CoNet app

CoNet app infers biological association networks from presence/absence and abundance matrices to detect co-occurrence (copresence) and mutual exclusion among taxa, genes, metabolites, or other repeatedly observed entities.

Key Features:

• Network Inference Approaches: Implements multiple association inference methods to detect significant associations within presence/absence and abundance matrices, identifying copresence and mutual exclusion.
• Generic Application: Applies to microbial community sequencing data such as 16S rDNA and to other datasets where species, genes, or metabolites are repeatedly observed across samples.
• Combination of Approaches: Supports combining different inference approaches to enhance robustness and comprehensiveness of inferred networks.

Scientific Applications:

• Microbial ecology: Analyzes 16S rDNA sequencing data from arctic soil samples to detect significant associations between microbial species and infer ecological interactions and community dynamics.
• Genomics: Infers co-occurrence and mutual exclusion patterns between genes across samples to explore gene–gene relationships.
• Metabolomics: Infers relationships between metabolites across samples to support network analyses in metabolomic studies.

Methodology:

Analyzes incidence (presence/absence) and abundance data to identify significant patterns of co-occurrence (copresence) and mutual exclusion.