BiG-SCAPE

BiG-SCAPE constructs sequence similarity networks of biosynthetic gene clusters (BGCs) to cluster them into gene cluster families (GCFs) and quantify biosynthetic diversity across large genomic datasets.

Key Features:

• Sequence similarity networks: Constructs sequence similarity networks of BGCs and groups BGCs into gene cluster families (GCFs).
• Distance matrix computation: Rapidly computes a distance matrix between gene clusters by comparing protein domain content, domain order, domain copy number, and protein sequence identity.
• Composite distance metric: Implements a composite distance metric integrating the Jaccard Index for domain types, Domain Sequence Similarity, and Adjacency.
• Protein-domain comparisons: Performs detailed comparisons of protein domain content, order, copy number, and sequence identity among clusters.
• Integration with CORASON: Can be used in conjunction with CORASON (Core Analysis of Syntenic Orthologues to Prioritize Natural Product Gene Clusters) to relate BGC networks to enzyme phylogenies.
• Metabolomic validation: Validated by correlation with metabolomic data from 363 actinobacterial strains.
• Natural product discovery: Enabled mapping of detoxin/rimosamide-related GCFs and the characterization of seven novel detoxin analogues.
• Implementation: Implemented in Python.

Scientific Applications:

• Genome-scale BGC mining: Mining biosynthetic gene clusters across large numbers of genomes to assess natural product diversity.
• GCF-based diversity analysis: Grouping BGCs into GCFs to study the distribution and diversity of biosynthetic pathways.
• Genomics–metabolomics integration: Correlating BGC sequence similarity networks with metabolomic data (e.g., 363 actinobacterial strains) to associate gene clusters with metabolites.
• Phylogenetic prioritization: Prioritizing natural product gene clusters and exploring enzyme phylogenies when combined with CORASON.
• Natural product discovery: Facilitating discovery and characterization of novel natural products such as seven detoxin analogues from detoxin/rimosamide-related GCFs.

Methodology:

Calculates a distance matrix by comparing protein domain content, domain order, domain copy number, and protein sequence identity; computes a composite distance metric integrating the Jaccard Index for domain types, Domain Sequence Similarity, and Adjacency; constructs sequence similarity networks and clusters BGCs into GCFs; implemented in Python; can be used with CORASON to analyze syntenic orthologues and enzyme phylogenies.