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Updated: May 8, 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

PMS6: A Fast Algorithm for Motif Discovery.

Shibdas Bandyopadhyay¹, Sartaj Sahni, Sanguthevar Rajasekaran

¹Department of CISE, University of Florida, Gainesville, FL 32611.

IEEE ... International Conference on Computational Advances in Bio and Medical Sciences : [Proceedings]. IEEE International Conference on Computational Advances in Bio and Medical Sciences

|August 21, 2013

Summary

This summary is machine-generated.

A new algorithm, PMS6, significantly speeds up (l, d)-motif discovery, finding strings in datasets with few mismatches. PMS6 offers substantial runtime and preprocessing improvements over previous methods like PMS5.

Keywords:

Planted motif search string algorithms

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Peptide-based Identification of Functional Motifs and their Binding Partners

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Published on: June 30, 2013

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Last Updated: May 8, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Published on: May 31, 2011

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

Bioinformatics
Computational Biology
Algorithm Development

Background:

Motif discovery is crucial for understanding biological sequences.
Existing algorithms face challenges with large datasets and permissible mismatches.
The (l, d)-motif discovery problem requires finding short sequences (motifs) present in a dataset with a specified number of allowed differences.

Purpose of the Study:

To introduce PMS6, a novel and efficient algorithm for the (l, d)-motif discovery problem.
To evaluate the performance of PMS6 against the fastest existing algorithm, PMS5.
To demonstrate the computational advantages of PMS6 in terms of runtime and preprocessing time.

Main Methods:

Development of the PMS6 algorithm for (l, d)-motif discovery.
Comparative analysis of PMS6 and PMS5 on various challenge instances (e.g., (21,8), (17,6), (23,9), (13,4)).
Measurement of runtime and preprocessing time for both algorithms.

Main Results:

PMS6 demonstrates a runtime advantage over PMS5, with ratios ranging from 1.69 to 2.20.
PMS6 significantly reduces preprocessing time, being up to 34 times faster than PMS5 for (23,9) instances.
When factoring in preprocessing, the overall runtime ratio of PMS5/PMS6 ranges from 1.95 to 2.75.

Conclusions:

PMS6 represents a significant advancement in motif discovery algorithms.
The new algorithm offers substantial speedups, making it more suitable for large-scale biological data analysis.
PMS6 provides a more efficient solution for identifying (l, d)-motifs compared to prior methods.