MiCoNE

MiCoNE infers microbial co-occurrence networks from 16S ribosomal RNA (16S rRNA) amplicon sequencing data to elucidate interactions within microbial communities.

Key Features:

• Pipeline Analysis: Evaluates each step of converting 16S sequencing data into microbial association networks and quantifies how algorithmic choices and parameter settings affect resulting networks.
• Robust Network Inference: Analyzes mock and synthetic datasets to identify tools and parameter combinations that produce robust co-occurrence networks.
• Consensus Network Algorithms: Implements consensus network algorithms that leverage benchmarks from synthetic datasets to generate more stable and consistent networks.
• Integration and Comparative Analysis: Facilitates integration of multiple datasets to enable comparative analyses across various biomes.
• Guidance on Tool Selection: Provides systematic guidelines for selecting tools and parameter settings tailored to specific datasets to address robustness and uniqueness of inferred networks.

Scientific Applications:

• Microbiome structure and function: Enables exploration of microbiome community structure and function by inferring co-occurrence relationships from 16S rRNA data.
• Environmental biology: Supports investigation of microbial community assembly and interactions across environmental biomes.
• Human health: Supports studies of host-associated microbiomes by revealing microbial interaction patterns relevant to human health.
• Biotechnology: Informs biotechnology research by providing inferred interaction networks that can guide interpretation of microbial processes.

Methodology:

Computational methods explicitly include evaluation of pipeline steps converting 16S sequencing data into microbial association networks, systematic assessment of algorithmic choices and parameter settings, analysis of mock and synthetic datasets for benchmarking, implementation of consensus network algorithms using synthetic-data benchmarks, and comparative integration of multiple datasets.