CisMiner

CisMiner predicts cis-regulatory modules (CRMs) across whole genomes to identify significant motif combinations underlying cooperative transcription factor regulation.

Key Features:

• Genome-wide scope: Operates at a genome-wide scale, enabling CRM discovery beyond promoters or conserved regions.
• Blind search capability: Performs blind searches for CRMs without requiring prior information about target motifs, allowing discovery of novel regulatory combinations.
• Itemset representation: Represents motif combinations as itemsets to address the combinatorial complexity of CRM identification.
• Top-Down Fuzzy Frequent-Pattern Tree algorithm: Employs the Top-Down Fuzzy Frequent-Pattern Tree algorithm to detect significant itemsets of motifs.
• Fuzzy technology integration: Incorporates fuzzy technology to handle imprecision and noise in transcription factor binding site data.
• Application to model genomes: Has been applied to known binding sites in Saccharomyces cerevisiae and extended to predict significant motif combinations in Drosophila melanogaster.

Scientific Applications:

• CRM discovery in model organisms: Identification of cis-regulatory modules and significant motif combinations in Saccharomyces cerevisiae and Drosophila melanogaster.
• Transcription factor interaction analysis: Characterization of cooperative transcription factor binding and regulatory interaction networks.
• Biological research support: Facilitation of studies in developmental biology, disease mechanisms, and evolutionary analyses by revealing regulatory module organization.

Methodology:

Represents motif combinations as itemsets and applies a Top-Down Fuzzy Frequent-Pattern Tree algorithm with fuzzy technology to perform blind, genome-wide CRM searches.