检测基序实例并计算比对序列中的保守性。MAFin还会计算保守百分比，该百分比基于与基序长度相关的匹配数，提供有关每个基序在比对序列中的保守水平的信息。一组统计数据能够解释每个基序的保守水平，并且检测到的基序会以JSON和CSV文件形式导出，以便进行下游分析。
MAFin作为一个遵循GPL许可的Python包提供，是一个多平台应用程序，可在以下网址获取：https://github.com/Georgakopoulos-Soares-lab/MAFin。
