Bio reporting jupyter reports
Skill FridrichMethod/awesome-skills/skills/bio-reporting-jupyter-reports
Curated, auto-synced collection of 2,000+ Claude Code & Codex skills for AI4Protein, bioinformatics, AI development, and academic paper writing. One curl command installs them all.
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Creates reproducible Jupyter notebooks for bioinformatics analysis with parameterization using papermill. Use when generating automated analysis reports, running notebook-based pipelines, or creating shareable computational notebooks.
SKILL.md
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Jupyter Reports with Papermill
Parameterized Notebooks
import papermill as pm
# Execute notebook with parameters
pm.execute_notebook(
'analysis_template.ipynb',
'output_report.ipynb',
parameters={
'input_file': 'data/counts.csv',
'condition_col': 'treatment',
'fdr_threshold': 0.05
}
)
Creating Parameterized Templates
Mark a cell with the parameters tag in Jupyter:
# Parameters (tag this cell as "parameters")
input_file = 'default.csv'
output_dir = 'results/'
fdr_threshold = 0.05
Batch Processing
import papermill as pm
from pathlib import Path
samples = ['sample1', 'sample2', 'sample3']
for sample in samples:
pm.execute_notebook(
'qc_template.ipynb',
f'reports/{sample}_qc.ipynb',
parameters={'sample_id': sample}
)
Converting to HTML/PDF
# Single notebook
jupyter nbconvert --to html report.ipynb
# With execution
jupyter nbconvert --execute --to html report.ipynb
# PDF (requires pandoc + LaTeX)
jupyter nbconvert --to pdf report.ipynb
Best Practices
- Keep analysis code in cells, explanatory text in markdown
- Use parameters for all configurable values
- Include version information and timestamps
- Clear outputs before committing to version control
Related Skills
- reporting/quarto-reports - Alternative report format
- reporting/rmarkdown-reports - R-based reports
- workflows/rnaseq-to-de - Embed in workflows