Train Region2Vec embeddings on genomic interval BED files.
npx skills add https://github.com/felixboehm/biochem-allergy --skill geniml-felixboehm
Please help me install this Agent Skill. Skill: geniml Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/geniml Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill geniml-felixboehm
This Skill streamlines machine learning tasks involving genomic interval data (BED files), simplifying complex analyses like region embeddings and single-cell data processing.
Use the geniml skill to train Region2Vec embeddings on BED files in the 'bed_files/' directory.
High-intent search queries and answers about installing and using this skill.
You can generate unsupervised embeddings for genomic regions in BED files using the Region2Vec method. It learns feature vectors from BED file intervals, enabling downstream classification tasks and pattern identification across collections of genomic regions.
Processing single-cell ATAC-seq data for cell clustering is handled using the scEmbed method. It generates unsupervised single-cell embeddings from scATAC-seq datasets to facilitate accurate cell clustering and subsequent cellular annotation.
Building a consensus peak set from multiple ChIP-seq experiments requires using the built-in consensus peak calling utilities. This constructs a robust reference peak set by aggregating genomic intervals across multiple BED files.
Yes, scanpy is supported as a required Python package for downstream analysis of scATAC-seq embeddings. The generated single-cell embeddings integrate with scanpy workflows for cell clustering and annotation tasks.
PyTorch is potentially required as a machine learning dependency to train models on genomic interval data. The skill uses it alongside the geniml and scanpy Python packages to support underlying deep learning operations for embedding generation.
yf8578Train Region2Vec embeddings on genomic interval BED files.
StarlitnightlyAutomate single-cell RNA-seq preprocessing with omicverse notebooks for QC, HVG detection, and embeddings.
SologaTrain Region2Vec embeddings on genomic interval data from BED files.
robinbarvaagTrain Region2Vec and BEDspace models on genomic interval data.
