k-dense-ai/scientific-agent-skills/geniml

Geniml

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.

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.

Verified release snapshot

• Latest stable PyPI release on 2026-10-01: geniml==0.8.4 (2026-01-14).
• PyPI does not declare Requires-Python; its classifiers list Python

3.10-3.14. The current recipes below were tested on Python 3.12.

• geniml==0.8.4 accepts gtars>=0.2.5; the verified base smoke used current

gtars==0.10.0 (2026-09-05, 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 separate project environment. The tested CPU stack uses Python 3.12:

uv venv --python 3.12
uv pip install "geniml==0.8.4" "gtars==0.10.0"

For the Region2Vec/scEmbed recipes tested here, add only their required libraries:

uv pip install "torch==2.14.1" "gensim==4.4.0" "huggingface-hub==2.0.0" \
  "scanpy==1.12.4" "anndata==0.12.19" "zarr==2.18.7"

For the consensus recipes, also install pyBigWig==0.3.26 and hmmlearn==0.3.3. For a durable project, use the same requirements with uv add and retain uv.lock.

Geniml requires Zarr <3. AnnData 0.13 requires Zarr >=3, so current AnnData cannot share this environment. Keep AnnData 0.12.19 here; transfer H5AD files between separate environments when newer AnnData features are needed. Never force an incompatible installation with --no-deps.

The full geniml[ml]==0.8.4 extra includes additional, older pinned components. Its resolution was checked with anndata==0.12.19 and gtars==0.10.0, but that full stack was not executed; it selected Scanpy 1.11.5 and Transformers 4.57.6. It is unnecessary for the workflows above.

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.

Start with the safety gate

Before importing Geniml or running an external binary:

1. Work only with explicit local regular files. Reject URLs, FIFOs, devices,

and symlinks unless the user deliberately changes that policy.

1. Validate BED structure and the declared assembly against a trusted local

chromosome-sizes file.

1. Bound file count, bytes, rows, workers, epochs, and output size.
2. Separate train/validation/test by patient, donor, biological replicate, or

other independent unit—not by BED row or cell alone.

1. Inventory and checksum the universe, tokenizer, model, config, inputs,

metadata manifest, and native binaries.

1. Obtain explicit approval before any BEDbase or Hugging Face download. Never

infer approval from a model ID or BEDbase identifier.

1. Keep logs aggregate and bounded. BED filenames, sample IDs, phenotypes,

labels, barcodes, and genomic intervals may be sensitive.

Coordinate and assembly contract

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.

For every corpus and artifact, record:

• 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.

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.

Run a bounded validation and normalization plan before analysis:

python skills/geniml/scripts/bed_validator.py \
  --input data/peaks.bed \
  --assembly GRCh38 \
  --chrom-sizes refs/GRCh38.chrom.sizes

The validator reports proposed actions but never rewrites the BED file.

Current API map

Region and tokenizer I/O

Prefer Gtars for new interval/tokenizer code:

from gtars.models import Region, RegionSet
from gtars.tokenizers import Tokenizer
regions = RegionSet("data/peaks.bed")
tokenizer = Tokenizer.from_bed("refs/universe.bed")
encoded = tokenizer(regions)
input_ids = encoded["input_ids"]

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 0.10.0, 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:

from geniml.region2vec.main import Region2VecExModel
from geniml.region2vec.utils import Region2VecDataset
from gtars.tokenizers import Tokenizer
tokenizer = Tokenizer.from_bed("refs/universe.bed")
dataset = Region2VecDataset("work/tokens.parquet", shuffle=True, convert_to_str=True)
model = Region2VecExModel(tokenizer=tokenizer, embedding_dim=100)
model.train(dataset, epochs=10, window_size=5, num_cpus=4, seed=42)

The Parquet input must contain one list-valued tokens column, one document per row. Record token frequencies and min_count: in the 0.8.4 training implementation, only Gensim-retained token IDs receive trained weights. Universe membership alone therefore does not prove a token has a learned embedding. Report the fraction of inference tokens excluded by training-frequency filtering and exclude or explicitly flag their embeddings in downstream comparisons. See 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. The released tokenize_anndata and ScEmbed.encode fail with Gtars 0.10.0. Use the tested explicit Region construction and token projection in the scEmbed reference; preserve cell order and reject empty, unmatched, or untrained token sets. See 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. Its preprocessing also returns blank documents with Gtars 0.10.0 after catching a tokenizer API error. See references/bedspace.md for the source contract and reproduction limitations.

Consensus universes and assessment

The installed 0.8.4 CLI uses:

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 and 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.

python skills/geniml/scripts/model_artifact_inspector.py \
  --model-dir models/region2vec
 
python skills/geniml/scripts/tokenizer_compatibility.py \
  --model-dir models/region2vec \
  --universe refs/universe.bed \
  --assembly GRCh38

Region2VecExModel(model_path="org/repo"), ScEmbed(model_path="org/repo"), and Gtars Tokenizer.from_pretrained(...) can download from Hugging Face. The Geniml classes' local from_pretrained("models/local") loads a bundle. Their constructors discard Hub revision, cache_dir, and local_files_only kwargs. For an authorized download, fetch the three files with huggingface_hub.hf_hub_download directly at a reviewed immutable revision, verify hashes, assemble a local bundle, then use the local classmethod. Do not pass a Hub ID to the constructor expecting offline or revision enforcement.

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. The token-cache download ignores the instance's bedbase_api and uses the import-time default. BEDset downloads are unbounded all-member downloads, without pagination in the checked server route. The exact GET routes and source-only/live-verification boundary are in the utilities reference.

Local inspection commands are safer: