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Related Experiment Video

Updated: Mar 1, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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An efficient method for significant motifs discovery from multiple DNA sequences.

Abdulrakeeb M Al-Ssulami1, Aqil M Azmi1, Hassan Mathkour1

  • 11 Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Journal of Bioinformatics and Computational Biology
|June 3, 2017
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm for discovering biological motifs, which are crucial for understanding gene regulation. Our method efficiently identifies significant motifs, outperforming existing tools like MEME and Weeder.

Keywords:
DNA motifsM. tuberculosisalgorithmssequence analysis

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying transcription factor binding sites (biological motifs) is essential for understanding gene regulation.
  • Developing efficient and accurate motif discovery tools remains a significant challenge in bioinformatics.

Purpose of the Study:

  • To devise a novel, efficient, and accurate three-phase algorithm for mining biologically significant motifs.
  • To compare the performance of the new algorithm against established motif discovery tools.

Main Methods:

  • A three-phase approach: 1. Generating all possible string motifs. 2. Filtering motifs based on predefined constraints. 3. Scoring and ranking motifs using stochastic techniques and p-values.
  • Benchmarking against popular tools like MEME and Weeder using established datasets.
  • Application to non-coding regions of Mycobacterium tuberculosis.

Main Results:

  • The algorithm demonstrated superior performance compared to MEME and Weeder on benchmark datasets.
  • Significant motifs of size 10 were identified in the non-coding regions of M. tuberculosis with excellent p-values.
  • The new method identified five of the top 10 motifs (by p-value) in M. tuberculosis, including the 3rd and 4th best.
  • Achieved results significantly faster than MEME and MoSDi (1/17th and 1/6th the time, respectively).

Conclusions:

  • The developed three-phase algorithm is an efficient and effective tool for biological motif discovery.
  • This method offers a significant advancement in deciphering gene regulation mechanisms.
  • The algorithm shows promise for applications in microbial genomics and identifying regulatory elements.