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.

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: For the Region2Vec/scEmbed recipes tested here, add only their required libraries: 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. 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. 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: 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: 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: 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](https://github.com/databio/geniml/blob/v0.8.4/geniml/region2vec/main.py), 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](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](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](references/bedspace.md) for the source contract and reproduction limitations. Consensus universes and assessment The installed 0.8.4 CLI uses: 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. 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: 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: Use help for resource limits and explicit path disclosure controls. References [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. Synthetic CPU checks covered training, tokenization, explicit cell pooling, local export/reload, evaluation loading, CC construction, and local caches. BEDspace native training, hosted annotation, public model inference, and real cohort performance remain untested. 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. Citing Scientific Agent Skil