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Related Experiment Video

Updated: Jun 16, 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

Evidence-ranked motif identification.

Stoyan Georgiev¹, Alan P Boyle, Karthik Jayasurya

¹Program for Computational Biology and Bioinformatics, Duke University, 102 North Building, Durham, NC 27708, USA.

|February 17, 2010

Summary

This summary is machine-generated.

cERMIT is a novel motif discovery tool that analyzes genome-wide regulatory evidence for efficient and accurate results. It outperforms existing methods on ChIP-chip and scales to large ChIP-seq datasets.

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Last Updated: Jun 16, 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

Peptide-based Identification of Functional Motifs and their Binding Partners

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Published on: June 30, 2013

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

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

Published on: July 14, 2015

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Motif discovery is crucial for understanding gene regulation.
Existing methods often rely on pre-selected sequences, limiting their scope.
Scalability to large datasets like ChIP-seq is a challenge.

Purpose of the Study:

To introduce cERMIT, a computationally efficient motif discovery tool.
To leverage genome-wide quantitative regulatory evidence for motif identification.
To demonstrate cERMIT's superior performance compared to state-of-the-art approaches.

Main Methods:

Developed cERMIT, a motif discovery algorithm.
Utilized genome-wide quantitative regulatory evidence.
Applied cERMIT to direct binding and overexpression datasets, including ChIP-chip and ChIP-seq data.

Main Results:

cERMIT significantly outperforms existing state-of-the-art methods on curated ChIP-chip datasets.
The tool demonstrates computational efficiency and scalability for large-scale mammalian ChIP-seq experiments.
It effectively analyzes thousands of non-coding regions.

Conclusions:

cERMIT offers a powerful and efficient approach to motif discovery.
Its ability to utilize comprehensive regulatory evidence enhances accuracy.
The tool is well-suited for analyzing modern, large-scale genomic datasets.