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Updated: Apr 4, 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

CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements.

Eman Badr¹, Lenwood S Heath²

¹Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA.

BMC Bioinformatics

|September 5, 2015

Summary

This summary is machine-generated.

A new algorithm, Combinatorial SRE Miner (CoSREM), identifies sets of splicing regulatory elements (SREs) that control gene expression. This method advances understanding of alternative splicing in diseases and tissue specificity.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Alternative splicing (AS) regulates gene expression post-transcriptionally.
Splicing factor binding to splicing regulatory elements (SREs) dictates splicing decisions.
Current methods often limit SRE discovery to pairs and fixed lengths.

Purpose of the Study:

To develop a novel graph mining algorithm, CoSREM, for discovering combinatorial SREs in human exons.
To identify sets of SREs without predefined length constraints.
To incorporate experimental evidence for increased accuracy in SRE identification.

Main Methods:

Developed CoSREM, a graph mining algorithm for combinatorial SRE discovery.
Applied CoSREM to human exons, considering variable SRE lengths.
Integrated experimental evidence to enhance SRE identification accuracy.

Main Results:

Identified 37 SRE sets comprising both enhancer and silencer elements.
Validated findings against previous experimental results.
Discovered specific SRE sets (GGGAGG, GAGGAC) potentially involved in tumor-specific exon skipping.
Applied CoSREM to RNA-Seq data across multiple tissues to identify tissue-specific combinatorial SREs.

Conclusions:

CoSREM effectively identifies diverse combinations of splicing enhancers and silencers.
The algorithm is not limited by SRE size or the number of SREs in a set.
This approach offers new avenues for studying SREs and the role of AS in disease and tissue specificity.