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Novel Sequence Discovery by Subtractive Genomics
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Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments.

Morten Muhlig Nielsen1, Paula Tataru2, Tobias Madsen1,2

  • 11Department of Molecular Medicine (MOMA), Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8000 Aarhus C, Denmark.

Algorithms for Molecular Biology : AMB
|December 18, 2018
PubMed
Summary

Regmex identifies overrepresented motifs in ranked biological sequences using regular expressions and Markov models. This tool enhances motif analysis for functional genomics, offering increased sensitivity for complex motif discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Motif analysis is crucial for understanding nucleotide sequence function in genomics.
  • Functional genomics experiments generate ranked sequence lists, necessitating advanced motif discovery tools.
  • Existing tools have limitations in discovering complex motifs within large datasets.

Purpose of the Study:

  • To develop a motif analysis method tailored for specific, complex motifs in functional genomics.
  • To address the limitations of current motif discovery tools in large-scale analyses.

Main Methods:

  • Introduced Regmex (REGular expression Motif EXplorer), a tool for identifying overrepresented motifs in ranked sequence lists.
  • Utilizes regular expressions for motif definition and embedded Markov models for p-value calculation.
  • Employs random walks and Brownian bridges for evaluating motif distribution biases.

Main Results:

  • Demonstrated Regmex's utility on simulated and microRNA transfection expression data.
  • Confirmed previous findings and validated hypothesis testing for microRNA seed sites and U-rich motifs.
  • Showcased increased sensitivity compared to existing motif discovery tools.

Conclusions:

  • Regmex is a flexible and valuable tool for hypothesis-driven motif analysis in large functional genomics datasets.
  • The method is accessible as an R package, facilitating its application in research.