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geniml

Train Region2Vec embeddings on genomic interval BED files.

8|Updated Nov 19, 2025
Search Tags:#embeddings#machine learning#bioinformatics#genomics#scATAC-seq#bed files
One-click install
npx skills add https://github.com/sanand0/scientific-research --skill geniml-sanand0
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geniml
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/geniml
Command: npx skills add https://github.com/sanand0/scientific-research --skill geniml-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines machine learning tasks involving genomic interval data (BED files), automating complex analyses that would otherwise require extensive custom scripting and bioinformatics expertise.

Core Features & Use Cases

  • Genomic Region Embeddings (Region2Vec): Learn unsupervised representations of genomic regions for similarity analysis and feature generation.
  • Joint Region/Metadata Embeddings (BEDspace): Train models that understand relationships between genomic regions and their associated metadata (e.g., cell types, experimental conditions).
  • Single-Cell Embeddings (scEmbed): Generate cell-level embeddings from scATAC-seq data for clustering and annotation.
  • Consensus Peak Calling: Build standardized reference peak sets from multiple experiments.
  • Use Case: Analyze single-cell ATAC-seq data to identify distinct cell populations by training scEmbed models on your peak-cell matrix, then use scanpy for visualization and clustering.

Quick Start

Use the geniml skill to train a Region2Vec model on BED files located in the 'bed_files/' directory, saving the output to 'model/'.

Frequently Asked Questions about geniml

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

FAQPage Schema
How do I apply machine learning to genomic interval data in BED files?▼

To apply machine learning to genomic interval data in BED files, you can use unsupervised embedding techniques like Region2Vec and BEDspace to learn numerical representations of genomic regions for downstream similarity analysis and clustering.

What is Region2Vec and how does it work for scATAC-seq analysis?▼

Region2Vec is an unsupervised embedding technique that learns numerical representations of genomic regions from BED files. For scATAC-seq analysis, it helps identify distinct cell populations by generating region-level features for clustering and annotation.

Can I use scanpy for clustering single-cell ATAC-seq data after generating embeddings?▼

Yes, you can use scanpy for clustering scATAC-seq data after generating cell-level embeddings with scEmbed. The geniml skill creates the peak-cell matrix embeddings, which you then visualize and cluster using scanpy.

How do I generate consensus peaks from multiple genomic experiments?▼

You can generate consensus peaks from multiple genomic experiments by using the consensus peak calling utility functions. This builds a standardized reference peak set from multiple BED files for consistent downstream analysis.

Do I need StarSpace to train BEDspace models for genomic region embeddings?▼

Yes, training BEDspace models for joint region and metadata embeddings may require the external StarSpace tool. The geniml skill facilitates this process, integrating metadata like cell types with genomic regions from BED files.

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