AOPxGeneNet: Cross-Linking Gene Co-expression Networks with Adverse Outcome Pathways (AOPs) for mechanistic exploration of transcriptomic data
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Recurso digital |
| Publié: |
Zenodo
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866902214002343936 |
|---|---|
| author | Ali, Asmaa Yordanov, Yordan Mohr, Thomas Hardy, Barry |
| author_facet | Ali, Asmaa Yordanov, Yordan Mohr, Thomas Hardy, Barry |
| contents | <p><a title="AOPxGeneNet" href="https://aopxlink.edelweissconnect.com/" target="_blank" rel="noopener"><strong>AOPxGeneNet</strong></a> is an interactive, reproducible workflow and Streamlit application that connects <strong>gene co-expression networks</strong> to the <strong>Adverse Outcome Pathway (AOP) framework</strong> to help researchers move from “lists of differentially expressed genes” to <strong>mechanistically grounded hypotheses</strong> (Key Events → Adverse Outcomes).</p> <h3>What it does</h3> <p>AOPxGeneNet operationalizes a 3-stage pipeline (exposed as a simple UI):</p> <ol> <li> <p><strong>Build a gene co-expression network (GraphML) from expression data</strong></p> <ul> <li> <p>Runs a WGCNA-style workflow and exports a <strong>gene co-expression network in GraphML</strong>, plus an edge list and module assignments. </p> </li> <li> <p>Optionally exports <strong>one GraphML per module</strong>, so users can inspect modules independently (useful for Cytoscape/Gephi workflows and targeted downstream analyses). </p> </li> </ul> </li> <li> <p><strong>Map AOP knowledge to genes and compute a ranked gene–KE–AO overlap</strong><br>The AOPxGeneNet mapping layer links the WGCNA output to AOP-Wiki resources in a cached, reproducible way:</p> <ul> <li> <p>Downloads and <strong>quarterly-caches</strong> AOP-Wiki tables for KE components and KE relationships. </p> </li> <li> <p>Computes a <strong>topological distance from each Key Event (KE) to the nearest Adverse Outcome (AO)</strong> within the same AOP using KE–KE relationships (graph-based propagation). </p> </li> <li> <p>Joins AOP-mapped genes to genes present in the co-expression network and produces a <strong>ranked overlap table</strong> that combines (i) proximity to AO, (ii) gene-level DE signals (e.g., log2FC/padj when available), and (iii) network connectivity metrics (degree/strength/mean edge weight). </p> </li> </ul> </li> <li> <p><strong>Integrate and visualize the merged AOP–gene network</strong></p> <ul> <li> <p>Merges an <strong>AOP network (JSON)</strong> with a <strong>gene co-expression network (GraphML)</strong> and renders an interactive HTML network view. </p> </li> <li> <p>Supports a scalable “focus mode” for large graphs by letting users select Key Events and restrict neighbor expansion by edge-weight threshold and max-neighbors. </p> </li> <li> <p>The AOP network JSON is designed to be obtained directly from <strong>AOPGraphExplorer v2</strong> exports (node-link JSON)</p> </li> </ul> </li> </ol> <h3>Why this is strong (core value)</h3> <ul> <li> <p><strong>Mechanistic anchoring:</strong> AOPxGeneNet ties co-expression structure to curated mechanistic knowledge (MIE/KE/AO), enabling interpretable hypotheses rather than purely statistical associations. </p> </li> <li> <p><strong>Prioritization that blends multiple evidence types:</strong> it ranks candidate genes not just by DE or connectivity alone, but by integrating AOP topology (KE→AO distance), expression perturbation, and network centrality in one evidence-aware table. </p> </li> <li> <p><strong>Interoperable outputs:</strong> the pipeline produces widely-used exchange formats (<strong>GraphML</strong>, CSV tables, and interactive HTML) so results can move easily into downstream tools (e.g., Cytoscape/Gephi/NetworkX workflows). </p> </li> <li> <p><strong>Reproducible by design:</strong> AOP-Wiki downloads are cached (quarterly) and key inputs are hashed, reducing “silent drift” and making results easier to reproduce across runs. </p> </li> </ul> <h3>Who can use it (examples)</h3> <ul> <li> <p><strong>Academic toxicology & systems biology:</strong> mechanistic interpretation of transcriptomics, module hubs, and KE/AO consistency checks for a phenotype of interest.</p> </li> <li> <p><strong>Pharma & biotech:</strong> hypothesis generation for target/pathway prioritization and biomarker discovery framed in mechanistic adverse outcome logic.</p> </li> <li> <p><strong>Chemical safety & regulatory science:</strong> support weight-of-evidence exploration by linking observed molecular changes to structured AOP knowledge.</p> </li> <li> <p><strong>Other sectors working with stress biology/omics:</strong> cosmetics, environmental monitoring, materials safety, occupational health—any domain where mechanistic anchoring of omics signals is valuable.</p> </li> </ul> <h3>Current status and roadmap</h3> <p>This Zenodo release provides the working <strong>AOP×GeneNet integration and ranking workflow</strong> plus interactive visualization. Upcoming planned improvements include:</p> <ul> <li> <p><strong>SEM/causal modeling layer</strong> to move beyond “gene flowers around KEs” into testable structural models over AOP-informed graph structures.</p> </li> <li> <p>Improved network reduction and mechanistic summarization for very large graphs (scalable subgraph extraction and reporting).</p> </li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18793491 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AOPxGeneNet: Cross-Linking Gene Co-expression Networks with Adverse Outcome Pathways (AOPs) for mechanistic exploration of transcriptomic data Ali, Asmaa Yordanov, Yordan Mohr, Thomas Hardy, Barry AOP Adverse Outcome Pathways AOP-Wiki gene co-expression WGCNA mechanistic toxicology systems toxicology network biology knowledge integration biomarker discovery prioritization <p><a title="AOPxGeneNet" href="https://aopxlink.edelweissconnect.com/" target="_blank" rel="noopener"><strong>AOPxGeneNet</strong></a> is an interactive, reproducible workflow and Streamlit application that connects <strong>gene co-expression networks</strong> to the <strong>Adverse Outcome Pathway (AOP) framework</strong> to help researchers move from “lists of differentially expressed genes” to <strong>mechanistically grounded hypotheses</strong> (Key Events → Adverse Outcomes).</p> <h3>What it does</h3> <p>AOPxGeneNet operationalizes a 3-stage pipeline (exposed as a simple UI):</p> <ol> <li> <p><strong>Build a gene co-expression network (GraphML) from expression data</strong></p> <ul> <li> <p>Runs a WGCNA-style workflow and exports a <strong>gene co-expression network in GraphML</strong>, plus an edge list and module assignments. </p> </li> <li> <p>Optionally exports <strong>one GraphML per module</strong>, so users can inspect modules independently (useful for Cytoscape/Gephi workflows and targeted downstream analyses). </p> </li> </ul> </li> <li> <p><strong>Map AOP knowledge to genes and compute a ranked gene–KE–AO overlap</strong><br>The AOPxGeneNet mapping layer links the WGCNA output to AOP-Wiki resources in a cached, reproducible way:</p> <ul> <li> <p>Downloads and <strong>quarterly-caches</strong> AOP-Wiki tables for KE components and KE relationships. </p> </li> <li> <p>Computes a <strong>topological distance from each Key Event (KE) to the nearest Adverse Outcome (AO)</strong> within the same AOP using KE–KE relationships (graph-based propagation). </p> </li> <li> <p>Joins AOP-mapped genes to genes present in the co-expression network and produces a <strong>ranked overlap table</strong> that combines (i) proximity to AO, (ii) gene-level DE signals (e.g., log2FC/padj when available), and (iii) network connectivity metrics (degree/strength/mean edge weight). </p> </li> </ul> </li> <li> <p><strong>Integrate and visualize the merged AOP–gene network</strong></p> <ul> <li> <p>Merges an <strong>AOP network (JSON)</strong> with a <strong>gene co-expression network (GraphML)</strong> and renders an interactive HTML network view. </p> </li> <li> <p>Supports a scalable “focus mode” for large graphs by letting users select Key Events and restrict neighbor expansion by edge-weight threshold and max-neighbors. </p> </li> <li> <p>The AOP network JSON is designed to be obtained directly from <strong>AOPGraphExplorer v2</strong> exports (node-link JSON)</p> </li> </ul> </li> </ol> <h3>Why this is strong (core value)</h3> <ul> <li> <p><strong>Mechanistic anchoring:</strong> AOPxGeneNet ties co-expression structure to curated mechanistic knowledge (MIE/KE/AO), enabling interpretable hypotheses rather than purely statistical associations. </p> </li> <li> <p><strong>Prioritization that blends multiple evidence types:</strong> it ranks candidate genes not just by DE or connectivity alone, but by integrating AOP topology (KE→AO distance), expression perturbation, and network centrality in one evidence-aware table. </p> </li> <li> <p><strong>Interoperable outputs:</strong> the pipeline produces widely-used exchange formats (<strong>GraphML</strong>, CSV tables, and interactive HTML) so results can move easily into downstream tools (e.g., Cytoscape/Gephi/NetworkX workflows). </p> </li> <li> <p><strong>Reproducible by design:</strong> AOP-Wiki downloads are cached (quarterly) and key inputs are hashed, reducing “silent drift” and making results easier to reproduce across runs. </p> </li> </ul> <h3>Who can use it (examples)</h3> <ul> <li> <p><strong>Academic toxicology & systems biology:</strong> mechanistic interpretation of transcriptomics, module hubs, and KE/AO consistency checks for a phenotype of interest.</p> </li> <li> <p><strong>Pharma & biotech:</strong> hypothesis generation for target/pathway prioritization and biomarker discovery framed in mechanistic adverse outcome logic.</p> </li> <li> <p><strong>Chemical safety & regulatory science:</strong> support weight-of-evidence exploration by linking observed molecular changes to structured AOP knowledge.</p> </li> <li> <p><strong>Other sectors working with stress biology/omics:</strong> cosmetics, environmental monitoring, materials safety, occupational health—any domain where mechanistic anchoring of omics signals is valuable.</p> </li> </ul> <h3>Current status and roadmap</h3> <p>This Zenodo release provides the working <strong>AOP×GeneNet integration and ranking workflow</strong> plus interactive visualization. Upcoming planned improvements include:</p> <ul> <li> <p><strong>SEM/causal modeling layer</strong> to move beyond “gene flowers around KEs” into testable structural models over AOP-informed graph structures.</p> </li> <li> <p>Improved network reduction and mechanistic summarization for very large graphs (scalable subgraph extraction and reporting).</p> </li> </ul> |
| title | AOPxGeneNet: Cross-Linking Gene Co-expression Networks with Adverse Outcome Pathways (AOPs) for mechanistic exploration of transcriptomic data |
| topic | AOP Adverse Outcome Pathways AOP-Wiki gene co-expression WGCNA mechanistic toxicology systems toxicology network biology knowledge integration biomarker discovery prioritization |
| url | https://doi.org/10.5281/zenodo.18793491 |