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---
lineage_type: import
upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/geniml/SKILL.md
upstream_sha: 9c9bd2e9
imported_at: 2026-06-26
upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/991bd993/skills/geniml/SKILL.md
upstream_sha: 991bd993
imported_at: 2026-08-08
prompt_class: unknown
upstream_changes: accepted
name: geniml
description: 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.
license: BSD-2-Clause license
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
description: "Use Geniml for audited local genomic-interval workflows: validate BED and universe contracts, plan Region2Vec or scEmbed runs, inspect model/tokenizer compatibility, and assess consensus universes."
license: MIT
compatibility: Requires Python 3.10+ and uv. Guidance targets geniml 0.8.4 with gtars 0.9.2; ML workflows need the pinned ml extra and compatible native wheels. Bundled planners and inspectors are dependency-free, local-only, and make no network requests.
allowed-tools: Read Write Edit Bash Glob
metadata:
version: "1.1"
skill-author: "K-Dense Inc."
upstream-version: "0.8.4"
last-reviewed: "2026-07-23"
---
# Geniml: Genomic Interval Machine Learning
# Geniml
## Overview
Use Geniml for machine learning and statistical workflows over genomic interval
sets. Treat coordinates, assemblies, token vocabularies, model artifacts, and
sample grouping as explicit contracts. The bundled scripts validate or plan;
they do not import Geniml, contact services, deserialize models, or execute
training.
Geniml is a Python package for building machine learning models on genomic interval data from BED files. It provides unsupervised methods for learning embeddings of genomic regions, single cells, and metadata labels, enabling similarity searches, clustering, and downstream ML tasks.
`Bash` is declared only for explicit, user-approved `uv`, Python, Geniml,
Gtars, Git, and native CLI commands shown in this guide; bundled Python helpers
do not spawn subprocesses. Example paths under `data/`, `refs/`, `work/`, and
`models/` are user-provided project placeholders, not missing bundled files.
## Installation
## Verified release snapshot
Install geniml using uv:
- Latest stable PyPI release on 2026-07-23: `geniml==0.8.4` (2026-01-14).
- PyPI does not declare `Requires-Python`; its classifiers list Python
3.10-3.14. Prefer Python 3.11 or 3.12 where all native/ML wheels resolve.
- `geniml==0.8.4` accepts `gtars>=0.2.5`; the verified base smoke used current
`gtars==0.9.2` (2026-06-17, Python >=3.10).
- Extras are `ml` and `test`. The base install omits Torch, Gensim, Scanpy,
Hugging Face Hub, pyBigWig, and HMM dependencies.
- Upstream documentation contains stale examples. Release source and installed
`--help` output take precedence where they conflict.
## Install reproducibly
Use a project environment and commit its generated lockfile:
```bash
uv pip install geniml
uv venv --python 3.12
uv pip install "geniml==0.8.4" "gtars==0.9.2"
```
For ML dependencies (PyTorch, etc.):
For Region2Vec, scEmbed, evaluation, or universe methods needing ML libraries:
```bash
uv pip install 'geniml[ml]'
uv pip install "geniml[ml]==0.8.4" "gtars==0.9.2"
```
Development version from GitHub:
For a durable project, prefer:
```bash
uv pip install git+https://github.com/databio/geniml.git
uv add "geniml[ml]==0.8.4" "gtars==0.9.2"
uv lock
```
## Core Capabilities
Do not install an unpinned Git branch. Record Python, OS/architecture, the
resolved lockfile, and the PyPI artifact digest. Geniml itself is BSD-2-Clause;
the `MIT` frontmatter value licenses this skill's content.
Geniml provides five primary capabilities, each detailed in dedicated reference files:
## Start with the safety gate
### 1. Region2Vec: Genomic Region Embeddings
Before importing Geniml or running an external binary:
Train unsupervised embeddings of genomic regions using word2vec-style learning.
1. Work only with explicit local regular files. Reject URLs, FIFOs, devices,
and symlinks unless the user deliberately changes that policy.
2. Validate BED structure and the declared assembly against a trusted local
chromosome-sizes file.
3. Bound file count, bytes, rows, workers, epochs, and output size.
4. Separate train/validation/test by patient, donor, biological replicate, or
other independent unit—not by BED row or cell alone.
5. Inventory and checksum the universe, tokenizer, model, config, inputs,
metadata manifest, and native binaries.
6. Obtain explicit approval before any BEDbase or Hugging Face download. Never
infer approval from a model ID or BEDbase identifier.
7. Keep logs aggregate and bounded. BED filenames, sample IDs, phenotypes,
labels, barcodes, and genomic intervals may be sensitive.
**Use for:** Dimensionality reduction of BED files, region similarity analysis, feature vectors for downstream ML.
## Coordinate and assembly contract
**Workflow:**
1. Tokenize BED files using a universe reference
2. Train Region2Vec model on tokens
3. Generate embeddings for regions
BED intervals are normally **0-based, half-open** `[start, end)`: start is
included, end is excluded, and length is `end - start`. Do not mix them with
1-based closed coordinates from VCF/GFF or user-facing genome browsers.
**Reference:** See `references/region2vec.md` for detailed workflow, parameters, and examples.
For every corpus and artifact, record:
### 2. BEDspace: Joint Region and Metadata Embeddings
- assembly and patch/accession where possible (for example GRCh38 versus
GRCh38.p14), plus the chromosome-sizes checksum;
- contig naming convention (`chr1` versus `1`), alt/random/decoy policy, and
mitochondrial naming;
- coordinate convention, sorting order, duplicate/overlap policy, and whether
BED strand is meaningful;
- liftover tool, chain digest, source/target assemblies, unmapped fraction, and
post-liftover validation.
Train shared embeddings for region sets and metadata labels using StarSpace.
Reject negative coordinates, `end <= start`, integer overflow, unknown
contigs, ends beyond contig length, malformed columns, mixed assemblies, and
silent contig renaming. Sorting and normalization never repair an assembly
mismatch. BED3 has no strand; when column 6 is present, preserve `+`, `-`, or
`.` unless the assay contract says otherwise.
**Use for:** Metadata-aware searches, cross-modal queries (region→label or label→region), joint analysis of genomic content and experimental conditions.
Run a bounded validation and normalization **plan** before analysis:
**Workflow:**
1. Preprocess regions and metadata
2. Train BEDspace model
3. Compute distances
4. Query across regions and labels
```bash
python skills/geniml/scripts/bed_validator.py \
--input data/peaks.bed \
--assembly GRCh38 \
--chrom-sizes refs/GRCh38.chrom.sizes
```
**Reference:** See `references/bedspace.md` for detailed workflow, search types, and examples.
The validator reports proposed actions but never rewrites the BED file.
### 3. scEmbed: Single-Cell Chromatin Accessibility Embeddings
## Current API map
Train Region2Vec models on single-cell ATAC-seq data for cell-level embeddings.
### Region and tokenizer I/O
**Use for:** scATAC-seq clustering, cell-type annotation, dimensionality reduction of single cells, integration with scanpy workflows.
**Workflow:**
1. Prepare AnnData with peak coordinates
2. Pre-tokenize cells
3. Train scEmbed model
4. Generate cell embeddings
5. Cluster and visualize with scanpy
**Reference:** See `references/scembed.md` for detailed workflow, parameters, and examples.
### 4. Consensus Peaks: Universe Building
Build reference peak sets (universes) from BED file collections using multiple statistical methods.
**Use for:** Creating tokenization references, standardizing regions across datasets, defining consensus features with statistical rigor.
**Workflow:**
1. Combine BED files
2. Generate coverage tracks
3. Build universe using CC, CCF, ML, or HMM method
**Methods:**
- **CC (Coverage Cutoff)**: Simple threshold-based
- **CCF (Coverage Cutoff Flexible)**: Confidence intervals for boundaries
- **ML (Maximum Likelihood)**: Probabilistic modeling of positions
- **HMM (Hidden Markov Model)**: Complex state modeling
**Reference:** See `references/consensus_peaks.md` for method comparison, parameters, and examples.
### 5. Utilities: Supporting Tools
Additional tools for caching, randomization, evaluation, and search.
**Available utilities:**
- **BBClient**: BED file caching for repeated access
- **BEDshift**: Randomization preserving genomic context
- **Evaluation**: Metrics for embedding quality (silhouette, Davies-Bouldin, etc.)
- **Tokenization**: Region tokenization utilities (hard, soft, universe-based)
- **Text2BedNN**: Neural search backends for genomic queries
**Reference:** See `references/utilities.md` for detailed usage of each utility.
## Common Workflows
### Basic Region Embedding Pipeline
Prefer Gtars for new interval/tokenizer code:
```python
from geniml.tokenization import hard_tokenization
from geniml.region2vec import region2vec
from geniml.evaluation import evaluate_embeddings
from gtars.models import Region, RegionSet
from gtars.tokenizers import Tokenizer
# Step 1: Tokenize BED files
hard_tokenization(
src_folder='bed_files/',
dst_folder='tokens/',
universe_file='universe.bed',
p_value_threshold=1e-9
)
# Step 2: Train Region2Vec
region2vec(
token_folder='tokens/',
save_dir='model/',
num_shufflings=1000,
embedding_dim=100
)
# Step 3: Evaluate
metrics = evaluate_embeddings(
embeddings_file='model/embeddings.npy',
labels_file='metadata.csv'
)
regions = RegionSet("data/peaks.bed")
tokenizer = Tokenizer.from_bed("refs/universe.bed")
encoded = tokenizer(regions)
input_ids = encoded["input_ids"]
```
### scATAC-seq Analysis Pipeline
`RegionSet` and `Tokenizer` also accept remote inputs in some constructors;
this skill permits local paths only unless network access is explicitly
approved. `geniml.io.RegionSet(regions, backed=False)` remains available as a
legacy Python implementation; backed sets are iterable but not indexable.
`geniml.io.Region` uses `stop`, while `gtars.models.Region` uses `end`.
With gtars 0.9.2, seven special tokens are added to a BED vocabulary. Therefore
`len(tokenizer)` is not simply the number of universe rows. Preserve universe
row order and the exact special-token map.
### Region2Vec
The modern class lives at a concrete module path:
```python
import scanpy as sc
from geniml.scembed import ScEmbed
from geniml.io import tokenize_cells
from geniml.region2vec.main import Region2VecExModel
from geniml.region2vec.utils import Region2VecDataset
from gtars.tokenizers import Tokenizer
# Step 1: Load data
adata = sc.read_h5ad('scatac_data.h5ad')
# Step 2: Tokenize cells
tokenize_cells(
adata='scatac_data.h5ad',
universe_file='universe.bed',
output='tokens.parquet'
)
# Step 3: Train scEmbed
model = ScEmbed(embedding_dim=100)
model.train(dataset='tokens.parquet', epochs=100)
# Step 4: Generate embeddings
embeddings = model.encode(adata)
adata.obsm['scembed_X'] = embeddings
# Step 5: Cluster with scanpy
sc.pp.neighbors(adata, use_rep='scembed_X')
sc.tl.leiden(adata)
sc.tl.umap(adata)
tokenizer = Tokenizer.from_bed("refs/universe.bed")
dataset = Region2VecDataset("work/tokens.parquet", shuffle=True)
model = Region2VecExModel(tokenizer=tokenizer, embedding_dim=100)
model.train(dataset, epochs=10, window_size=5, num_cpus=4, seed=42)
```
### Universe Building and Evaluation
The Parquet input must contain one list-valued `tokens` column, one document
per row. See [references/region2vec.md](references/region2vec.md) for export,
encoding, legacy CLI, and evaluation details.
### scEmbed
Import `ScEmbed` from `geniml.scembed.main`. AnnData `.var` must contain
`chr`, `start`, and `end`; rows are cells and nonzero features identify
accessible regions. Pre-tokenize to a Parquet `tokens` column and use the same
Tokenizer for training and inference. See
[references/scembed.md](references/scembed.md).
### BEDspace
BEDspace remains in 0.8.4 and invokes an external StarSpace executable.
StarSpace is archived and upstream Geniml does not pin a compatible revision.
Treat BEDspace as a legacy reproduction path, not the default for new systems.
See [references/bedspace.md](references/bedspace.md) for the exact stable CLI
spelling and an immutable, explicitly unverified build baseline.
### Consensus universes and assessment
The installed 0.8.4 CLI uses:
```text
geniml build-universe {cc,ccf,ml,hmm} ...
geniml assess-universe ...
geniml eval {gdst,npt,ctt,rct,bin-gen} ...
```
CC/CCF/ML/HMM consume precomputed coverage bigWigs. Do not concatenate or
generate coverage until all BED files pass the same assembly contract.
Assessment and embedding metrics are distinct: `assess-universe` measures fit
of a universe to interval collections, while `eval` implements CTT, RCT, GDST,
and NPT for embeddings. See
[references/consensus_peaks.md](references/consensus_peaks.md) and
[references/utilities.md](references/utilities.md).
## Important 0.8.4 migration notes
- The 0.7.0 changelog moved new RegionSet/tokenizer work toward Gtars.
- The 0.4.0 names `TreeTokenizer` and `AnnDataTokenizer` are historical; the
current Gtars API exposes `Tokenizer`.
- In the 0.8.4 wheel, `geniml.region2vec` and `geniml.scembed` do not re-export
their modern classes/functions. Use the concrete module paths above.
- `geniml tokenize` and `geniml region2vec` call names no longer exported by
their package `__init__` files; do not build new workflows around those CLI
paths without an installed-version smoke test.
- `geniml scembed` parses legacy MatrixMarket options but its command body is a
no-op in 0.8.4. Use `geniml.scembed.main.ScEmbed`.
- Official pages still show `geniml assess`; the release command is
`geniml assess-universe`.
- `.gtok` remains present in legacy datasets, but upstream issue #14 proposes
deprecating many-file `.gtok` workflows. Prefer one bounded Parquet corpus.
- Config key `embedding_size` is accepted only for backward compatibility;
use `embedding_dim`.
## Model and universe compatibility
A Region2Vec/scEmbed inference bundle is valid only when these agree:
- model `config.yaml` `vocab_size` and `embedding_dim`;
- exact `universe.bed` bytes/order and assembly;
- tokenizer implementation/version and special-token IDs;
- checkpoint tensor shapes and pooling policy;
- Geniml/Gtars versions and any tokenization parameters.
Geniml 0.8.4 defaults to `checkpoint.pt`, `config.yaml`, and `universe.bed`.
Its loader uses `torch.load(..., weights_only=True)`, but `.pt`, Gensim
`.model`, pickle, joblib, and native binaries remain untrusted inputs. Inspect
and checksum artifacts before loading; use an isolated environment and never
load a checkpoint merely to discover its metadata.
```bash
# Generate coverage
cat bed_files/*.bed > combined.bed
uniwig -m 25 combined.bed chrom.sizes coverage/
python skills/geniml/scripts/model_artifact_inspector.py \
--model-dir models/region2vec
# Build universe with coverage cutoff
geniml universe build cc \
--coverage-folder coverage/ \
--output-file universe.bed \
--cutoff 5 \
--merge 100 \
--filter-size 50
# Evaluate universe quality
geniml universe evaluate \
--universe universe.bed \
--coverage-folder coverage/ \
--bed-folder bed_files/
python skills/geniml/scripts/tokenizer_compatibility.py \
--model-dir models/region2vec \
--universe refs/universe.bed \
--assembly GRCh38
```
## CLI Reference
`Region2VecExModel(model_path="org/repo")`, `ScEmbed(model_path="org/repo")`,
and Gtars `Tokenizer.from_pretrained(...)` can download from Hugging Face.
Local `from_pretrained("models/local")` loads a local bundle. Pin Hub revision
and expected hashes when a user approves download; then work offline from the
verified cache.
Geniml provides command-line interfaces for major operations:
## BEDbase downloads and caches
`BBClient.load_bed`, `load_bedset`, and token-cache operations may contact
`https://api.bedbase.org`. The default cache is
`$BBCLIENT_CACHE` or `~/.bbcache`; `BEDBASE_API` changes the endpoint. Do not
read unrelated environment variables. Set an explicit project cache, estimate
size, approve identifiers/endpoints, and verify returned checksums before use.
Local inspection commands are safer:
```text
geniml bbclient seek ID --cache-folder /absolute/project/cache
geniml bbclient inspect-bedfiles --cache-folder /absolute/project/cache
geniml bbclient inspect-bedsets --cache-folder /absolute/project/cache
```
The `cache-bed`, `cache-bedset`, and `cache-tokens` subcommands may use the
network. Do not run them implicitly or include sensitive local BED files in an
upload/cache workflow.
## Local audit and planning CLIs
All scripts are standard-library-only and default to redacted JSON:
```bash
# Region2Vec training
geniml region2vec --token-folder tokens/ --save-dir model/ --num-shuffle 1000
# Audit manifest paths, checksums, assemblies, and patient/donor leakage
python skills/geniml/scripts/corpus_auditor.py \
--manifest data/manifest.tsv --assembly-column assembly \
--group-column patient_id --split-column split
# BEDspace preprocessing
geniml bedspace preprocess --input regions/ --metadata labels.csv --universe universe.bed
# Plan tokenizer/model compatibility checks
python skills/geniml/scripts/tokenizer_compatibility.py \
--model-dir models/r2v --universe refs/universe.bed --assembly GRCh38
# BEDspace training
geniml bedspace train --input preprocessed.txt --output model/ --dim 100
# Plan consensus construction; does not execute Geniml or coverage tools
python skills/geniml/scripts/consensus_plan.py \
--manifest data/manifest.tsv --chrom-sizes refs/GRCh38.chrom.sizes \
--assembly GRCh38 --method cc --output-dir work/consensus
# BEDspace search
geniml bedspace search -t r2l -d distances.pkl -q query.bed -n 10
# Universe building
geniml universe build cc --coverage-folder coverage/ --output universe.bed --cutoff 5
# BEDshift randomization
geniml bedshift --input peaks.bed --genome hg38 --preserve-chrom --iterations 100
# Plan an embedding run; does not import ML libraries
python skills/geniml/scripts/embedding_plan.py \
--mode region2vec --data work/tokens.parquet \
--universe refs/universe.bed --output-dir work/r2v \
--assembly GRCh38
```
## When to Use Which Tool
Use `--help` for resource limits and explicit path-disclosure controls.
**Use Region2Vec when:**
- Working with bulk genomic data (ChIP-seq, ATAC-seq, etc.)
- Need unsupervised embeddings without metadata
- Comparing region sets across experiments
- Building features for downstream supervised learning
## References
**Use BEDspace when:**
- Metadata labels available (cell types, tissues, conditions)
- Need to query regions by metadata or vice versa
- Want joint embedding space for regions and labels
- Building searchable genomic databases
**Use scEmbed when:**
- Analyzing single-cell ATAC-seq data
- Clustering cells by chromatin accessibility
- Annotating cell types from scATAC-seq
- Integration with scanpy is desired
**Use Universe Building when:**
- Need reference peak sets for tokenization
- Combining multiple experiments into consensus
- Want statistically rigorous region definitions
- Building standard references for a project
**Use Utilities when:**
- Need to cache remote BED files (BBClient)
- Generating null models for statistics (BEDshift)
- Evaluating embedding quality (Evaluation)
- Building search interfaces (Text2BedNN)
## Best Practices
### General Guidelines
- **Universe quality is critical**: Invest time in building comprehensive, well-constructed universes
- **Tokenization validation**: Check coverage (>80% ideal) before training
- **Parameter tuning**: Experiment with embedding dimensions, learning rates, and training epochs
- **Evaluation**: Always validate embeddings with multiple metrics and visualizations
- **Documentation**: Record parameters and random seeds for reproducibility
### Performance Considerations
- **Pre-tokenization**: For scEmbed, always pre-tokenize cells for faster training
- **Memory management**: Large datasets may require batch processing or downsampling
- **Computational resources**: ML/HMM universe methods are computationally intensive
- **Model caching**: Use BBClient to avoid repeated downloads
### Integration Patterns
- **With scanpy**: scEmbed embeddings integrate seamlessly as `adata.obsm` entries
- **With BEDbase**: Use BBClient for accessing remote BED repositories
- **With Hugging Face**: Export trained models for sharing and reproducibility
- **With R**: Use reticulate for R integration (see utilities reference)
## Related Projects
Geniml is part of the BEDbase ecosystem:
- **BEDbase**: Unified platform for genomic regions
- **BEDboss**: Processing pipeline for BED files
- **Gtars**: Genomic tools and utilities
- **BBClient**: Client for BEDbase repositories
## Additional Resources
- **Documentation**: https://docs.bedbase.org/geniml/
- **GitHub**: https://github.com/databio/geniml
- **Pre-trained models**: Available on Hugging Face (databio organization)
- **Publications**: Cited in documentation for methodological details
## Troubleshooting
**"Tokenization coverage too low":**
- Check universe quality and completeness
- Adjust p-value threshold (try 1e-6 instead of 1e-9)
- Ensure universe matches genome assembly
**"Training not converging":**
- Adjust learning rate (try 0.01-0.05 range)
- Increase training epochs
- Check data quality and preprocessing
**"Out of memory errors":**
- Reduce batch size for scEmbed
- Process data in chunks
- Use pre-tokenization for single-cell data
**"StarSpace not found" (BEDspace):**
- Install StarSpace separately: https://github.com/facebookresearch/StarSpace
- Set `--path-to-starspace` parameter correctly
For detailed troubleshooting and method-specific issues, consult the appropriate reference file.
- [Region2Vec](references/region2vec.md): modern API, artifacts, CLI drift,
training, encoding, and evaluation.
- [scEmbed](references/scembed.md): AnnData/token preparation, training,
inference, annotation, privacy, and leakage.
- [BEDspace](references/bedspace.md): metadata schema, exact legacy CLI,
StarSpace status, artifacts, and retrieval.
- [Consensus peaks](references/consensus_peaks.md): coverage prerequisites,
CC/CCF/ML/HMM, assessment, and assembly safeguards.
- [Utilities](references/utilities.md): I/O, Gtars tokenizers, BBClient,
evaluation, model safety, migration, and dated sources.
Source snapshot and primary-paper links are dated in
[references/utilities.md](references/utilities.md). Re-check release metadata
and installed signatures before changing the pinned versions.