Datasets:

Taykhoom
/

functional-random-gamba

pretty_name: 'GAMBA Functional Regions: Feature vs Category-Matched Random'
license: other
tags:
  - biology
  - genomics
  - dna
  - regulatory-genomics
  - genome-language-model
  - benchmark
  - hg38
  - parquet
configs:
  - config_name: causal
    data_files:
      - split: all
        path: functional-random-gamba-causal.parquet
  - config_name: bidi
    data_files:
      - split: all
        path: functional-random-gamba-bidi.parquet

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

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

Scope	Rows	Held-out `test` rows
Complete release	184,978	36,808
Corrected paper categories	165,972	33,012

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.

Context configurations

Config	Geometry
`causal`	ROI end-anchored after strand orientation for autoregressive models.
`bidi`	ROI 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:

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

Columns

Column group	Description
`split`, `sequence`, `label`, `pair_id`	Chromosome partition, exact input, `feature`/`random` class, and matched pair.
`context_group_id`	Exact model-input leakage group within this physical config.
`category`, `repeat_class`, `scope`	Source category, nullable repeat class, and `full` scope.
`context_policy`	`causal` or `symmetric`.
`chrom`, `start`, `end`, `source_strand`	Zero-based, half-open hg38 coordinates and biological strand (`.` means unknown).
`sequence_orientation`	Explicit `+`/`-` orientation used for sequence geometry and reverse complementation.
`context_start`, `context_end`	Forward-genome sequence context coordinates.
`roi_start`, `roi_end`	Feature/control offsets inside oriented `sequence`.
`pool_start_in_window`, `pool_end_in_window`	Evaluation pooling offsets.
`name`	Source/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.