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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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PairMotif: A new pattern-driven algorithm for planted (l, d) DNA motif search.

Qiang Yu1, Hongwei Huo, Yipu Zhang

  • 1School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.

Plos One
|November 3, 2012
PubMed
Summary

PairMotif is a new exact algorithm for finding transcription factor binding sites (TFBSs) in DNA. It efficiently identifies weak motifs, requiring less memory and time than existing methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Motif search is crucial for identifying transcription factor binding sites (TFBSs) in DNA sequences.
  • Existing exact algorithms struggle with weak motifs due to high memory and time requirements.
  • The planted (l, d) motif search (PMS) problem requires efficient solutions for identifying subtle patterns.

Purpose of the Study:

  • To introduce PairMotif, a novel exact algorithm for the planted (l, d) motif search (PMS) problem.
  • To address the challenge of identifying weak motifs efficiently.
  • To improve upon the performance of existing algorithms in terms of speed and memory usage.

Main Methods:

  • PairMotif utilizes multiple pairs of l-mers with significant distances to constrain the search space.
  • This approach effectively reduces the number of candidate motifs and scanned l-mers.
  • The algorithm's performance is evaluated against several recently proposed PMS algorithms.

Main Results:

  • PairMotif demonstrates reduced storage space and faster execution times on most PMS instances compared to existing methods.
  • It successfully solves the challenging weak instance (27, 9) within 10 hours, a feat not achieved by other compared algorithms.
  • The algorithm exhibits stable performance across varying sequence lengths, enabling motif identification in longer DNA sequences.

Conclusions:

  • PairMotif is an effective and efficient exact algorithm for planted (l, d) motif search.
  • It offers a significant improvement for identifying weak motifs, overcoming limitations of current methods.
  • Experimental results on real biological data validate the algorithm's practical applicability and robustness.