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Improving MEME via a two-tiered significance analysis.

Emi Tanaka1, Timothy L Bailey2, Uri Keich2

  • 1School of Mathematics and Statistics, University of Sydney, Sydney 2006, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong 2522, New South Wales and Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, AustraliaSchool of Mathematics and Statistics, University of Sydney, Sydney 2006, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong 2522, New South Wales and Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia.

Bioinformatics (Oxford, England)
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Summary

This study introduces a novel two-tiered significance analysis to improve motif discovery in the MEME tool. The new method enhances motif finding performance and offers reliable statistical evaluation, outperforming the current E-value approach.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The MEME tool is a widely used motif-finding algorithm with over 9000 users in early 2013.
  • Accurate statistical significance estimation is crucial for motif discovery tools, analogous to E-values in BLAST.
  • MEME currently uses an extension of the E-value for motif significance, which has known drawbacks and is used internally for ranking.

Purpose of the Study:

  • To develop and implement a superior method for evaluating the statistical significance of motifs identified by MEME.
  • To replace the existing E-value-based approach with a more robust and practical significance analysis.
  • To enhance the overall performance and reliability of the MEME motif-finding tool.

Main Methods:

  • A novel two-tiered statistical significance analysis was developed.
  • This new approach was integrated into the MEME motif-finding pipeline.
  • Performance was evaluated against the existing E-value method.

Main Results:

  • The proposed two-tiered analysis effectively replaces the E-value for selecting and evaluating candidate motifs.
  • The new method significantly improves MEME's motif-finding performance.
  • For large datasets, the new significance analysis is faster than the current E-value method.

Conclusions:

  • The developed two-tiered significance analysis provides a reliable and improved method for evaluating motif statistical significance in MEME.
  • This approach enhances the utility of MEME by offering more trustworthy results and better performance.
  • The new method offers computational advantages, particularly for large-scale analyses.