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STEME: efficient EM to find motifs in large data sets.

John E Reid1, Lorenz Wernisch

  • 1MRC Biostatistics Unit, Institute of Public Health, Forvie Site, Robinson Way, Cambridge CB2 0SR, UK. john.reid@mrc-bsu.cam.ac.uk

Nucleic Acids Research
|July 26, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces STEME, a faster algorithm for motif discovery that uses suffix trees to approximate the expectation-maximization (EM) algorithm, significantly speeding up analysis of large biological datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Popular motif finders like MEME utilize the expectation-maximization (EM) algorithm for parameter optimization.
  • The computational complexity of the standard EM algorithm is linear to the input sequence length, limiting its use with large-scale biological data.
  • High-throughput biological techniques generate massive datasets, necessitating more efficient motif discovery methods.

Purpose of the Study:

  • To develop a novel algorithm that accelerates motif elicitation.
  • To address the computational limitations of the expectation-maximization algorithm for large biological sequence datasets.
  • To leverage suffix tree data structures for improved efficiency in motif discovery.

Main Methods:

  • Developed Suffix Tree EM for Motif Elicitation (STEME), an algorithm that approximates EM using suffix trees.
  • Analyzed the expected running time of the STEME algorithm.
  • Assessed the approximation quality theoretically and practically.

Main Results:

  • STEME demonstrates an order of magnitude speedup compared to the EM implementation used by MEME.
  • Theoretical bounds confirm the quality of the approximation.
  • Practical evaluations show a negligible impact of the approximation on motif discovery outcomes.
  • STEME represents the first application of suffix trees to the EM algorithm for motif finding.

Conclusions:

  • STEME significantly enhances the speed of motif discovery, making it suitable for large biological datasets.
  • The suffix tree-based approximation of EM offers a practical solution to computational bottlenecks in motif analysis.
  • An open-source implementation of STEME is provided to facilitate broader adoption and further development in motif search algorithms.