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Updated: Dec 23, 2025
07:55
Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Published on: May 31, 2011
10.7K
A novel pattern matching algorithm for genomic patterns related to protein motifs.
Mohammad-Hadi Foroughmand-Araabi1, Sama Goliaei2, Bahram Goliaei3
1Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran.
Journal of Bioinformatics and Computational Biology
|April 28, 2020
Summary
This study introduces the Consecutive Positions Scoring Matrix (CPSSM) for genomic pattern analysis, enabling direct pattern discovery from protein data. This method significantly improves true positive rates in gene finding, with no false negatives reported in human and yeast genomes.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Protein and genomic sequence patterns are extensively studied.
- Few studies link protein patterns to induced genomic coding region patterns.
Purpose of the Study:
- Introduce a novel genomic pattern structure, CPSSM, derived from protein patterns.
- Develop and validate a pattern matching algorithm for CPSSMs in genomes.
Main Methods:
- Defined the Consecutive Positions Scoring Matrix (CPSSM) as a genomic pattern representation.
- Developed a dynamic programming algorithm for CPSSM matching in genomic sequences.
- Modified the algorithm to handle intronic gaps and large genomes.
Main Results:
- CPSSMs effectively represent induced patterns from proteins in a genomic context.
- The algorithm demonstrated high accuracy on Saccharomyces cerevisiae and human genomes.
- Achieved 132% more true positives in yeast and 10x more true positives in humans, with no false negatives.
Conclusions:
- CPSSM and associated methods are valuable for open reading frame detection and gene finding.
- The developed tool offers improved performance over existing methods.
- Source code and application are publicly available for download and use.
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