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Imad-Oute
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geniml

Generate region and joint embeddings from genomic interval data.

Updated May 10, 2026
Search Tags:#embeddings#machine learning#bioinformatics#genomics#bed files#universe building
One-click install
npx skills add https://github.com/Imad-Oute/ResearchForge --skill geniml-imad-oute
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geniml
Source: https://github.com/Imad-Oute/ResearchForge/tree/main/OpenSource-Projects/claude-scientific-skills/scientific-skills/geniml
Command: npx skills add https://github.com/Imad-Oute/ResearchForge --skill geniml-imad-oute

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill enables automated analysis and modeling of genomic interval data, simplifying the process of extracting meaningful features from large-scale genomic datasets.

Core Features & Use Cases

  • Region Embeddings: Generate vector representations of genomic regions for similarity analysis and clustering.
  • Joint Embeddings: Create shared spaces for regions and metadata labels, facilitating cross-modal searches.
  • Single-Cell Analysis: Embed single-cell ATAC-seq data for cell-type annotation and clustering.
  • Universe Building: Construct consensus peak sets from multiple BED files for standardized analysis.
  • Utilities: Support tools for caching, randomization, and evaluation, streamlining genomic workflows. For example, a researcher can build a universe of accessible chromatin regions, tokenize datasets, train embeddings, and visualize cell clusters—all within an integrated pipeline.

Quick Start

Tokenize BED files with a universe reference, train a Region2Vec model, and evaluate embeddings using the command-line tools.

Frequently Asked Questions about geniml

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

FAQPage Schema
How do I generate vector embeddings for genomic regions from BED files?▼

You can generate genomic region embeddings by tokenizing BED files with a universe reference and training a Region2Vec model. This produces vector representations of genomic intervals for similarity analysis and clustering.

What is universe building for genomic interval data and when is it needed?▼

Universe building constructs a consensus peak set from multiple BED files to standardize analysis. It is needed when preparing accessible chromatin regions for consistent tokenization and reproducible genomic workflows.

Can I use this for single-cell ATAC-seq clustering and cell-type annotation?▼

Yes, single-cell ATAC-seq analysis is supported. You can embed single-cell genomic interval data to perform cell-type annotation and visualize cell clusters within an integrated pipeline.

Do I need numpy and scipy to process genomic interval data with this tool?▼

Yes, numpy and scipy are required dependencies for processing genomic interval data. Full operation also requires external bioinformatics utilities to support the embedding and universe building workflows.

What's the best way to create joint embeddings for genomic regions and metadata labels?▼

The tool creates joint embeddings by mapping genomic regions and metadata labels into a shared vector space. This facilitates cross-modal searches and integrates genomic features with phenotypic metadata.

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