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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

EXTREME: an online EM algorithm for motif discovery.

Daniel Quang¹, Xiaohui Xie¹

¹Department of Computer Science, University of California, Irvine, CA 92697, USA and Center for Complex Biological Systems, University of California, Irvine, CA 92697, USADepartment of Computer Science, University of California, Irvine, CA 92697, USA and Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA.

Bioinformatics (Oxford, England)

|February 18, 2014

Summary

This summary is machine-generated.

EXTREME is a new motif discovery algorithm for analyzing large ChIP-Seq and DNase-Seq datasets. It efficiently identifies DNA-binding motifs, including novel ones, without discarding data, unlike the traditional MEME algorithm.

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Last Updated: May 3, 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

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Identifying DNA-binding motifs is crucial for understanding gene transcription regulation.
Traditional algorithms like MEME struggle with large datasets from ChIP-Seq and DNase-Seq experiments.
Current methods often require discarding data, potentially losing important motif information.

Purpose of the Study:

To develop an efficient motif discovery algorithm for large-scale genomic data.
To address the limitations of existing algorithms in handling extensive ChIP-Seq and DNase-Seq datasets.
To enable the discovery of novel and infrequent transcription factor binding motifs.

Main Methods:

Introduced EXTREME, a novel motif discovery algorithm.
Employed the online expectation-maximization algorithm, differing from MEME's standard expectation-maximization.
Applied EXTREME to analyze ChIP-Seq and DNase-Seq data.

Main Results:

EXTREME efficiently discovers DNA-binding motifs in large datasets without data exclusion.
The algorithm identified numerous motifs, including novel and infrequent ones, from ChIP-Seq and DNase-Seq data.
Conservation analysis validated the evolutionary significance and potential functionality of a newly discovered motif.

Conclusions:

EXTREME provides a computationally efficient solution for motif discovery in large genomic datasets.
The algorithm facilitates the identification of previously undiscovered regulatory elements.
EXTREME enhances the analysis of transcription factor binding preferences from high-throughput sequencing data.