skills.rest
tony-zhelonkin
tony-zhelonkinCOMMUNITY
Explore all skills by this author →
𝕏@TonyZhelonkinGitHub

gatom-metabolomic-predictions

Identify maximally-regulated metabolic subnetworks from differential expression data.

1|Updated Nov 20, 2025
Search Tags:#metabolomics#pathway-analysis#kegg#metabolic-networks#rhea#sgmwcs#active-modules
One-click install
npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill gatom-metabolomic-predictions
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gatom-metabolomic-predictions
Source: https://github.com/tony-zhelonkin/SciAgent-toolkit/tree/main/skills/gatom-metabolomic-predictions
Command: npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill gatom-metabolomic-predictions

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Finds maximally-regulated metabolic subnetworks from differential expression data using atom-transition networks and BUM scoring, enabling interpretation of transcriptomic or metabolomic changes within metabolic pathways.

Core Features & Use Cases

  • Atom-transition network approach to map metabolites and reactions with gene associations.
  • BUM-scored SGMWCS optimization to identify active modules.
  • Handles input DE data with raw p-values and linear baseMean; supports KEGG, combined, and Rhea network topologies.
  • Output includes a ranked list of module components (genes/metabolites) with scores for interpretation.

Quick Start

Provide a differential expression table with raw p-values and baseMean to initialize the GATOM workflow and discover active metabolic modules.

Frequently Asked Questions about gatom-metabolomic-predictions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify active metabolic modules from differential expression data?▼

To identify active metabolic modules from differential expression data, apply atom-transition network mapping and BUM-scored SGMWCS optimization to infer maximally-regulated subnetworks within KEGG and Rhea pathways.

What input format is required for metabolic pathway analysis using atom-transition networks?▼

Metabolic pathway analysis using atom-transition networks requires a differential expression table containing raw p-values and a linear baseMean column to initialize the GATOM workflow and discover active modules.

Can I use adjusted p-values instead of raw p-values for identifying active metabolic subnetworks?▼

No, identifying active metabolic subnetworks with this approach specifically requires raw p-values rather than adjusted p-values, along with a linear baseMean measure, to correctly compute BUM scores.

Does the active module identification approach support both KEGG and Rhea network topologies?▼

Yes, active module identification supports KEGG, combined, and Rhea network topologies, mapping metabolites and reactions with gene associations to output a ranked list of module components with scores.

What is the best way to interpret transcriptomic changes within metabolic pathways?▼

The best way to interpret transcriptomic changes within metabolic pathways is to extract a ranked list of module components, including genes and metabolites, outputted with scores from the SGMWCS optimization.

Related Skills

Explore More Data & Analytics Skills→
tony-zhelonkintony-zhelonkin
1

decoupler-tf-activity

Infer transcription factor activities from differential expression t-statistics using ULM with CollecTRI priors.

Community
Advanced
StarlitnightlyStarlitnightly
1.2k

Bulk RNA-seq differential expression with omicverse

Perform bulk RNA-seq differential expression analysis with omicverse.

Community
Intermediate
openscientist-ioopenscientist-io
44

genomics

Identify differential gene expression patterns from RNA-seq and transcriptomics data.

Official
Advanced
jaechang-hitsjaechang-hits
298

reactome-database

Query Reactome pathway data and perform gene enrichment analysis via REST API.

Community
Intermediate