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Taykhoom/functional-random-gamba

GAMBA Functional Regions: Feature vs Category-Matched Random This paired binary representation benchmark asks whether a model can distinguish an annotated functional region from a chromosome- and length-matched random control. For this dataset, a random control avoids retained anchors from the same functional category. It may overlap annotations from other categories. Use the annotation-free random dataset if controls must avoid every retained annotation category. Each… See the full description on the dataset page: https://huggingface.co/datasets/Taykhoom/functional-random-gamba.

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GAMBA Functional Regions: Feature vs Category-Matched Random

This paired binary representation benchmark asks whether a model can distinguish an annotated functional region from a chromosome- and length-matched random control.

For this dataset, a random control avoids retained anchors from the same functional category. It may overlap annotations from other categories. Use the annotation-free random dataset if controls must avoid every retained annotation category.

Each feature/control pair shares pair_id.

Loading

python
from datasets import load_dataset

bidi = load_dataset(
    "Taykhoom/functional-random-gamba",
    "bidi",
    split="all",
)

paper_test = bidi.filter(
    lambda row: row["split"] == "test"
    and row["category"] != "noncoding_regions"
)

Dataset size and splits

ScopeRowsHeld-out `test` rows
Complete release184,97836,808
Corrected paper categories165,97233,012

Labels are balanced: 92,489 feature and 92,489 random rows.

Rows on chr2, chr3, chr16, and chr22 are marked test; remaining included chromosomes are train. The Hugging Face split all loads the whole physical file.

Categories

The complete release contains 92,489 source features across:

text
repeats, UCNE, vista_enhancer, promoters, UTR5, UTR3,
coding_regions, exons, introns, upstream_TSS, noncoding_regions

category != "noncoding_regions" yields the corrected ten-category paper view. repeat_class is populated on the 19,974 repeat-derived rows (feature and matched control).

Context configurations

ConfigGeometry
causalROI end-anchored after strand orientation for autoregressive models.
bidiROI centered for bidirectional/masked models.

All 184,978 bidi sequences are 2,048 bp. In causal, 165,012 rows are 2,048 bp and 19,966 long-feature rows are truncated to 1,000 bp. Every released random control context contains only uppercase A/C/G/T.

Evaluation

For the paper protocol:

  1. 1.filter split == "test";
  2. 2.exclude category == "noncoding_regions";
  3. 3.pool each representation over [pool_start_in_window, pool_end_in_window);
  4. 4.evaluate cosine leave-one-out 1-nearest-neighbor balanced accuracy.

Columns

Column groupDescription
split, sequence, label, pair_idChromosome partition, exact input, feature/random class, and matched pair.
context_group_idExact model-input leakage group within this physical config.
category, repeat_class, scopeSource category, nullable repeat class, and full scope.
context_policycausal or symmetric.
chrom, start, end, source_strandZero-based, half-open hg38 coordinates and biological strand (. means unknown).
sequence_orientationExplicit +/- orientation used for sequence geometry and reverse complementation.
context_start, context_endForward-genome sequence context coordinates.
roi_start, roi_endFeature/control offsets inside oriented sequence.
pool_start_in_window, pool_end_in_windowEvaluation pooling offsets.
nameSource/control identifier.
phylop_*, phylop_context_*Six ROI and six symmetric-context float32 phyloP summaries.

Corrected repeat processing

RepeatMasker genomic strand and repeat class are separate fields. The release uses UCSC rmsk field 9 as source_strand, field 11 as repeat_class, and rebuilds repeat-derived controls and sequence orientation. Labels remain unchanged.

VISTA requires hg38 and normalized positive expression: 1,367 raw records collapse to 1,240 unique elements; 1,522 non-positive hg38 and 1,750 non-hg38 rows are excluded without liftover. All ambiguous feature/control groups and all conflicting-label exact-input groups are removed. Same-label duplicates remaining within a config share context_group_id; independent checks find zero exact inputs crossing train/test and zero conflicting-label groups. Every preserved random candidate is also rechecked against current same-category anchors; invalid candidates are deterministically resampled.

Processing and citation

Processing and verification:

Consens, M. E. et al. Predicting evolutionary rate as a pretraining task improves genome language model representations. bioRxiv (2026). https://doi.org/10.64898/2026.02.02.703275

License

The processing code derived from GAMBA is MIT licensed under the processing repository's LICENSE. This generated dataset is marked other: incorporated reference sequence, annotations, and phyloP-derived values retain their upstream terms, so no blanket MIT license is asserted for the Parquets.