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Related Experiment Video

Updated: Feb 16, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Sequence motif finder using memetic algorithm.

Jader M Caldonazzo Garbelini1, André Y Kashiwabara2, Danilo S Sanches2

  • 1Department of Computer Science, Bioinformatics Graduate Program, Federal University of Technology - Paraná, Cornélio Procópio, PR, Brazil. jadermcg@hotmail.com.

BMC Bioinformatics
|January 5, 2018
PubMed
Summary
This summary is machine-generated.

A new algorithm, Memetic Framework for Motif Discovery (MFMD), accurately predicts Transcription Factor Binding Sites (TFBS) in DNA sequences. This computational tool aids in understanding gene regulation and drug development by identifying overrepresented patterns.

Keywords:
Evolutionary algorithmsHeuristicsMemetic algorithmsMotifTranscription factor binding sites

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • De novo prediction of Transcription Factor Binding Sites (TFBS) is crucial for understanding gene regulation.
  • Accurate TFBS identification aids in drug development and biological mechanism elucidation.

Purpose of the Study:

  • To present the Memetic Framework for Motif Discovery (MFMD) algorithm for de novo motif discovery in biopolymer sequences.
  • To evaluate MFMD's performance against existing methods for TFBS prediction.

Main Methods:

  • MFMD employs semi-greedy constructive heuristics as a local optimizer.
  • A hybridized genetic algorithm serves as a global optimizer to refine solutions.
  • The algorithm identifies and classifies overrepresented patterns in DNA sequences.

Main Results:

  • MFMD successfully finds and classifies DNA sequence patterns and predicts their positions.
  • Performance was validated using ChIP-seq, promoter, and synthetic datasets.
  • MFMD outperformed MEME and Gibbs Motif Sampler, achieving higher f-scores on most datasets.

Conclusions:

  • MFMD is a novel approach for detecting motifs in biopolymer sequences.
  • The freely available, open-source software offers a promising alternative for de novo motif discovery.
  • MFMD can be downloaded from GitHub for broader research application.