Train unsupervised genomic region embeddings from BED files with geniml.
npx skills add https://github.com/Yezez9/Research-Agent --skill geniml-yezez9
Please help me install this Agent Skill. Skill: geniml Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/geniml Command: npx skills add https://github.com/Yezez9/Research-Agent --skill geniml-yezez9
This Skill simplifies complex machine learning tasks on genomic interval data (BED files), enabling advanced analysis of chromatin accessibility, gene regulation, and more.
Use the geniml skill to train Region2Vec embeddings on BED files located in the 'bed_files/' directory.
High-intent search queries and answers about installing and using this skill.
You can train unsupervised embeddings for genomic regions from BED files using Region2Vec or BEDspace algorithms. This generates numerical vector representations capturing genomic interval similarities for downstream machine learning tasks.
Yes, scATAC-seq cell clustering is supported using the scEmbed method. It trains embeddings on single-cell chromatin accessibility datasets to identify distinct cell populations based on their accessibility patterns.
Building a consensus peak set from multiple datasets creates a standardized reference peak set. This constructs a unified genomic interval coordinate system for consistent cross-dataset chromatin accessibility comparisons.
Yes, this approach works directly with genomic interval data formatted as BED files. It processes these raw genomic coordinates to learn region similarities and single-cell feature representations without requiring pre-converted formats.
Learning embeddings for single-cell chromatin accessibility requires sufficient genomic interval coverage in BED files. Unsupervised feature learning on sparse scATAC-seq datasets may produce noisy cell clustering if peak overlap is minimal.
ya-wayTrain unsupervised genomic interval embeddings from BED files using Region2Vec and related workflows.
hxk622Train genomic region embeddings and analyze single-cell ATAC-seq data with Python.
exon-researchRetrieve pathway members, cell-type markers, and genomic interval overlaps from biological databases.
inflexa-aiStandardize ATAC-seq, ChIP-seq, and CUT&Tag analysis with peak calling and differential binding.
