mBONITA

mBONITA integrates multiomics datasets using Boolean network and topology-based analyses to identify genes and signaling pathways consistently modulated across molecular layers.

Key Features:

• Integration of Multiomics Data: Handles datasets that quantify multiple molecular layers to enable integrated pathway analysis.
• Boolean Network-Based Methodology: Employs a Boolean network framework that leverages prior knowledge networks for topology-based pathway analysis.
• Identification of Consistently Modulated Genes: Combines observed fold-changes and variance with measures of node influence over signaling and evidence strength across datasets to identify genes with consistent modulation.
• Pathway Enrichment Analysis: Performs topology-based pathway enrichment to characterize signaling mechanisms modulated under specific conditions.
• Performance Comparison: Evaluated against six other pathway analysis methods and consistently identified pathways with modulation evidence across all omics layers.

Scientific Applications:

• RAMOS B cell cyclosporine A study: Integrated multiomics datasets from RAMOS B cells treated with cyclosporine A under varying oxygen tensions to identify pathways involved in hypoxia-mediated chemotaxis.
• Immunology and drug-response analyses: Applied to investigate immunological signaling and drug-response mechanisms across multiple molecular layers.

Methodology:

Integrates multiomics data via a Boolean network-based approach that incorporates prior knowledge networks for topology-based pathway analysis and assesses gene influence by evaluating fold-changes, variance, node influence, and evidence strength across datasets.