# Formulating Biological Findings

> Drawing findings from proteomics analysis. Use when computational data analysis is complete. Use for investigating biological or clinical impact.

- Skill: `mannlabs/formulating-biological-findings` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mannlabs/formulating-biological-findings`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mannlabs/formulating-biological-findings/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: MannLabs (https://skillmd.com/u/mannlabs)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/mannlabs/formulating-biological-findings

---


# Formulating Biological Findings from Proteomics Data

This skill guides the transition from the analysis of differentially expressed proteins (upregulated and downregulated) to the synthesis of findings that highlight protein interactions, pathway cross-talk, or potential roles in disease.

## Use When

Use when all preprocessing steps (including data reading, QC, preprocessing, statistical testing) are complete, and only the biological/clinical interpretation remains.

# Workflow

## Checklist

Copy this checklist to track your progress:

```
finding Generation Progress:
- [ ] Step 1: Functional Analysis
- [ ] Step 2: Ontology Analysis
- [ ] Step 3: Critical Review
- [ ] Step 4: Biological Context
- [ ] Step 5: Focused Analysis & Prioritization
- [ ] Step 6: Deeper Analysis
- [ ] Step 7: Documentation

```

## Detailed Instructions

### Step 1: Functional Analysis

- Identify relationships between differentially expressed proteins (DEPs) using your broad biological knowledge and functional protein annotations (e.g. in uniprot)
- Investigate whether differentially expressed proteins are functionally related, e.g. via biochemical pathways or gene sets, or protein complexes.

### Step 2: Ontology Analysis

- Interpret enrichment results (e.g., GSEA/ORA) if available
- Examine which cellular processes are most affected based on protein changes
- Identify shared proteins between pathways (leading edges) as potential regulatory hubs

### Step 3: Critical Review

- Review all collected information
- Flag contradictions between different analyses
- Identify repeating patterns across analyses

### Step 4: Biological Context

- Consider the experimental design and research question for interpretation
- Explain implications of changes across biological scales (organelles, cells, organs, organism)
- Assess how these changes might affect overall biological function

### Step 5: Focused Analysis & Prioritization

- Recommend **3–5 key aspects** for further investigation (e.g., specific ontology terms, mechanisms, or homeostatic pathways)
- Explain the scientific rationale for each recommendation
- Select the most promising aspect for deep-dive analysis in the next step

### Step 6: Deeper Analysis (Post-Selection)

- List all proteins involved in the selected aspect
- Gather all available information on key proteins with the tool UniProt
- Retrieve quantitative information for all proteins within the identified pathways or functional units
- Place findings within the context of the current understanding in the field
- Identify results that either confirm or challenge existing biological models
- **Develop formal findings** explaining the observed patterns
- Suggest follow-up experiments to validate these findings

### Step 7: Documentation

- Compile findings into an `observed_findings.md` file.
- Each formal finding from step 6 should be summarized in a table with the following content:

| Finding | Related proteins | Evidence from Database search + Literature | (If applicable): Contradicting evidence | Suggested follow up experiment

# Success Criteria

- findings must be supported by both the protein change and the known biological function
- An `observed_findings.md` file must be generated