Bioinformatician Skill

Bioinformatician Skill

Purpose

Implement computational analyses of biological data, including:

• Data loading and quality control
• Statistical analysis
• Bioinformatics pipelines
• Visualization
• Integration with domain-specific tools

When to Use This Skill

Use this skill when you need to:

• Implement an analysis plan in code (from PI)
• Process genomics/​transcriptomics/​proteomics data
• Perform statistical tests on biological data
• Create publication-quality visualizations
• Build reproducible analysis pipelines
• Integrate multiple bioinformatics tools

Workflow Integration

Primary Pattern: Receive Plan → Implement → Deliver Notebook

Receive analysis_plan.md from PI
    ↓
Implement in Jupyter notebook
    ↓  (copilot reviews continuously)
Deliver completed notebook to PI for interpretation

Integration Points:

• RECEIVES: Analysis plan from principal-investigator
• WORKS WITH: copilot (adversarial code review during implementation)
• CALLS: Domain-specific skills (scanpy, pydeseq2, biopython, etc.)
• OUTPUTS: Jupyter notebooks with analysis code + results

Core Capabilities

1. Data Loading and Validation

• Read common formats (CSV, TSV, HDF5, Parquet, FASTQ, BAM, VCF)
• Validate data integrity and format
• Handle compressed files
• Memory-efficient loading for large datasets

2. Quality Control

• Sample quality metrics
• Outlier detection
• Batch effect assessment
• Positive/​negative control validation

3. Statistical Analysis

• Differential expression/​abundance
• Enrichment analysis
• Clustering and dimensionality reduction
• Correlation and regression
• Multiple testing correction

4. Visualization

• Publication-quality plots (matplotlib, seaborn, plotly)
• Interactive visualizations
• Consistent styling
• Proper labeling and legends

5. Pipeline Development

• Modular, reusable code
• Parameter documentation
• Progress logging
• Error handling

Standard Notebook Structure

Use the template in assets/​notebook_structure_template.ipynb:

1. Title and Description
   - Research question
   - Date, author
   - Reference to analysis plan

2. Setup
   - Imports
   - Configuration parameters
   - Random seeds for reproducibility

3. Data Loading
   - Read data files
   - Initial inspection
   - Data structure validation

4. Quality Control
   - Sample metrics
   - Filtering criteria
   - QC visualizations

5. Analysis
   - Statistical tests
   - Transformations
   - Model fitting

6. Visualization
   - Main figures
   - Supplementary plots

7. Export Results
   - Save processed data
   - Export figures
   - Summary statistics

8. Session Info
   - Package versions
   - Execution time

Biological Literacy Framework

Writing Style for Biological Context

All biological context in notebooks should follow concise scientific prose:

Principles:

• ✅ Brief: 1-3 sentences per section, not paragraphs
• ✅ Clear: Use precise biological terminology
• ✅ Factual: State what/​why without excessive detail
• ✅ Publication-ready: Like Methods/​Results sections in papers

Example - Good (Concise):

## Biological Context
Differential expression analysis comparing wild-type and mutant neurons identifies genes affected by loss of transcription factor X. Expected upregulation of target genes based on ChIP-seq data (Smith et al. 2020).

Example - Avoid (Too Verbose):

## Biological Context
In this analysis, we will perform differential expression analysis to compare gene expression between wild-type neurons and neurons with a mutation in transcription factor X. Previous research has shown that transcription factor X plays a critical role in neuronal development by binding to the promoters of many developmentally important genes...

When to Provide Interpretation vs Handoff

Bioinformatician Handles (routine interpretation):

• Standard results following known biology
• Positive/​negative controls behaving as expected
• Results matching literature precedents
• Technical QC assessments with biological implications
• Magnitude/​direction sanity checks

Handoff to Biologist-Commentator (expert needed):

• Novel or unexpected findings
• Results contradicting established biology
• Unclear biological mechanisms
• Publication-critical interpretations
• Proposing new hypotheses or models

Enhanced Notebook Structure

Use this structure for biologically-literate notebooks:

1. Title and Scientific Context
   - Research question (biological, not just technical)
   - Biological hypothesis
   - Expected outcome and why it matters
   - Relevant background (1-2 sentences)

2. Setup (code)
   - Imports, parameters, seeds

3. Data Loading
   - Code: Load data
   - Biological description of dataset (markdown):
     * What organism/​tissue/​condition
     * What genes/​features measured
     * What biological question dataset addresses

4. Quality Control
   - Code: QC metrics, filtering
   - Biological interpretation of QC (markdown):
     * Are pass rates expected for this data type?
     * Do failed samples have biological meaning?
     * Red flags from biological perspective?

5. Analysis
   - Code: Statistical tests, transformations
   - Biological reasoning for each step (markdown):
     * Why this method for this question?
     * What biological assumption being tested?
     * Positive/​negative controls?

6. Results
   - Code: Generate results
   - Biological sanity checks (markdown):
     * Do magnitudes make sense?
     * Do directions align with biology?
     * Any known biology violated?

7. Visualization
   - Code: Plots
   - Biological interpretation scaffolding (markdown):
     * What biological pattern does this show?
     * Is this expected or surprising?
     * What follow-up questions does this raise?

8. Preliminary Interpretation
   - Bioinformatician's biological assessment (markdown):
     * Main findings in biological terms
     * Caveats and limitations
     * Questions for biologist-commentator

9. Handoff to Expert (if needed)
   - Structured questions for biologist-commentator (markdown):
     * Specific results needing interpretation
     * Unexpected findings to validate
     * Biological mechanisms to explore

10. Export (code)
    - Save data, figures, session info

Biological Sanity Check Framework

Run these checks before accepting results:

Expression/​Abundance Checks

• Order of magnitude reasonable? (log2FC > 10 is suspicious)
• Direction matches known biology? (check a few known genes)
• Positive controls behave as expected?
• Negative controls show no signal?

Statistical Checks with Biological Lens

• Top hits include known biology? (literature validation)
• Results robust to threshold changes?
• Batch effects vs real biology separated?
• Multiple testing appropriate for biology? (discovery vs validation)

Genomics-Specific

• Chromosome names consistent? (chr1 vs 1)
• Coordinates sensible? (within chromosome bounds)
• Strand orientation correct for gene features?
• Genome build consistent throughout?

Experimental Design

• Sample size adequate for this effect size?
• Replicates biological or technical?
• Confounders identified and addressed?
• Controls appropriate for this experiment type?

If any check fails: Document in notebook, flag for biologist-commentator review

Biological Context Templates

Template: Differential Expression Analysis

## Biological Context
Comparing [condition A] vs [condition B] to identify genes involved in [biological process]. Expected upregulation of [pathway X] genes based on [mechanism/​literature]. Positive controls: [gene1, gene2]. Expected log2FC range: [X-Y] based on [citation].

## Biological Sanity Checks
- [ ] Known pathway genes show expected direction (e.g., gene1 ↑, gene2 ↓)
- [ ] Housekeepers unchanged (actb, gapdh)
- [ ] Magnitudes reasonable (log2FC < 10 for transcriptional regulation)

## Preliminary Interpretation
Top hits include [gene X, Y, Z] involved in [biological process], consistent with [hypothesis/​literature]. [Gene W] unexpected - requires expert validation.

**Handoff**: Unexpected downregulation of [gene W] contradicts known role in [process]. Biologist-commentator needed for mechanism assessment.

Template: Single-Cell Clustering

## Biological Context
Clustering [tissue] cells to identify cell types. Expected populations: [celltype1 (markers: a,b,c), celltype2 (markers: d,e,f)]. Reference atlas: [citation if available].

## Cluster Validation
- Cluster 1: [celltype] - markers: [genes] ✓
- Cluster 2: [celltype] - markers: [genes] ✓
- Cluster 3: Novel population - markers: [genes] - needs expert review

**Handoff**: Cluster 3 shows unexpected marker combination [X+Y+Z-]. Biologist-commentator needed for cell type identification and biological significance.

Template: Expert Handoff Format

Use this concise format when escalating to biologist-commentator:

## Expert Interpretation Needed

**Finding**: [Specific result with statistics]
**Context**: [1-2 sentence background]
**Issue**: [What's unexpected/​unclear and why]
**Question**: [Specific question for expert]

**Validation Done**: [Positive controls: ✓/✗, Literature: consistent/​contradicts]

Example:

## Expert Interpretation Needed

**Finding**: Gene X shows 8-fold upregulation (padj<0.001) in mutant vs WT
**Context**: Gene X is transcriptional repressor, expected downregulation of targets
**Issue**: Target genes also upregulated (contradicts repressor function)
**Question**: Alternative mechanism? Post-transcriptional regulation? Data artifact?

**Validation Done**: Positive controls ✓, replicates consistent ✓, literature shows conflicting results

Biologist-Commentator Integration Pattern

When to Invoke Biologist-Commentator

Pre-Analysis (Method Validation):

Skill(skill="biologist-commentator", args="Validate that DESeq2 appropriate for [specific experiment design]. Confirm controls adequate and confounders addressed.")

During Analysis (Quick Check):

• Use biological sanity check framework (above)
• Document any red flags
• Continue if checks pass, escalate if fail

Post-Analysis (Expert Interpretation):

Skill(skill="biologist-commentator", args="Interpret biological significance of [specific finding]. Results show [X], which is [expected/​unexpected]. Known biology suggests [Y]. Please validate interpretation and suggest mechanisms.")

Handoff Workflow

1. Bioinformatician: Run analysis, perform sanity checks, document findings
2. Handoff: Create structured handoff section in notebook (see template above)
3. Biologist-Commentator: Provides expert interpretation, mechanism insights, validation
4. Bioinformatician: Incorporate interpretation into notebook, flag needed validations

Pre-Flight Checklist

Before starting implementation, verify:

• Analysis plan clearly defines objectives
• Data files exist and paths are correct
• Required packages installed
• Expected output format understood
• Random seeds set for reproducibility

Use assets/​analysis_checklist.md for complete list.

Reproducibility Standards

Critical: Every bioinformatics analysis must be fully reproducible. Another researcher should be able to recreate your computational environment and obtain identical results.

Environment Documentation (Mandatory)

Start every notebook with environment documentation:

# %%
# Computational Environment
import sys
import numpy as np
import pandas as pd
import scanpy as sc  # or relevant packages

print("=" * 60)
print("COMPUTATIONAL ENVIRONMENT")
print("=" * 60)
print(f"Python: {sys.version}")
print(f"NumPy: {np.__version__}")
print(f"Pandas: {pd.__version__}")
print(f"Scanpy: {sc.__version__}")  # Replace with your key packages
print("=" * 60)
print("\nFor full environment, see requirements.txt")

Create environment files before starting analysis:

# For conda users (recommended for bioinformatics):
conda env export > environment.yml

# For pip users:
pip freeze > requirements.txt

# Document which file to use in notebook

In notebook markdown cell:

## Computational Environment

- **Kernel**: Python 3.11 (bio-analysis-env)
- **Environment file**: `environment.yml` (recreate with `conda env create -f environment.yml`)
- **Key packages**: scanpy==1.10.0, numpy==1.26.3, pandas==2.2.0, scipy==1.12.0
- **Execution date**: 2026-01-29

Random Seed Setting (Mandatory for Stochastic Processes)

Set seeds in setup cell:

# %%
# Random seeds for reproducibility
import numpy as np
import random

RANDOM_SEED = 42  # Document choice (convention, replicating published analysis, etc.)

# Core Python/​NumPy
np.random.seed(RANDOM_SEED)
random.seed(RANDOM_SEED)

# Scanpy (single-cell analysis)
import scanpy as sc
sc.settings.seed = RANDOM_SEED

# PyTorch (if using deep learning)
import torch
torch.manual_seed(RANDOM_SEED)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(RANDOM_SEED)

# TensorFlow (if using)
import tensorflow as tf
tf.random.set_seed(RANDOM_SEED)

print(f"Random seed set to {RANDOM_SEED} for reproducibility")

Bioinformatics operations requiring seeds:

• Dimensionality reduction: UMAP, t-SNE, PCA with randomized SVD
• Clustering: Leiden, Louvain (graph-based)
• Sampling: Random subsampling, bootstrap, cross-validation
• Imputation: Stochastic imputation methods
• Simulation: Monte Carlo, permutation tests
• Machine learning: Random forests, neural networks, k-means initialization

Document in notebook:

## Stochastic Operations
This analysis uses:
- UMAP (random initialization, seed=42)
- Leiden clustering (random walk, seed=42)
- 1000-iteration permutation test (seed=42)

All seeds set to 42 for reproducibility.

Session Info Output (Mandatory)

End every notebook with comprehensive session info:

# %%
# Session Information for Reproducibility
import session_info

session_info.show(
    dependencies=True,
    html=False
)

# Alternative for single-cell workflows:
# import scanpy as sc
# sc.logging.print_versions()

# Alternative for base Python:
# import sys
# import pkg_resources
# print(f"Python: {sys.version}")
# for pkg in ['numpy', 'pandas', 'scipy', 'matplotlib', 'seaborn']:
#     print(f"{pkg}: {pkg_resources.get_distribution(pkg).version}")

What this captures:

• Python version
• Operating system
• All package versions (including dependencies)
• Execution timestamp

Why this matters:

• API changes between package versions
• Statistical method implementations evolve
• Bugs get fixed (results may change)
• Reviewers need to verify methods

File Path Best Practices

Use relative paths and variables:

# %%
from pathlib import Path

# Define all paths at top of notebook
DATA_DIR = Path("data/​raw")
PROCESSED_DIR = Path("data/​processed")
RESULTS_DIR = Path("results/​analysis_2026-01-29")
FIGURES_DIR = RESULTS_DIR / "figures"

# Create output directories
for directory in [PROCESSED_DIR, RESULTS_DIR, FIGURES_DIR]:
    directory.mkdir(parents=True, exist_ok=True)

# Use variables throughout
counts_file = DATA_DIR / "counts_matrix.h5ad"
metadata_file = DATA_DIR / "sample_metadata.csv"
output_file = PROCESSED_DIR / "normalized_counts.h5ad"
figure_file = FIGURES_DIR / "umap_clusters.pdf"

print(f"Data directory: {DATA_DIR.resolve()}")
print(f"Results directory: {RESULTS_DIR.resolve()}")

Never use hardcoded absolute paths:

# ❌ BAD (non-reproducible):
adata = sc.read_h5ad("/​Users/​yourname/​project/​data/​counts.h5ad")
plt.savefig("/​Users/​yourname/​Desktop/​figure.pdf")

# ✅ GOOD (reproducible):
adata = sc.read_h5ad(DATA_DIR / "counts.h5ad")
plt.savefig(FIGURES_DIR / "umap_clusters.pdf")

Data Provenance Documentation

Document data sources in notebook:

## Data Sources

### Input Data
- **File**: `data/​raw/​GSE123456_counts.h5ad`
- **Source**: GEO accession GSE123456
- **Download date**: 2026-01-15
- **Download command**: `wget https://www.ncbi.nlm.nih.gov/​geo/​download/?acc=GSE123456`
- **Original publication**: Smith et al. (2025) Nature 600:123-130
- **Organism**: Homo sapiens
- **Tissue**: Primary cortical neurons
- **n samples**: 50 (25 control, 25 treatment)
- **n features**: 20,000 genes

### Reference Data
- **Genome build**: GRCh38 (hg38)
- **Gene annotations**: GENCODE v42
- **Downloaded**: 2026-01-10 from https://www.gencodegenes.org/

Why this matters:

• Data can be updated or removed from repositories
• Genome builds affect coordinate-based analyses
• Sample metadata clarifies experimental design
• Enables others to download identical data

Reproducibility Pre-Flight Checklist

Before starting analysis, verify:

• Environment documented (environment.yml or requirements.txt exists)
• Environment creation documented in notebook
• Random seeds will be set for all stochastic operations
• File paths use variables (no hardcoded absolute paths)
• Data sources documented (where to download, version, date)
• Genome build / reference database versions specified
• Session info cell will be added at end

Before handoff to PI, verify:

• Notebook runs end-to-end without errors (Restart Kernel & Run All)
• Results reproducible (run twice, identical outputs)
• All figures saved to FIGURES_DIR with descriptive names
• All processed data saved to PROCESSED_DIR
• Session info cell executed and output visible
• Execution time reasonable (< 2 hours for routine analyses)

Integration with notebook-writer Skill

When creating notebooks programmatically, use notebook-writer skill with reproducibility standards:

from pathlib import Path

# Use notebook-writer to create template
cells = [
    {'type': 'markdown', 'content': '## Computational Environment\n...'},
    {'type': 'code', 'content': 'import sys\nprint(f"Python: {sys.version}")'},
    {'type': 'markdown', 'content': '## Data Loading\n...'},
    # ... analysis cells ...
    {'type': 'markdown', 'content': '## Session Info'},
    {'type': 'code', 'content': 'import session_info\nsession_info.show()'}
]

# Create reproducible notebook
notebook_path = create_notebook_markdown(
    title="Reproducible RNA-seq Analysis",
    cells=cells,
    output_path=Path("analysis/​rnaseq_analysis.md")
)

Common Reproducibility Failures and Fixes

Bioinformatics-Specific Reproducibility Considerations

Organism and Reference Versions:

# Document in code cell
ORGANISM = "Homo sapiens"
GENOME_BUILD = "GRCh38"  # or "mm39" for mouse, "dm6" for fly, etc.
ANNOTATION_VERSION = "GENCODE v42"  # or "Ensembl 110"
ANNOTATION_DATE = "2026-01-10"

print(f"Analysis configuration:")
print(f"  Organism: {ORGANISM}")
print(f"  Genome: {GENOME_BUILD}")
print(f"  Annotations: {ANNOTATION_VERSION} ({ANNOTATION_DATE})")

Bioinformatics Tools (if used):

## External Tools
- **STAR aligner**: v2.7.11a (for read mapping)
- **MACS2**: v2.2.9.1 (for peak calling)
- **bedtools**: v2.31.0 (for interval operations)

All tools available in conda environment (see environment.yml).

Data Processing Parameters:

# Document all filtering/​QC thresholds
QC_PARAMS = {
    'min_genes_per_cell': 200,
    'min_cells_per_gene': 3,
    'max_pct_mt': 15,  # percent mitochondrial reads
    'min_counts': 1000,
    'highly_variable_genes': 2000,
    'n_pcs': 50,  # principal components
    'umap_neighbors': 15,
    'leiden_resolution': 0.8
}

print("Quality control parameters:")
for param, value in QC_PARAMS.items():
    print(f"  {param}: {value}")

Code Quality Standards

During Implementation

• Copilot reviews continuously - expect adversarial feedback
• Write clear comments explaining biological context
• Use descriptive variable names
• Modularize repeated operations into functions
• Log progress for long-running analyses

Testing

• Validate on small test data first
• Check edge cases (empty data, single sample, all zeros)
• Compare to expected results (positive controls)
• Verify reproducibility (run twice, same results)

Common Analysis Patterns

Pattern 1: Differential Expression (RNA-seq)

# 1. Load counts
# 2. Filter low-abundance genes
# 3. Normalize (DESeq2, TMM, or library size)
# 4. Statistical test (DESeq2, edgeR, limma)
# 5. Multiple testing correction
# 6. Volcano plot + heatmap

→ Use pydeseq2 skill for implementation details

Pattern 2: Single-Cell Analysis

# 1. Load AnnData object
# 2. QC filtering (cells and genes)
# 3. Normalization and log-transform
# 4. Feature selection (highly variable genes)
# 5. Dimensionality reduction (PCA, UMAP)
# 6. Clustering
# 7. Marker gene identification
# 8. Visualization

→ Use scanpy skill for implementation details

Pattern 3: Sequence Analysis

# 1. Read FASTA/​FASTQ
# 2. Quality filtering
# 3. Alignment or motif search
# 4. Feature extraction
# 5. Statistical summary

→ Use biopython skill for implementation details

References

For detailed guidance:

• references/​analysis_workflows.md - Step-by-step workflows for common analyses
• references/​data_structures.md - When to use pandas/​anndata/​Bioconductor
• references/​statistical_methods.md - Which test for which data
• references/​visualization_best_practices.md - Plot selection and styling

Helper Scripts

Available in scripts/:

• qc_pipeline.py - Automated QC for RNA-seq data
• differential_expression_template.py - Complete DESeq2 pipeline
• data_loader_helpers.py - Functions for common file formats

Usage: Read these scripts as reference implementations, copy/​adapt for your specific analysis, or call directly via Bash if appropriate.

Integration with Domain Skills

When analysis requires specialized knowledge:

Pattern:

1. Use bioinformatician for overall workflow
2. Invoke specialized skill for domain-specific steps
3. Integrate results back into main analysis

Copilot Review Integration

During implementation, copilot skill reviews your code:

• Expect critical feedback (adversarial but constructive)
• Fix issues immediately before proceeding
• Iterate until code is robust
• Don't take criticism personally - it catches bugs early

Deliverables

Complete notebook should include:

Technical Components (existing):

1. Code cells: Well-commented, modular analysis
2. Visualizations: Publication-ready figures
3. Statistics: Complete reporting (test, p-value, effect size, n)
4. Exports: Processed data files, figure files
5. Session info: Package versions for reproducibility

Biological Components (new): 6. Biological Context Cells (markdown):

• Research question in biological terms
• Hypothesis and expected outcomes
• Biological description of each analysis step
• Relevance to biological question

1. Sanity Check Documentation (markdown):

   • Results of biological plausibility checks
   • Positive/​negative control validation
   • Known biology comparison
   • Red flags or concerns

2. Preliminary Interpretation (markdown):

   • Main findings in biological language
   • Consistency with expectations
   • Novel or surprising results
   • Biological implications

3. Expert Handoff Section (markdown, if needed):

   • Structured questions for biologist-commentator
   • Specific findings needing interpretation
   • Recommended follow-up analyses
   • Caveats and limitations

Quality Indicator: Notebook should be readable by biologist who doesn't code

Quality Indicators

Your notebook is ready when:

Technical Quality:

• All code executes without errors
• Random seed set, results reproducible
• QC checks passed (positive controls work)
• Visualizations properly labeled
• Statistics completely reported
• Copilot approved code (no outstanding critical issues)

Biological Quality:

• Biological context provided for all major sections (concise, 1-3 sentences)
• Biological sanity checks completed and documented
• Positive/​negative controls validated against biological expectations
• Preliminary interpretation written in biological terms
• Handoff to biologist-commentator structured (if unexpected findings)
• Notebook readable by non-coding biologist

Integration Ready:

• Ready for PI to expand interpretations for publication
• Clear which findings are routine vs need expert review