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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill geniml --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 pip install geniml ``` For ML dependencies (PyTorch, etc.): ```bash uv pip install 'geniml[ml]' ``` Development version from GitHub: ```bash 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
- Overview
- Installation
- Core Capabilities
- 1. Region2Vec: Genomic Region Embeddings
- 2. BEDspace: Joint Region and Metadata Embeddings
- 3. scEmbed: Single-Cell Chromatin Accessibility Embeddings
- 4. Consensus Peaks: Universe Building
- 5. Utilities: Supporting Tools
- Common Workflows
- Basic Region Embedding Pipeline
- scATAC-seq Analysis Pipeline
- Universe Building and Evaluation
- CLI Reference
- When to Use Which Tool
uv pip install geniml uv pip install 'geniml[ml]' uv pip install git+https://github.com/databio/geniml.git Generate coverage cat bed_files/*.bed > combined.bed uniwig -m 25 combined.bed chrom.sizes coverage/ Build universe with coverage cutoff geniml universe build cc \ Evaluate universe quality geniml universe evaluate \
What does the geniml skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill geniml --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.