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geniml

Train unsupervised genomic interval embeddings from BED files using Region2Vec and related workflows.

Updated Mar 30, 2026
Search Tags:#bioinformatics#genomics#unsupervised-learning#bed#genomic-intervals#region-embeddings#scatac
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill geniml-ya-way
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geniml
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/labclaw-geniml
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill geniml-ya-way

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Geniml provides tools for building machine learning models on genomic interval data from BED files, enabling unsupervised embeddings and region-level representations for downstream analyses such as scATAC-seq and multi-omics integration.

Core Features & Use Cases

  • Region2Vec: train genomic region embeddings to capture similarities between intervals and enable downstream clustering and search.
  • BEDspace: jointly embed regions and metadata labels for cross-modal queries and metadata-aware analyses.
  • scEmbed: generate cell-level embeddings from single-cell ATAC-seq data for clustering and annotation.
  • Consensus Peaks: build universes from BED collections to standardize tokenization and enable cross-study comparisons.
  • Utilities: provide caching, randomization, evaluation, and tokenization tools to support robust workflows.

Quick Start

Run geniml on a BED file collection to train region embeddings and produce scATAC-ready cell representations.

Frequently Asked Questions about geniml

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

FAQPage Schema
How do I generate unsupervised embeddings for genomic intervals from BED files?▼

To generate unsupervised embeddings for genomic intervals, you can use the Region2Vec workflow to train region-level representations from BED file collections. This process captures interval similarities to enable downstream clustering and search.

How do I create cell-level embeddings for scATAC-seq clustering and annotation?▼

You can create cell-level embeddings for scATAC-seq data using the scEmbed workflow. It generates unsupervised cell representations directly from single-cell chromatin accessibility data for downstream clustering and annotation tasks.

What is the best way to standardize genomic interval tokenization for cross-study comparisons?▼

The best way to standardize genomic interval tokenization is by building a consensus peak universe from BED collections. This creates a shared interval set that enables consistent cross-study comparisons and metadata-aware analyses.

Can I jointly embed genomic regions and metadata labels for cross-modal queries?▼

Yes, you can jointly embed genomic regions and metadata labels using the BEDspace workflow. This enables metadata-aware analyses and cross-modal queries by training joint representations of intervals and their associated labels.

Do I need Python to train region embeddings from bulk chromatin data?▼

Yes, you need Python and standard preprocessing to train region embeddings from bulk chromatin data. The workflows use YAML frontmatter to guide configuration for processing BED files and generating interval representations.

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