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bioinformatics

Run Scanpy single-cell RNA-seq pipelines and pathway enrichment analyses.

31|8|Updated Apr 10, 2026
Search Tags:#pathway enrichment#differential expression#biopython#scanpy#single-cell rna-seq#sequence alignment#bioinformatics pipeline
One-click install
npx skills add https://github.com/itallstartedwithaidea/agent-skills --skill bioinformatics-itallstartedwithaidea
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bioinformatics
Source: https://github.com/itallstartedwithaidea/agent-skills/tree/main/skills/scientific-research/bioinformatics
Command: npx skills add https://github.com/itallstartedwithaidea/agent-skills --skill bioinformatics-itallstartedwithaidea

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, numpy, pandas, gprofiler-official, biopython, and includes assets (resource) components.

What problem does it solve?

Bioinformatics automates end-to-end computational biology workflows so you can transform raw sequencing and single-cell gene expression data into reproducible results and biological interpretations.

Core Features & Use Cases

  • Single-cell RNA-seq pipeline with Scanpy: Quality control, normalization, dimensionality reduction (PCA/UMAP), clustering (Leiden), and differential expression.
  • Sequence and alignment analysis with BioPython: FASTA parsing and pairwise sequence alignment for comparative sequence statistics.
  • Gene regulatory network and pathway enrichment analysis: Infers biological mechanisms via pathway enrichment (e.g., GO/KEGG/REACTOME sources) using gene lists.

Example: Analyze an h5ad single-cell RNA-seq dataset to identify marker genes by cluster and then enrich those genes to interpret the underlying pathways.

Quick Start

Use the bioinformatics skill to run a Scanpy single-cell RNA-seq pipeline on your h5ad file and produce clustered differential expression results plus pathway enrichment for the resulting gene list.

Frequently Asked Questions about bioinformatics

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

FAQPage Schema
How do I run a single-cell RNA-seq pipeline on an h5ad file?▼

To run a single-cell RNA-seq pipeline on an h5ad file, use Scanpy for quality control, normalization, dimensionality reduction, clustering, and differential expression.

What is pathway enrichment for GO, KEGG, and Reactome?▼

Pathway enrichment for GO, KEGG, and Reactome is the process of inferring biological mechanisms from a gene list by identifying statistically overrepresented pathways.

Can I use BioPython for FASTA parsing and pairwise sequence alignment?▼

Yes, you can use BioPython for FASTA parsing and pairwise sequence alignment to perform comparative sequence statistics and structural analysis.

Does this bioinformatics pipeline support dimensionality reduction and Leiden clustering?▼

Yes, this bioinformatics pipeline supports dimensionality reduction using PCA and UMAP, alongside Leiden clustering to identify cellular subpopulations.

What's the best way to identify marker genes by cluster in scRNA-seq data?▼

The best way to identify marker genes by cluster in scRNA-seq data is applying Scanpy-based preprocessing, Leiden clustering, and differential expression analysis.

Do I need numpy and pandas to perform computational biology workflows?▼

Yes, you need numpy and pandas alongside scanpy, biopython, and gprofiler-official to ensure reproducible pipeline execution for computational biology workflows.

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