Multi-Omics Disease Characterization Pipeline

Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.

KEY PRINCIPLES

:

Report-first approach

- Create report file FIRST, then populate progressively

Disease disambiguation FIRST

- Resolve all identifiers before omics analysis

Layer-by-layer analysis

- Systematically cover all omics layers

Cross-layer integration

- Identify genes/targets appearing in multiple layers

Evidence grading

- Grade all evidence as T1 (human/clinical) to T4 (computational)

Tissue context

- Emphasize disease-relevant tissues/organs

Quantitative scoring

- Multi-Omics Confidence Score (0-100)

Druggable focus

- Prioritize targets with therapeutic potential

Biomarker identification

- Highlight diagnostic/prognostic markers

Mechanistic synthesis

- Generate testable hypotheses

Source references

- Every statement must cite tool/database

Completeness checklist

- Mandatory section showing analysis coverage

English-first queries

- Always use English terms in tool calls. Respond in user's language

When to Use This Skill

Apply when users:

Ask about disease mechanisms across omics layers

Need multi-omics characterization of a disease

Want to understand disease at the systems biology level

Ask "What pathways/genes/proteins are involved in [disease]?"

Need biomarker discovery for a disease

Want to identify druggable targets from disease profiling

Ask for integrated genomics + transcriptomics + proteomics analysis

Need cross-layer concordance analysis

Ask about disease network biology / hub genes

NOT for

(use other skills instead):

Single gene/target validation -> Use

tooluniverse-drug-target-validation

Drug safety profiling -> Use

tooluniverse-adverse-event-detection

General disease overview -> Use

tooluniverse-disease-research

Variant interpretation -> Use

tooluniverse-variant-interpretation

GWAS-specific analysis -> Use

tooluniverse-gwas-*

skills

Pathway-only analysis -> Use

tooluniverse-systems-biology

Input Parameters

Parameter

Required

Description

Example

disease

Yes

Disease name, OMIM ID, EFO ID, or MONDO ID

Alzheimer disease

,

MONDO_0004975

tissue

No

Tissue/organ of interest

brain

,

liver

,

blood

focus_layers

No

Specific omics layers to emphasize

genomics

,

transcriptomics

,

pathways

Pipeline Overview

The pipeline runs 9 phases sequentially. Each phase uses specific tools documented in detail in

tool-reference.md

.

Phase 0: Disease Disambiguation (ALWAYS FIRST)

Resolve disease to standard identifiers (MONDO/EFO) for all downstream queries.

Primary tool:

OpenTargets_get_disease_id_description_by_name

Get description, synonyms, therapeutic areas, disease hierarchy, cross-references

CRITICAL

Disease IDs use underscore format (e.g.,

MONDO_0004975

), NOT colon

If ambiguous, present top 3-5 options and ask user to select

Phase 1: Genomics Layer

Identify genetic variants, GWAS associations, and genetically implicated genes.

Tools: OpenTargets associated targets, evidence by datasource, GWAS Catalog, ClinVar

Get top 10-15 genes with genetic evidence scores

Track genes with Ensembl IDs for downstream phases

Phase 2: Transcriptomics Layer

Identify differentially expressed genes, tissue-specific expression, and expression-based biomarkers.

Tools: Expression Atlas (differential + experiments), HPA (tissue expression), EuropePMC scores

Check expression in disease-relevant tissues for top genes from Phase 1

For cancer: check prognostic biomarkers via HPA

Phase 3: Proteomics & Interaction Layer

Map protein-protein interactions, identify hub genes, and characterize interaction networks.

Tools: STRING (partners, network, enrichment), IntAct, HumanBase (tissue-specific PPI)

Build PPI network from top 15-20 genes across previous layers

Identify hub genes by degree centrality (degree > mean + 1 SD)

Phase 4: Pathway & Network Layer

Identify enriched biological pathways and cross-pathway connections.

Tools: Enrichr (KEGG, Reactome, WikiPathways), ReactomeAnalysis, KEGG search

Run enrichment on combined gene list (top 20-30 genes)

NOTE

Enrichr

data

field is a JSON string that needs parsing

Phase 5: Gene Ontology & Functional Annotation

Characterize biological processes, molecular functions, and cellular components.

Tools: Enrichr (GO libraries), QuickGO, GO annotations, OpenTargets GO

Run GO enrichment for all 3 aspects (BP, MF, CC)

Phase 6: Therapeutic Landscape

Map approved drugs, druggable targets, repurposing opportunities, and clinical trials.

Tools: OpenTargets drugs/tractability, clinical trials search, drug mechanisms

size

parameter is REQUIRED for OpenTargets drug queries (use 100)

query_term

is REQUIRED for clinical trials search

Phase 7: Multi-Omics Integration

Integrate findings across all layers. See

integration-scoring.md

for full details.

Cross-layer gene concordance: count layers per gene, score multi-layer hub genes

Direction concordance: genetics + expression agreement

Biomarker identification: diagnostic, prognostic, predictive

Mechanistic hypothesis generation

Phase 8: Report Finalization

Write executive summary, calculate confidence score, verify completeness.

See

integration-scoring.md

for quality checklist and scoring formula

Key Tool Parameter Notes

These are the most common parameter pitfalls:

OpenTargets

disease IDs: underscore format (

MONDO_0004975

), NOT colon

STRING

protein_ids

must be

array

(

['APOE']

), not string

enrichr

libs

must be

array

(

['KEGG_2021_Human']

)

HPA_get_rna_expression_by_source

ALL 3 params required (

gene_name

,

source_type

,

source_name

)

humanbase_ppi_analysis

ALL params required ( gene_list , tissue , max_node , interaction , string_mode ) expression_atlas_disease_target_score : pageSize is REQUIRED search_clinical_trials : query_term is REQUIRED even if condition is provided For full tool parameters and per-phase workflows, see tool-reference.md . Reference Files All detailed content is in reference files in this directory: File Contents tool-reference.md Full tool parameters, inputs/outputs, per-phase workflows, quick reference table report-template.md Complete report markdown template with all sections and checklists integration-scoring.md Confidence score formula (0-100), evidence grading (T1-T4), integration procedures, quality checklist response-formats.md Verified JSON response structures for key tools use-patterns.md Common use patterns, edge case handling, fallback strategies