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MAFin: motif detection in multiple alignment files.

Michail Patsakis1,2, Kimonas Provatas1,2,3, Fotis A Baltoumas4

  • 1Institute for Personalized Medicine, Department of Molecular and Precision Medicine, The Pennsylvania State University College of Medicine, Hershey, PA 17033, United States.

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|March 19, 2025
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Summary
This summary is machine-generated.

MAFin is a new tool for detecting conserved motifs in Multiple Alignment Format (MAF) files, crucial for comparative genomics and proteomics. It offers efficient motif detection and conservation analysis, streamlining research.

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Area of Science:

  • Genomics
  • Proteomics
  • Bioinformatics

Background:

  • Multiple Alignment Format (MAF) files are standard in comparative genomics and proteomics.
  • Identifying conserved motifs within MAF files is essential for understanding functional and evolutionary relationships.
  • Existing methods lack direct motif detection capabilities for MAF files.

Purpose of the Study:

  • Introduce MAFin, a novel tool for motif detection and conservation analysis in MAF files.
  • Address the gap in current approaches for analyzing conserved motifs within MAF data.
  • Streamline genomic and proteomic research through efficient motif identification.

Main Methods:

  • MAFin is a Python package enabling multithreaded motif searches.
  • Supports motif detection using user-specified k-mers, regular expressions, or Position Weight Matrices.
  • Calculates motif conservation percentages and provides statistics for interpretation.

Main Results:

  • MAFin is the first tool specifically designed for motif detection in MAF files.
  • Successfully identifies motif instances and quantifies conservation across aligned sequences.
  • Exports detected motifs and conservation data in JSON and CSV formats for downstream analysis.

Conclusions:

  • MAFin provides an efficient and versatile solution for motif discovery in MAF files.
  • Enhances the analysis of conserved elements in comparative genomics and proteomics.
  • Facilitates deeper understanding of functional and evolutionary insights from sequence alignments.