# Geniml

Should be used when working with genomic interval data (BED files) for machine learning...

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Design
- License: BSD-2-Clause license
- Last updated: 2026-07-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill geniml -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill geniml -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill geniml -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill geniml -g -a gemini-cli -y`

## Description

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Geniml: Genomic Interval Machine Learning

## Overview

Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.

## Installation

Install geniml using uv:

```bash
uv uv pip install geniml
```

For ML dependencies (PyTorch, etc.):

```bash
uv uv pip install 'geniml[ml]'
```

Development version from GitHub:

```bash
uv uv pip install git+https://github.com/databio/geniml.git
```

## Core Capabilities

Geniml provides five primary capabilities, each detailed in dedicated reference files:

### 1. Region2Vec: Genomic Region Embeddings

Train unsupervised embeddings of genomic regions using word2vec-style learning.

**Use for:** Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.

**Workflow:**
1. Tokenize BED files using a universe reference
2. Train Region2Vec model on tokens
3. Generate embeddings for regions

**Reference:** See `references/region2vec.md` for detailed workflow, parameters, and examples.

### 2. BEDspace: Joint Region and Metadata Embeddings

Train shared embeddings for region sets and metadata labels using StarSpace.

**Use for:** Metadata-aware searches, cross-modal queries (region→label or label→region), joint analysis of genomic content and experimental conditions.

**Workflow:**
1. Preprocess regions and metadata
2. Train BEDspace model
3. Compute distances
4. Query across regions and labels

**Reference:** See `references/bedspace.md` for detailed workflow, search types, and examples.

### 3. scEmbed: Single-Cell Chromatin Accessibility Embeddings

Train Region2Vec models on single-cell ATAC-seq data for cell-level embeddings.

**Use for:** scATAC-seq clustering, cell-type annotation, dimensionality reduction of single cells, integration with scanpy workflows.

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/05-生物信息与基因组学/geniml/SKILL.md