Cliquely

Cliquely identifies protein-protein interaction networks by detecting proteins that are functionally linked through genome-scale co-occurrence analysis.

Key Features:

• Co-occurrence Pattern Exploration: Analyzes co-occurrence patterns across 4,742 fully sequenced genomes using a dataset of over 23 million proteins grouped into 404,947 orthologous clusters spanning Archaea, Bacteria, and Eukarya.
• Graph-Based Analysis: Constructs a co-occurrence graph whose edge weights represent probabilities of protein co-occurrence.
• Bron-Kerbosch Algorithm: Applies the Bron–Kerbosch algorithm to detect maximal cliques in the co-occurrence graph representing candidate functional modules.
• Functional Network Identification: Recovers known networks such as nitrogen fixation, glycolysis, methanogenesis, mevalonate synthesis, and ribosome assembly and has identified 13 novel proteins associated with the type III secretion system (T3SS).
• Customizable Exploration: Supports analyses with adjustable stringency and domain-specific (Archaea, Bacteria, Eukarya) or whole-dataset focus.

Scientific Applications:

• Functional genomics discovery: Enables discovery of novel protein interactions and candidate components of biological pathways.
• Protein function annotation: Facilitates generation of hypotheses for uncharacterized proteins based on co-occurrence-derived functional modules.
• Comparative and systems biology: Supports comparative analyses across Archaea, Bacteria, and Eukarya to study conserved and lineage-specific networks.
• Virulence and pathway component identification: Identifies components of virulence systems such as the type III secretion system (T3SS) and pathway-specific networks like nitrogen fixation and glycolysis.

Methodology:

Analyzes protein co-occurrence across 4,742 genomes using a dataset of >23 million proteins in 404,947 orthologous clusters, builds a probabilistic co-occurrence graph with edge weights as co-occurrence probabilities, and detects maximal cliques using the Bron–Kerbosch algorithm.