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Updated: Mar 1, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

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

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 motifs M. tuberculosis algorithms sequence analysis

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Last Updated: Mar 1, 2026

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

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.