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Related Experiment Video

Updated: Jun 11, 2026

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

A parallel combinatorial algorithm for subtle motifs.

Michael Arock¹, Srinivasulu Reddy, A V Reddy

¹Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620 015, Tamil Nadu, India. michael@nitt.edu

International Journal of Bioinformatics Research and Applications

|July 10, 2010

Summary

This summary is machine-generated.

This study introduces a parallel combinatorial algorithm to find subtle motifs in biological sequences, addressing challenges posed by mutations and NP-complete complexity for improved bioinformatics analysis.

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

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Area of Science:

Bioinformatics
Computational Biology
Algorithm Design

Background:

Motif finding is crucial in bioinformatics for identifying recurring patterns in nucleotide or amino acid sequences.
Biological mutations introduce variability, making exact pattern matching difficult and complicating motif discovery.
Existing algorithms often fail to address the subtleties of biological sequence variations.

Purpose of the Study:

To propose a novel parallel combinatorial algorithm for subtle motif finding.
To address the NP-complete nature of motif finding in bioinformatics.
To provide an implementation strategy for the proposed algorithm on a Shared Memory Multiprocessor model.

Main Methods:

Development of a parallel combinatorial algorithm tailored for subtle motif detection.
Algorithm designed for execution on a Shared Memory Multiprocessor architecture.
Focus on addressing biological sequence variations beyond exact matches.

Main Results:

The proposed algorithm offers a new approach to tackle the complexities of subtle motif finding.
Demonstrates the feasibility of parallel processing for this NP-complete problem.
Provides a framework for implementing advanced motif discovery techniques.

Conclusions:

The parallel combinatorial algorithm presents a significant advancement in bioinformatics for motif discovery.
The approach effectively handles biological sequence variations, offering more robust pattern identification.
Implementation on shared memory systems enables efficient computation for complex biological data analysis.