claw-metagenomics

Analyze metagenomic FASTQ data for taxonomy, resistome, and pathway profiles.

Updated May 10, 2026

Search Tags:#bioinformatics #taxonomy #reproducibility #metagenomics #resistome #functional profiling

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill claw-metagenomics-mubashermohammed

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: claw-metagenomics
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/claw-metagenomics
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill claw-metagenomics-mubashermohammed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, scipy, biopython, kraken2, bracken, rgi, humann, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive solution for analyzing metagenomic data, offering taxonomy classification, antimicrobial resistance gene detection, and functional pathway profiling.

Core Features & Use Cases

Shotgun Metagenomics Analysis: Combines taxonomic classification, antimicrobial resistance gene detection, and functional pathway profiling.
Taxonomy Profiling: Uses Kraken2 for classification and Bracken for read re-estimation.
Resistome Profiling: Detects antimicrobial resistance genes with RGI and classifies them by WHO priority.
Functional Pathway Profiling: Optionally runs HUMAnN3 for pathway-level functional context.
Reproducibility: Produces a full reproducibility bundle for every run.
Use Case: For a researcher analyzing environmental samples, this Skill can automatically process and analyze metagenomic data, generating reports and figures for publication.

Quick Start

Run the metagenomics_profiler.py script with the forward and reverse reads for your sample.

Frequently Asked Questions about claw-metagenomics

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

FAQPage Schema

How do I analyze metagenomic data for taxonomy, resistome, and functional pathways?▼

Metagenomic data analysis for taxonomy, resistome, and functional pathways is performed by running the metagenomics_profiler.py script on paired-end or concatenated FASTQ files to generate comprehensive profiles. This pipeline uses Kraken2, Bracken, RGI, and HUMAnN3 to produce publication-ready reports and figures.

What is the best way to detect antimicrobial resistance genes in shotgun metagenomics samples?▼

Detecting antimicrobial resistance genes in shotgun metagenomics samples is best achieved using RGI (Resistance Gene Identifier), which identifies resistome profiles and classifies detected genes by WHO priority. This pipeline integrates RGI directly to process FASTQ files and generate standardized resistance reports.

Can I use Kraken2 and Bracken for taxonomic classification of paired-end FASTQ files?▼

Yes, you can use Kraken2 and Bracken for taxonomic classification of paired-end FASTQ files within this metagenomics pipeline. Kraken2 handles the initial read classification, while Bracken performs read re-estimation to provide accurate taxonomic abundance profiles.

Do I need HUMAnN3 installed for functional pathway profiling of environmental metagenomes?▼

Yes, HUMAnN3 is required for functional pathway profiling of environmental metagenomes. The pipeline optionally runs HUMAnN3 to map reads to pathway-level functional context, providing deeper insights into the metabolic and functional potential of your samples.

How does this pipeline ensure reproducibility for clinical metagenomics analysis?▼

Reproducibility for clinical metagenomics analysis is ensured by generating a full reproducibility bundle for every run. This bundle captures the exact parameters, software versions, and reference data used, allowing you to transparently replicate your taxonomic and functional profiling results.

Are there limitations when processing concatenated FASTQ files instead of paired-end reads?▼

Processing concatenated FASTQ files instead of paired-end reads does not limit the core taxonomic and resistome profiling capabilities, but paired-end reads are standard for HUMAnN3 functional pathway analysis. The pipeline accepts both formats to accommodate different sequencing outputs.