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omics-tools

Profile omics datasets by summarizing metrics from AnnData, BAM/CRAM, and mzML files.

126|8|Updated Mar 10, 2026
Search Tags:#bioinformatics#mass spectrometry#alignment#single-cell#omics#data profiling
One-click install
npx skills add https://github.com/DrugClaw/DrugClaw --skill omics-tools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omics-tools
Source: https://github.com/DrugClaw/DrugClaw/tree/main/skills/science/omics-tools
Command: npx skills add https://github.com/DrugClaw/DrugClaw --skill omics-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, anndata, pysam, scanpy, pydeseq2, pyopenms, skbio, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users quickly inspect and triage various omics datasets (single-cell, alignment, mass-spectrometry) before diving into complex downstream analysis, ensuring data readiness and identifying potential issues early.

Core Features & Use Cases

  • AnnData Profiling: Summarize cell types, batch information, and QC metrics from h5ad files using Scanpy-style checks.
  • Alignment Inspection: Profile BAM/CRAM files for read counts, mapped/unmapped statistics, and region-specific coverage using pysam.
  • Mass-Spectrometry Triage: Inventory mzML files for spectra counts, MS levels, and retention time ranges using pyOpenMS.
  • Use Case: Before running a differential expression analysis on single-cell RNA-seq data, use this Skill to get a quick overview of cell type distributions and batch effects in your h5ad file.

Quick Start

Use the omics-tools skill to profile the AnnData file 'data/pbmc.h5ad', checking for 'cell_type' and 'batch' columns.

Frequently Asked Questions about omics-tools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile an AnnData h5ad file before running single-cell RNA-seq analysis?▼

To profile an AnnData h5ad file, you can use this Skill to summarize cell types, batch information, and QC metrics using Scanpy-style checks. It helps identify batch effects and data readiness before downstream modeling.

What is the best way to triage BAM or CRAM alignment files for read statistics?▼

The best way to triage BAM or CRAM alignment files is using pysam to profile read counts, mapped and unmapped statistics, and region-specific coverage. This provides a quick structural overview of your alignment data.

Can I inventory mass-spectrometry mzML files for spectra counts and retention time ranges?▼

Yes, you can inventory mzML files for spectra counts, MS levels, and retention time ranges using pyOpenMS. This triage step summarizes key structural properties before advanced mass-spectrometry analysis.

Do I need specific Python libraries installed to inspect omics datasets?▼

Yes, you need Python libraries such as anndata, pysam, and pyopenms installed for data inspection and summary generation. Additional dependencies include pandas, numpy, scanpy, pydeseq2, and skbio.

When do I need to run data triage on my omics datasets?▼

You need omics data triage before diving into complex downstream analysis to ensure data readiness and identify potential issues early. It summarizes key metrics, metadata, and structural properties of single-cell, alignment, and mass-spectrometry files.

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