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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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PMS6: a fast algorithm for motif discovery.

Shibdas Bandyopadhyay1, Sartaj Sahni1, Sanguthevar Rajasekaran2

  • 1Department of CISE, University of Florida, Gainesville, FL 32611, USA.

International Journal of Bioinformatics Research and Applications
|July 4, 2014
PubMed
Summary
This summary is machine-generated.

We introduce PMS6, a novel algorithm for (l,d)-motif discovery. This new method significantly outperforms the previous fastest algorithm, PMS5, in both runtime and preprocessing time for identifying sequence motifs.

Keywords:
PMS6approximate string searchbioinformaticsmotif discoverymotif searchplanted motifs

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The (l,d)-motif discovery problem is crucial for identifying conserved DNA or protein sequences.
  • Existing algorithms like PMS5 face challenges with large datasets and significant mismatch tolerances.
  • Efficient motif discovery is essential for understanding gene regulation and protein function.

Purpose of the Study:

  • To develop and evaluate a new, more efficient algorithm for the (l,d)-motif discovery problem.
  • To compare the performance of the proposed PMS6 algorithm against the state-of-the-art PMS5 algorithm.
  • To assess the impact of preprocessing time on the overall efficiency of motif discovery algorithms.

Main Methods:

  • Development of the PMS6 algorithm for (l,d)-motif discovery.
  • Comparative analysis of PMS6 and PMS5 algorithms using various (l,d) challenge instances.
  • Measurement of runtime and preprocessing time for both algorithms.
  • Calculation of runtime ratios (PMS5/PMS6) to quantify performance differences.

Main Results:

  • PMS6 demonstrates a significant runtime advantage over PMS5, with ratios ranging from 1.69 to 2.20.
  • PMS6 offers a substantial improvement in preprocessing time, being up to 34 times faster than PMS5 for certain instances.
  • When factoring in preprocessing, the overall efficiency gain of PMS6 over PMS5 is considerable, with ratios up to 2.75.

Conclusions:

  • PMS6 represents a significant advancement in (l,d)-motif discovery, offering superior performance compared to PMS5.
  • The new algorithm provides substantial speedups in both execution and setup time, making it suitable for large-scale biological data analysis.
  • PMS6 is a more efficient tool for identifying conserved sequence patterns with mismatches, aiding biological research.