Agent skill · research science · aiskillstore
geniml
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.
Why this skill is useful
Adds executable scripts for training genomic region embeddings and consensus peak building that are not commonly found in public documentation.
What it needs
Requires geniml, pytorch installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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: For ML dependencies (PyTorch, etc.): Development version from GitHub: 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. Workflow: 1. Prepare AnnData with peak coordinates 2. Pre-tokenize cells 3. Train scEmbed model 4. Generate cell embeddings 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/geniml--d241d1