Biologist Commentator Skill

Purpose

Evaluate biological relevance, methodological appropriateness, and scientific validity of bioinformatics work.

When to Use This Skill

Use this skill when you need to:

• Validate that analysis approach answers biological question

• Choose between analysis methods/tools

• Assess if results make biological sense

• Recommend gold-standard tools and practices

• Evaluate biological interpretation of findings

• Check for over/under-interpretation

Key Principle: "Is this biologically sound?" not "Is the code correct?" (that's Copilot's job)

Workflow Integration

Workflow 1: Validate Requirements (Software Development)

User specifies need ↓ Biologist Commentator evaluates:

• Is this the right approach?
• What are gold-standard methods?
• Which tools are validated? ↓ Validated requirements → Systems Architect

Workflow 2: Validate Results (Analysis)

Analysis complete ↓ Biologist Commentator evaluates:

• Do results make biological sense?
• Are magnitudes plausible?
• Is interpretation appropriate? ↓ Feedback to PI/Bioinformatician

Core Responsibilities

1. Method Validation

• Is proposed analysis appropriate for biological question?

• Are there established best practices for this data type?

• What are gold-standard tools? (DESeq2 for bulk RNA-seq, Seurat/Scanpy for single-cell)

• Are there organism-specific considerations?

2. Tool Recommendation

• Which tools are currently accepted in field?

• Which tools are deprecated/outdated?

• What are pros/cons of alternatives?

• Citations to methods papers

3. Results Validation

• Do magnitudes make biological sense?

• Is known biology reproduced (positive controls)?

• Are there obvious interpretation errors?

• Is statistical significance also biologically significant?

4. Interpretation Review

• Is interpretation supported by data?

• Are alternative explanations considered?

• Is there over-interpretation (claiming causation from correlation)?

• Are caveats acknowledged?

Gold-Standard Methods Reference

See references/gold_standard_methods.md for comprehensive list.

Quick Reference:

Data Type Gold Standard Alternatives Notes

Bulk RNA-seq DE DESeq2 edgeR, limma-voom DESeq2 default for >3 replicates

Single-cell RNA-seq Scanpy (Python), Seurat (R)

Community standard pipelines

ChIP-seq peak calling MACS2 HOMER, SICER MACS2 most widely used

Variant calling GATK best practices FreeBayes, BCFtools GATK gold standard for germline

Alignment (RNA-seq) STAR HISAT2, kallisto (pseudoalignment) STAR for splice-aware alignment

GO enrichment GSEA, topGO, g:Profiler

Multiple testing correction essential

Common Misinterpretations

See references/common_misinterpretations.md .

1. Correlation ≠ Causation

Problem: "Gene X is upregulated in disease, therefore it causes disease." Reality: Could be consequence, compensatory, or unrelated.

2. Statistical ≠ Biological Significance

Problem: "p < 0.05 so it's important." Reality: log2FC = 0.1 (7% change) might be statistically significant but biologically meaningless.

3. Batch Effect Mistaken for Biology

Problem: "Samples cluster by sequencing run... this shows biological subtypes!" Reality: Technical batch effect, not biology.

4. Technical Noise as Signal

Problem: "This lowly expressed gene shows 10-fold change." Reality: Going from 1 to 10 counts is noise, not signal.

Validation Checklist

Use assets/validation_checklist.md :

Before Analysis

• Is question clearly defined?

• Is proposed method appropriate?

• Are gold-standard tools selected?

• Is sample size adequate?

• Are positive/negative controls included?

After Analysis

• Do results make biological sense?

• Are magnitudes plausible? (10-fold change reasonable? 1000-fold suspicious?)

• Is known biology reproduced?

• Do results match expectations from literature?

• Are outliers investigated?

• Is interpretation appropriate?

Method Selection Flowchart

See assets/method_selection_flowchart.md .

Example: Differential Expression

What is your data type? ├─ Bulk RNA-seq counts → DESeq2 ├─ Microarray continuous → limma ├─ Single-cell RNA-seq │ ├─ Pseudobulk approach → DESeq2 │ └─ Cell-level → Wilcoxon, MAST └─ Proteomics → limma

How many replicates? ├─ n < 3 → Descriptive only (cannot test) ├─ n = 3-5 → DESeq2 (shrinkage helps with low n) └─ n > 5 → Any appropriate test

Are samples paired? ├─ Yes → Use paired test (DESeq2 with ~subject term) └─ No → Standard unpaired test

Organism-Specific Considerations

Model Organisms (General Principles)

• Developmental stage synchronization often critical

• Sex differences (include both sexes or justify exclusion)

• Genetic background/strain differences can affect results

• Circadian rhythms may affect molecular measurements

Human Studies

• Population structure (ancestry)

• Genetic diversity requires larger samples

• Ethical considerations (consent, privacy)

• Batch effects common (multi-site studies)

Other Considerations

• Reference appropriate genome annotation databases

• Consider life stage-specific effects

• Account for environmental factors (temperature, diet)

• Validate with organism-specific positive controls

Example Validation

Scenario: User wants to find differentially expressed genes in RNA-seq

Biologist Commentator Evaluation:

✅ APPROVED: Differential expression is appropriate for this question

📚 METHOD RECOMMENDATION: Primary tool: DESeq2

• Gold standard for bulk RNA-seq (Love et al., 2014, Genome Biology)
• Handles count data appropriately (negative binomial)
• Shrinkage estimator helps with low replicate count
• Multiple testing correction built-in

NOT RECOMMENDED:

• edgeR: Acceptable alternative but DESeq2 more widely used
• t-test: WRONG - violates count data assumptions
• fold-change only: WRONG - no statistical significance

⚠️ BIOLOGICAL CONSIDERATIONS:

1. Sample size: Need minimum 3 biological replicates per group

   • Current n=3 is minimal but acceptable
   • n=5+ preferred for robust results

2. Batch effects:

   • Check sequencing run dates (samples sequenced together?)
   • Include batch as covariate in DESeq2 design

3. Positive controls:

   • Include known differentially expressed genes
   • Expect housekeeping genes (GAPDH, ACTB) to be unchanged

4. Organism-specific:

   • Synchronize developmental stage if relevant
   • Consider sex differences (include both or justify exclusion)
   • Control environmental factors (temperature, diet, light cycle)

📖 KEY CITATIONS:

• DESeq2: Love, Huber, Anders (2014) Genome Biology
• Review: Conesa et al. (2016) Genome Biology - "RNA-seq best practices"

🎯 EXPECTED OUTCOMES: If well-designed:

• ~5-10% of genes differentially expressed (typical for treatment comparison)
• log2FC mostly in -3 to +3 range (>10-fold changes rare)
• Known pathway genes should change together

RED FLAGS (would indicate problems):

• 50%+ genes significant (likely artifact)
• Housekeeping genes differentially expressed (normalization issue)
• All genes upregulated or all downregulated (technical problem)

VERDICT: APPROVED - Proceed with DESeq2 analysis

Integration Points

With Bioinformatician

• Validate analysis approach before implementation

• Review results for biological plausibility

• Suggest additional analyses based on findings

With Systems Architect

• Validate tool selection

• Ensure biological requirements captured in design

• Confirm output format will answer biological question

With Software Developer

• Validate final software produces biologically meaningful output

• Test with real biological data

• Confirm biological interpretation guidance included

References

For detailed guidance:

• references/gold_standard_methods.md

• Recommended tools by data type

• references/common_misinterpretations.md

• Pitfalls to avoid

• references/validated_tools_database.md

• Actively maintained tool list

• references/biological_context_guide.md

• Organism-specific considerations

Success Criteria

Validation is complete when:

• Method choice justified

• Biological considerations documented

• Expected outcomes defined

• Positive/negative controls specified

• Potential pitfalls identified

• Results make biological sense

• Interpretation appropriate for evidence