gatom

gatom implements a metabolic network analysis pipeline to identify an active metabolic module based on high-throughput transcriptional and/or metabolomic data between two conditions of interest.

Key Features:

• Atom transition network topology: Employs a metabolic network topology based on atom transition to improve interpretability of analysis results.
• Maximum weight connected subgraph solver: Introduces a new variant of the maximum weight connected subgraph problem with a corresponding exact solver implemented in the companion R package mwcsr.
• Network construction from KEGG: Upgrades the KEGG-based network construction pipeline for generating metabolic networks.
• Network construction from Rhea: Develops a network construction pipeline based on the Rhea database to enable analysis of lipidomics data.
• Module post-processing: Provides functions for post-processing of identified metabolic modules.
• Module annotation: Provides functions for annotation of identified metabolic modules.
• Module visualization: Provides functions for visualization of identified metabolic modules.

Scientific Applications:

• Integrative metabolic network analysis: Integrates transcriptional and/or metabolomic data to find the metabolic subnetwork most regulated between two conditions.
• Lipidomics data analysis: Enables analysis of lipidomics data through Rhea-based network construction.

Methodology:

The pipeline takes transcriptional and/or metabolomic data as input, constructs atom transition networks from KEGG or Rhea databases, and solves a new variant of the maximum weight connected subgraph problem to find the most regulated metabolic module between two conditions.