ClusterScan

ClusterScan identifies genomic clusters from feature coordinates and categorical annotations to analyze the spatial organization and functional grouping of genes, transcripts, regulatory regions, and other genome-mapped elements.

Key Features:

• User-Defined Cluster Identification: Groups genes, transcripts, regulatory regions, and other genome-mapped elements into clusters using genomic coordinates and user-defined categorical annotations.
• Annotation Integration: Accepts a BED-format annotation file and a two-column feature-to-category mapping file linking feature IDs to categorical information such as Gene Ontology classes, KEGG pathways, or Pfam accessions.
• Broad Application Scope: Supports analyses relevant to genome evolution, localization of metabolic pathways, and characterization of gene families across sequenced genomes.
• Implementation: Implemented in Python.

Scientific Applications:

• Evolutionary genomics: Detects clustered arrangements that inform studies of genome organization and evolutionary processes.
• Functional genomics: Identifies genomic regions enriched for functions or pathways using annotations from Gene Ontology, KEGG, and Pfam.
• Pathway and gene-family analysis: Pinpoints co-localized pathway components and gene-family members to support analyses of operon-like or pathway clustering.

Methodology:

Processes a BED annotation file and a two-column feature ID–to–category mapping to group features into clusters based on genomic coordinates and user-defined categorical annotations; implemented in Python.