How does Geniml handle single-cell ATAC-seq data?

It uses the scEmbed capability to train Region2Vec models on single-cell chromatin accessibility data, generating cell-level embeddings that can be used for clustering and cell-type annotation.

What are 'universes' in genomic machine learning?

Universes are reference peak sets or consensus regions built from multiple BED files. They act as a standardized coordinate system for tokenizing genomic data before training ML models.

What is Geniml in the context of Claude Code?

Geniml is a Python library and Claude Code skill that provides unsupervised machine learning methods for genomic interval data, such as BED files, allowing AI to assist in complex biological data analysis.

Can I perform cross-modal searches with this skill?

Yes, using the BEDspace module, you can train joint embeddings for both genomic regions and metadata labels, enabling queries like finding regions associated with specific experimental conditions.

Geniml: Genomic Machine Learning

Name: Geniml: Genomic Machine Learning
Author: jimmc414

byjimmc414

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324

Ciencia de Datos y ML

Applies unsupervised machine learning models to genomic interval data for region embeddings, single-cell analysis, and similarity searches.

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Geniml is a specialized Claude Code skill designed for bioinformaticians and researchers to perform advanced machine learning on genomic interval data (BED files). It streamlines complex workflows like training Region2Vec for region embeddings, scEmbed for single-cell ATAC-seq analysis, and building consensus peak sets (universes). By integrating Geniml, users can automate the generation of embeddings for similarity analysis, cross-modal metadata queries, and downstream genomic feature learning directly within their AI-driven scientific discovery environment, bridging the gap between raw genomic data and actionable machine learning insights.

Características Principales

Region2Vec for unsupervised genomic region embeddings and dimensionality reduction
scEmbed for cell-level embeddings and clustering of single-cell ATAC-seq data
Automated 'universe' building for creating statistically rigorous consensus peak sets
BEDspace for joint region and metadata embeddings to enable cross-modal queries
Integration with the scanpy ecosystem and BEDbase for streamlined bioinformatics workflows
324 GitHub stars

Casos de Uso

Building searchable genomic databases using joint region-metadata embeddings for experimental discovery
Standardizing heterogeneous BED file collections into consensus references for tokenization
Dimensionality reduction and clustering of single-cell chromatin accessibility datasets

Acerca de

Características Principales

Region2Vec for unsupervised genomic region embeddings and dimensionality reduction
scEmbed for cell-level embeddings and clustering of single-cell ATAC-seq data
Automated 'universe' building for creating statistically rigorous consensus peak sets
BEDspace for joint region and metadata embeddings to enable cross-modal queries
Integration with the scanpy ecosystem and BEDbase for streamlined bioinformatics workflows
324 GitHub stars

Casos de Uso

Building searchable genomic databases using joint region-metadata embeddings for experimental discovery
Standardizing heterogeneous BED file collections into consensus references for tokenization
Dimensionality reduction and clustering of single-cell chromatin accessibility datasets