Datasets:

Taykhoom
/

functional-random-noannot-gamba

pretty_name: 'GAMBA Functional Regions: Feature vs Random-Noannot'
license: mit
tags:
  - biology
  - genomics
  - dna
  - genome-language-model
  - benchmark
  - functional-genomics
  - hg38
  - parquet
configs:
  - config_name: canonical
    data_files:
      - split: all
        path: functional-random-noannot-gamba.parquet
  - config_name: noncoding-added
    data_files:
      - split: all
        path: functional-random-noannot-gamba-noncoding-added.parquet

GAMBA Functional Regions: Feature vs Random-Noannot

This dataset packages GAMBA's binary functional-region benchmark comparing annotated genomic features with chromosome- and length-matched random controls that avoid retained anchors across all generated categories. "Noannot" means no overlap with the retained benchmark annotations, not absence of every possible genomic annotation.

Configs

Config	Train rows	Test rows	Total
`canonical`	135,692	33,982	169,674
`noncoding-added`	150,726	37,834	188,560

The noncoding-added config is an explicit extension from Gamba-Processing, not an additional category emitted by GAMBA's current create_eval_data.py.

Loading

from datasets import load_dataset

canonical = load_dataset(
    "Taykhoom/functional-random-noannot-gamba",
    "canonical",
    split="all",
)
train = canonical.filter(lambda row: row["split"] == "train")
test = canonical.filter(lambda row: row["split"] == "test")

Here, split="all" tells Hugging Face to load the single physical parquet file. The parquet's own split column records the biological chromosome split (train or test). Keeping one file avoids duplicating the dataset; filter the column as shown above.

Columns

Column	Description
`split`, `label`	Probe/fine-tuning `train` or held-out `test`; labels are `feature` or `random-noannot`.
`sequence`	Strand-oriented GAMBA context of at most 2,048 bp.
`pair_id`	Shared identifier for a feature and its matched control.
`category`, `scope`	Functional class and pooling scope.
`chrom`, `start`, `end`, `strand`	Zero-based, half-open hg38 feature/control coordinates.
`context_start`, `context_end`	Forward-genome coordinates of `sequence`.
`roi_start`, `roi_end`	Feature/control offsets within `sequence`.
`pool_start`, `pool_end`	Offsets to pool for representation evaluation.
`name`	Source annotation name.
`phylop_mean`, `phylop_std`	Mean and population standard deviation over GAMBA's baseline ROI.
`phylop_frac_pos`, `phylop_frac_neg`	Fractions of ROI bases with positive or negative phyloP scores.
`phylop_mean_pos`, `phylop_mean_neg`	Means over positive or negative ROI scores, or zero when absent.
`phylop_context_mean`, `phylop_context_std`	Mean and population standard deviation over GAMBA's symmetric 2,048 bp phyloP context.
`phylop_context_frac_pos`, `phylop_context_frac_neg`	Fractions of context bases with positive or negative scores.
`phylop_context_mean_pos`, `phylop_context_mean_neg`	Means over positive or negative context scores, or zero when absent.

Data source and processing

The processing code reproduces the ten-category BED output from Microsoft GAMBA commit e83984e byte-for-byte, then freezes all GAMBA autosomes plus chrX in parquet. The GAMBA models were pretrained on the chromosomes marked train. The paper's zero-shot result used the frozen models on chr2, chr3, chr16, and chr22, which are marked test; no task-specific model was fitted on these benchmark rows. Use only test to reproduce that paper subset. The train rows are provided for probing/fine-tuning or broader chromosome evaluation.

PhyloP values come from the Zoonomia 241-mammalian track used by GAMBA. Uncovered positions are zero and scores are rounded to two decimal places before the float32 summaries are computed.

Citation

Please cite the GAMBA paper:

Consens, M. E. et al. Predicting evolutionary rate as a pretraining task improves genome language model representations. bioRxiv (2026). doi:10.64898/2026.02.02.703275. A conference version is available on OpenReview.

@article{consens2026predicting,
  title = {Predicting evolutionary rate as a pretraining task improves genome language model representations},
  author = {Consens, Micaela Elisa and Yang, Kevin K. and Hall, Jimmy and Conard, Ashley Mae and Wang, Bo and Crawford, Lorin and Moses, Alan and Lu, Alex X.},
  journal = {bioRxiv},
  year = {2026},
  doi = {10.64898/2026.02.02.703275},
  url = {https://doi.org/10.64898/2026.02.02.703275}
}

License

GAMBA and its released benchmark code/data are distributed under the MIT license. Upstream annotation resources retain their original terms.