Train Region2Vec embeddings on genomic interval data from BED files.
npx skills add https://github.com/jimmc414/Kosmos --skill geniml-jimmc414
Please help me install this Agent Skill. Skill: geniml Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/geniml Command: npx skills add https://github.com/jimmc414/Kosmos --skill geniml-jimmc414
This Skill streamlines machine learning tasks involving genomic interval data (BED files), enabling advanced analysis of DNA accessibility, region embeddings, and single-cell data.
Use the geniml skill to train a Region2Vec model on your tokenized BED files.
High-intent search queries and answers about installing and using this skill.
You can generate embeddings for genomic regions from BED files by tokenizing the intervals and training an unsupervised Region2Vec model to capture latent genomic features and relationships.
Yes, you can analyze scATAC-seq data for cell clustering by training scEmbed models to generate embeddings and visualizing distinct cell populations using scanpy.
Yes, you need PyTorch for underlying machine learning dependencies and the external StarSpace tool to run BEDspace for creating joint embeddings of genomic regions and metadata labels.
The best way to build consensus peaks from multiple datasets is using the toolkit's consensus peak building utilities, which generate a standardized reference peak set from multiple BED files.
Yes, the toolkit provides utilities for BED file manipulation and randomization, allowing you to process, randomize, and evaluate genomic interval data for downstream analysis.
Joint embedding works by mapping genomic intervals and associated metadata labels into a shared embedding space using BEDspace, enabling simultaneous analysis of regions and their biological annotations.
SologaTrain Region2Vec embeddings on genomic interval data from BED files.
ya-wayIdentify cluster-specific marker genes and annotate cell types in single-cell RNA-seq data.
felixboehmTrain Region2Vec embeddings on genomic interval BED files.
galeepGenerate unsupervised embeddings for genomic regions from interval data.
