Geniml
Skill Verifiziert AktivPart of the AlterLab Academic Skills suite. 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.
To enable researchers to perform advanced machine learning tasks on genomic interval data by providing specialized tools for embedding generation, analysis, and interpretation.
Funktionen
- Train region embeddings (Region2Vec)
- Joint region and metadata embeddings (BEDspace)
- Single-cell ATAC-seq embedding (scEmbed)
- Build consensus peak universes
- Genomic region tokenization utilities
- Data caching and randomization tools
Anwendungsfälle
- Training embeddings for genomic regions for ML tasks
- Analyzing scATAC-seq data for cell-type clustering
- Building statistically rigorous reference peak sets (universes)
- Performing metadata-aware similarity searches on genomic regions
Nicht-Ziele
- General-purpose bioinformatics pipeline
- Analysis of non-genomic interval data
- Direct biological interpretation without ML models
Workflow
- Prepare genomic data (BED files, AnnData)
- Tokenize regions using a reference universe
- Train embedding models (Region2Vec, BEDspace, scEmbed)
- Generate embeddings for analysis
- Perform downstream tasks (clustering, similarity search, visualization)
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
Geniml
99This 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.
Gtars
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Pysam
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