drugclaw/omics-tools

Omics Tools

Use this skill when the user asks to inspect or triage omics datasets before deeper modeling.

Typical triggers:

• inspect h5ad or annotated single-cell matrices
• summarize cell types, batches, and QC columns from AnnData
• check alignment coverage or region counts from BAM or CRAM files
• profile a mass-spectrometry mzML experiment before proteomics or metabolomics analysis
• verify whether a dataset is ready for Scanpy, PyDESeq2, or downstream modeling

Environment Check

which python3 || true
python3 - <<'PY'
mods = ["pandas", "numpy", "anndata", "pysam"]
extra = ["scanpy", "pydeseq2", "pyopenms", "skbio"]
for name in mods + extra:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY

Do not claim single-cell, alignment, or mass-spec analysis ran if the required module is absent.

Bundled Assets

• templates/single_cell_profile.py
• templates/pysam_region_profile.py
• templates/mzml_summary.py

Preferred Workflow

1. Start with structural profiling before statistical interpretation.
2. For single-cell data, inspect dimensions, metadata coverage, and top group counts before clustering or marker analysis.
3. For BAM or CRAM data, report mapped reads, index presence, and region counts before variant or expression conclusions.
4. For mzML data, summarize spectra and acquisition structure before quantification.
5. Save both a tabular output and a compact summary JSON.

Single-Cell And AnnData Profiling

python3 templates/single_cell_profile.py \
  --input data/pbmc.h5ad \
  --cell-type-column cell_type \
  --group-column batch \
  --group-column donor \
  --output omics/pbmc_profile.csv \
  --summary omics/pbmc_profile.json

Use this first for:

• cell and gene counts
• observation and variable column inventory
• top cell-type or batch distributions
• quick readiness checks before Scanpy or scvi-style modeling

Alignment Profiling With Pysam

python3 templates/pysam_region_profile.py \
  --bam alignments/sample.bam \
  --region chr7:55019017-55211628 \
  --region chr12:25205246-25250928 \
  --output omics/sample_region_profile.csv \
  --summary omics/sample_region_profile.json

Use this for:

• mapped versus unmapped read counts
• region-specific read totals
• quick QA before variant or coverage workflows

Mass-Spectrometry Inventory

python3 templates/mzml_summary.py \
  --input proteomics/run01.mzML \
  --output omics/run01_mzml_profile.csv \
  --summary omics/run01_mzml_profile.json

Use this for:

• spectra and chromatogram counts
• MS level inventory
• retention-time range inspection before full pyOpenMS workflows

Working Boundary

This skill is for data profiling and workflow triage. It does not replace full differential-expression analysis, trajectory inference, peptide identification, or validated clinical interpretation.

Output Expectations

Good answers should mention:

• exact file paths and any regions or columns used
• which template ran
• core dataset dimensions or counts
• what output files were written
• whether the result is only profiling or a deeper analytical conclusion
• any missing modules, index files, or malformed records

Related Skills

For general sequence analysis or command-line bioinformatics, activate bio-tools. For remote biology APIs such as GEO, Ensembl, UniProt, PDB, or Reactome, activate bio-db-tools. For transcription-factor network inference from processed expression matrices, activate grn-tools. For statistical modeling or survival analysis on omics-derived tables, activate stat-modeling-tools or survival-analysis-tools. For static or interactive omics figures, activate scientific-visualization-tools. For chemistry, ADMET, QSAR, or structure-aware affinity, activate chem-tools.