geniml

Process genomic interval BED files for machine learning tasks.

3|Updated Apr 17, 2026

Search Tags:#machine learning #genomic analysis #BED files #single-cell ATAC-seq #genomic interval data

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill geniml-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: geniml
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/geniml
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill geniml-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, scikit-learn, pybedtools, databio, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of working with genomic interval data, enabling users to perform complex machine learning tasks like embeddings, clustering, and analysis with ease.

Core Features & Use Cases

Genomic Interval Data Processing: Handle BED files and other genomic interval data formats.
Machine Learning Capabilities: Train embeddings, perform clustering, and build consensus peaks.
Use Case: If you have a collection of BED files and you want to train embeddings for region-based genomic feature learning, this Skill can help you achieve that.

Quick Start

Train embeddings for genomic regions using the geniml skill with the following command: uv uv pip install geniml[ml]

Frequently Asked Questions about geniml

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

FAQPage Schema

How do I train embeddings for genomic interval data from BED files?▼

Train embeddings for genomic interval data by processing BED files to learn region-based genomic features. This skill applies machine learning to BED file collections, supporting training embeddings, clustering, and consensus peak building for chromatin accessibility analysis.

Can I perform clustering on single-cell ATAC-seq datasets using BED files?▼

Clustering on single-cell ATAC-seq datasets is supported by processing BED file genomic interval data. The skill applies scikit-learn and scipy machine learning libraries to chromatin accessibility datasets, enabling clustering and consensus peak building tasks.

What Python dependencies are required for genomic interval machine learning analysis?▼

Genomic interval machine learning analysis requires numpy, scipy, scikit-learn, pybedtools, and databio. These Python libraries support processing BED files, training embeddings, and performing clustering for single-cell ATAC-seq and chromatin accessibility datasets.

How do I build consensus peaks from genomic interval data for machine learning?▼

Build consensus peaks from genomic interval data by processing BED files through the skill's machine learning capabilities. This consensus peak building supports downstream single-cell ATAC-seq analysis and chromatin accessibility dataset workflows.

Does pybedtools work with scikit-learn for chromatin accessibility dataset analysis?▼

Pybedtools works with scikit-learn for chromatin accessibility dataset analysis by processing genomic interval BED files. The skill integrates pybedtools for genomic interval manipulation and scikit-learn for training embeddings and clustering tasks.

What is the best way to process BED files for region-based genomic feature learning?▼

The best way to process BED files for region-based genomic feature learning is using machine learning to train embeddings on genomic interval data. This approach supports single-cell ATAC-seq analysis and chromatin accessibility datasets.