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Protein-protein Interfaces

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Updated: Jan 2, 2026

DNA-affinity-purified Chip DAP-chip Method to Determine Gene Targets for Bacterial Two component Regulatory Systems

DNA-affinity-purified Chip DAP-chip Method to Determine Gene Targets for Bacterial Two component Regulatory Systems

Published on: July 21, 2014

A particle swarm optimization-based algorithm for finding gapped motifs.

Chengwei Lei¹, Jianhua Ruan

¹Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA. clei@cs.utsa.edu.

|December 15, 2010

Summary

This summary is machine-generated.

This study introduces PSO+, a novel motif-finding algorithm for DNA sequences. It accurately identifies gapped motifs, improving gene regulatory network analysis.

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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 repeated patterns (motifs) in DNA sequences is crucial for understanding gene regulation and function.
Gene regulatory networks are complex, and motif discovery aids in their deciphering.

Purpose of the Study:

To develop a novel motif-finding algorithm for DNA sequences.
To improve the accuracy and efficiency of motif discovery, particularly for motifs with gaps.

Main Methods:

Developed a novel motif-finding algorithm, PSO+, based on Particle Swarm Optimization (PSO).
Modified the standard PSO algorithm to handle discrete DNA sequence data.
Incorporated both consensus and position-specific weight matrix representations.
Explicitly modeled gaps within motifs, addressing limitations of existing methods.

Main Results:

The PSO+ algorithm effectively handles discrete values in DNA sequences.
The method integrates consensus and position-specific weight matrices for enhanced performance.
PSO+ successfully identifies gapped motifs without requiring prior knowledge of gap locations or lengths.
The algorithm accommodates input sequences with zero or multiple binding sites.

Conclusions:

PSO+ demonstrates superior efficiency and accuracy compared to existing algorithms.
The method shows particular strength in identifying motifs containing gaps.
Experimental results on synthetic and real biological data validate the algorithm's effectiveness.