Geniml

I/O, tokenization, caches, evaluation, and security

Research snapshot: 2026-10-01. API claims below use official documentation, PyPI metadata, and the geniml v0.8.4 release source. Where current docs and release code disagree, the discrepancy is stated explicitly.

Release and dependency facts

geniml==0.8.4 was released on 2026-01-14. PyPI metadata:

• does not set Requires-Python;

• classifies Python 3.10, 3.11, 3.12, 3.13, and 3.14;

• provides ml and test extras;

• ships a pure-Python wheel, but dependencies include native packages;

• uses Trusted Publishing with a Sigstore provenance link to tag v0.8.4.

Release files:

geniml-0.8.4-py3-none-any.whl
sha256 ac0fc520cde6f6461120aee6b40d3cbf20e1e3d8bdb95a3a45345724eee545b5

geniml-0.8.4.tar.gz
sha256 6f429c7c89d06a4c2c378e349d14b3a2d984dc441440ada38473772fce6addb1

The stable release requires gtars>=0.2.5 without an upper bound. The 2026-10-01 synthetic CPU checks used gtars==0.10.0, Python 3.12, AnnData 0.12.19, Scanpy 1.12.4, Zarr 2.18.7, Gensim 4.4.0, Torch 2.14.1, PyArrow 25.0.1, NumPy 2.5.3, pandas 2.3.3, and Hugging Face Hub 2.0.0. They exercised Region2Vec/scEmbed training, explicit token/cell pooling, local bundles, CC construction, and local BED/token caches. Gtars 0.10.0 was released 2026-09-05 and requires Python >=3.10.

Pin the tested stack and retain uv.lock; upstream transitive requirements are mostly lower bounds. Geniml's Zarr <3 constraint is incompatible with AnnData 0.13 (Zarr >=3). AnnData 0.12.19 works with the tested Zarr 2.18.7. The full ml extra resolves but brings additional old pinned components; it was resolver-checked only, unlike the selective stack above.

BED coordinates and validation

BED is normally 0-based, half-open [start, end). A valid BED3 row requires:

• nonempty contig;

• integer start >= 0;

• integer end > start;

• end no greater than the declared contig length;

• values within bounded integer limits.

Do not confuse BED with 1-based closed VCF/GFF coordinates. BED columns beyond BED3 have their own constraints; preserve them rather than guessing. BED3 carries no strand. When BED6 strand exists, preserve +, -, or . unless a documented assay conversion says otherwise.

Assembly compatibility requires more than matching chr prefixes. BEDbase describes chromosome-name sensitivity (XS), out-of-bounds regions (OOBR), and sequence fit (SF) as separate criteria. Record a concrete assembly/accession and chromosome-sizes digest. Reject unknown alt/decoy contigs rather than dropping them silently.

Run:

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

The script emits aggregate errors and a normalization plan. It never rewrites coordinates or performs liftover.

Region and RegionSet APIs

Gtars API for new workflows

from gtars.models import Region, RegionSet

region = Region("chr1", 100, 200, None)
regions = RegionSet("data/peaks.bed")
print(len(regions))
regions.to_bed("work/peaks.copy.bed")

Gtars 0.10.0 requires the fourth Region constructor argument (rest), including None for BED3. It exposes operations including sort, reduce, coverage, count_overlaps, jaccard, nearest_neighbors, to_bed, to_bed_gz, and to_bigbed. Do not assume these operations validate assembly or coordinate semantics. Validate before constructing the object and after writing output.

Official Gtars constructors can accept URLs. This skill restricts them to validated local regular files by default.

Legacy Geniml I/O

from geniml.io import BedSet, Region, RegionSet

region = Region("chr1", 100, 200)  # third field is named stop
regions = RegionSet("data/peaks.bed", backed=False)

geniml.io.RegionSet(regions, backed=False) accepts a path/URL or a list of legacy Regions. With backed=True, it streams the BED, supports iteration and length, and does not support indexing. Other release classes include BedSet, Maf, SNP, TokenizedRegionSet, and RegionSetCollection, though only Region, RegionSet, and BedSet are exported from geniml.io.

The 0.7.0 changelog says RegionSet use switched toward Gtars. Avoid mixing legacy and Gtars Region objects accidentally: field names and accepted types differ.

Tokenization

Current Gtars tokenizer

from gtars.models import RegionSet
from gtars.tokenizers import Tokenizer

tokenizer = Tokenizer.from_bed("refs/universe.bed")
encoded = tokenizer(RegionSet("data/peaks.bed"))
input_ids = encoded["input_ids"]

Gtars exposes Tokenizer.from_config(cfg) and Tokenizer.from_pretrained(path). The latter can access Hugging Face and has no revision/cache kwargs. For authorized downloads, use the Hub client with an immutable revision, then Tokenizer.from_bed on verified local bytes.

With Gtars 0.10.0, a two-region universe produced len(tokenizer) == 9 because seven special tokens are included. Preserve:

• universe bytes, row order, and assembly;

• Gtars version;

• special_tokens_map and every special-token ID;

• tokenizer length;

• token-corpus schema and checksum.

Run the local compatibility planner:

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

Legacy hard tokenization

The stable file geniml.tokenization.main defines:

hard_tokenization_main(
    src_folder,
    dst_folder,
    universe_file,
    fraction=1e-9,
    file_list=None,
    num_workers=10,
    bedtools_path="bedtools",
)

It invokes an external Bedtools executable and multiprocessing. The fraction is passed to interval intersection logic; it is not a statistical p-value. The package geniml.tokenization.__init__ comments out its public exports, so from geniml.tokenization import hard_tokenization and the installed geniml tokenize dispatcher are unreliable in 0.8.4.

Do not use an arbitrary bedtools found on PATH. If reproducing the legacy path, pin and checksum the binary, pass an explicit absolute path, validate argv, bound workers, use a fresh output directory, and compare synthetic results with the Gtars tokenizer.

The current preferred token corpus is a bounded Parquet file with one list-valued tokens column. Upstream issue #14 proposes deprecating one .gtok file per cell.

BBClient and cache behavior

from geniml.bbclient import BBClient

client = BBClient(cache_folder="/absolute/project/.bbcache")

Release defaults:

• endpoint: BEDBASE_API or https://api.bedbase.org;

• cache root: BBCLIENT_CACHE or ~/.bbcache;

• BEDs under bedfiles/;

• BED sets under bedsets/;

• token cache in tokens.zarr.

load_bed, load_bedset, load_bed_tokens, add_bed_tokens_to_cache, and corresponding cache-* CLI commands may use the network on a cache miss. cache_tokens itself writes local data. The S3 convenience methods add_bed_to_s3 and get_bed_from_s3 are deprecated; use a separately reviewed boto3/s3fs workflow for an explicitly requested cloud transfer.

The released client and current BEDhost source agree on these read routes:

These methods send no request body or API-authentication header. Public route source declares no authentication dependency. No invented bearer-token setup or pagination loop is needed. A deployment can impose its own access policy. Live openapi.json retrieval returned HTTP 403 in this review, so this table is source-verified and request-mocked, not a successful production download.

Important release behavior:

• BED and BEDset routes use self.bedbase_api; token-location requests use DEFAULT_BEDBASE_API captured at import time, ignoring the instance setting. Passing a private endpoint to the constructor does not contain token requests.

• Calls have no timeout, stream bound, or general retry handling. BEDset loading reads one complete response and downloads every listed BED; bound the requested collection before invoking it. Tokens can contact the separate endpoint_url returned by metadata.

• BEDBASE_API and BBCLIENT_CACHE defaults are read at module import time; changing them afterward does not update existing defaults. Explicit cache paths are preferable.

• BEDbase identifiers are content identifiers, not automatically SHA-256 checksums of the compressed file. Record your own digest and validate assembly/coordinates after retrieval.

Local-oriented CLI:

geniml bbclient seek ID --cache-folder CACHE
geniml bbclient inspect-bedfiles --cache-folder CACHE
geniml bbclient inspect-bedsets --cache-folder CACHE
geniml bbclient rm ID --cache-folder CACHE

rm mutates cache state; confirm the exact resolved target first. Inspection can still expose BED IDs and local paths, so redact output in shared logs.

Before an approved download:

1. approve endpoint and exact IDs;

2. set a project-scoped cache and byte quota;

3. reject redirects to unapproved hosts where tooling permits;

4. verify compressed and expanded size;

5. checksum downloaded bytes;

6. validate BED and assembly;

7. record provenance and retrieval time.

No bundled skill script performs BBClient or Hub downloads.

Model loading and artifact safety

Modern local classes:

from geniml.region2vec.main import Region2VecExModel
from geniml.scembed.main import ScEmbed

r2v = Region2VecExModel.from_pretrained("models/region2vec")
sce = ScEmbed.from_pretrained("models/scembed")

These classmethods use local bundle paths. Constructors with model_path="organization/model" call Hugging Face Hub and download checkpoint.pt, universe.bed, and config.yaml. Their constructors silently discard revision/cache/offline kwargs; call the Hub download API directly at a reviewed commit for authorized downloads, then use the local classmethod.

Geniml's loader uses torch.load(..., weights_only=True) and YAML safe_load. This is safer than unrestricted pickle, but artifacts can still cause resource exhaustion or exploit parser/native-library vulnerabilities. Gensim .model, pickle, joblib, TorchScript, shared libraries, and external binaries require even stronger distrust.

Inspect without importing Torch or deserializing:

python skills/geniml/scripts/model_artifact_inspector.py \
  --model-dir models/region2vec \
  --verify-manifest models/region2vec/SHA256SUMS

The inspector reads bounded metadata formats, hashes bytes, flags risky extensions, rejects URLs/symlinks/traversal, and never loads model objects.

Embedding evaluation

The geniml eval release CLI has:

gdst     Genome distance scaling test
npt      Neighborhood preserving test
ctt      Cluster tendency test
rct      Reconstruction test
bin-gen  Generate binary embeddings

Examples:

geniml eval gdst \
  --model-path /absolute/project/model \
  --embed-type exmodel \
  --num-samples 10000 \
  --seed 42

geniml eval npt \
  --model-path /absolute/project/model \
  --embed-type exmodel \
  --K 10 \
  --num-samples 1000 \
  --seed 42 \
  --num-workers 4

exmodel selects a modern local bundle; region2vec selects a legacy Gensim model file. The modern loader and its special-token filtering were executed. Full CTT/RCT/GDST/NPT calculations are illustrative templates here. Check that every retained region vector was actually trained before a metric calculation; removing special vectors alone does not establish that.

The official tutorial's BaseEmbeddings and bin-gen workflows use pickle for binary embedding objects. Generate them locally and never load an untrusted pickle. Bound samples/workers and record random seeds. Evaluate on patients/donors excluded from universe selection and training.

Primary source: Zheng et al. (2024), Methods for evaluating unsupervised vector representations of genomic regions, doi:10.1093/nargab/lqae086.

BEDshift

The integrated Bedshift CLI is:

geniml bedshift --help

It can use a local chromosome-length file or resolve a Refgenie genome through refgenconf. Prefer an explicit, checksummed local chromosome-sizes file to avoid implicit lookup/network behavior. Set a seed, output bound, and perturbation policy matching the null hypothesis. Validate randomized coordinates exactly like originals.

Primary source: Gu et al. (2021), Bedshift: perturbation of genomic interval sets, doi:10.1186/s13059-021-02440-w.

Privacy and bounded reporting

Potentially sensitive:

• BED coordinates and filenames;

• sample, donor, patient, treatment, diagnosis, and tissue metadata;

• cell barcodes and rare labels;

• embeddings that permit membership or nearest-neighbor inference;

• local cache paths and BEDbase identifiers.

Default reports should contain only aggregate counts, schema names, checksums, software versions, and redacted file IDs. Cap error examples; never dump entire malformed rows or metadata values. Store full provenance manifests in the protected project, not chat output.

Migration and deprecation ledger

• 0.4.0: tokenizers renamed to TreeTokenizer and AnnDataTokenizer.

• 0.7.0: RegionSet use moved toward Gtars and encoding changed.

• 0.8.0: Atacformer added.

• 0.8.1: BEDspace fixed according to the changelog.

• 0.8.4: latest stable; release notes contain only the version bump.

• Current Gtars API: use the unified gtars.tokenizers.Tokenizer.

• .gtok: proposed for deprecation in upstream issue #14; prefer Parquet.

• embedding_size: backward-compatible config key; use embedding_dim.

• Top-level Region2Vec/scEmbed/tokenization exports shown by old docs are absent/commented in the 0.8.4 wheel.

• Official geniml assess examples are stale; use geniml assess-universe.

Dated authoritative sources

Package and source:

• PyPI: geniml 0.8.4 — released 2026-01-14; accessed 2026-10-01.

• PyPI JSON: geniml 0.8.4 — artifact hashes, dependencies, extras, and null Requires-Python; accessed 2026-10-01.

• GitHub release v0.8.4 — commit 5e8dd14126c45d14917df74de4fb405f383afb61; released 2026-01-14; accessed 2026-10-01.

• Geniml changelog — through 0.8.1 on the rendered page; accessed 2026-10-01.

• PyPI: gtars 0.10.0 — released 2026-09-05; accessed 2026-10-01.

Official API/ecosystem documentation:

• Geniml documentation — accessed 2026-10-01.

• Geniml I/O API — accessed 2026-10-01.

• Gtars RegionSet — accessed 2026-10-01.

• Gtars tokenizers — accessed 2026-10-01.

• BEDbase reference-genome compatibility — accessed 2026-10-01.

• BEDbase BBClient and caching — accessed 2026-10-01.

• Geniml evaluation tutorial — accessed 2026-10-01.

• Official citation map — accessed 2026-10-01.

Primary papers:

• Gharavi et al. 2021, Region2Vec

• Gu et al. 2021, BEDshift

• Gharavi et al. 2024, BEDspace

• LeRoy et al. 2024, scEmbed

• Rymuza et al. 2024, consensus universes

• Zheng et al. 2024, embedding evaluation

Current source and execution boundaries

• Released Region2Vec implementation, export/load utilities, scEmbed implementation, and tokenization were checked against the SHA-256-verified released wheel, not master.

• Released BBClient and route constants were checked with mocked requests and a local Zarr cache.

• Current server source at commit 864eeddab6900249e05f4fc84a171890ac997877: objects, BEDsets, token metadata. This does not prove that production runs that commit.

• AnnData release notes and 0.13.4 metadata establish the separate Zarr 3 environment boundary.

• Released Annotator still uses the removed search API; Qdrant query documentation documents query_points. Qdrant 1.19.1 client API was inspected locally; no hosted annotation or credentialed call was executed.

• No model weights, human cohort data, or large genomes were downloaded. No StarSpace native build, HMM/ML/CCF end-to-end run, or external annotation service was executed. Their source contracts are illustrative until a project-specific synthetic smoke passes.