Taykhoom/functional-random-noannot-gamba

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
causal, bidi	150,728	37,834	188,562

Each config includes the noncoding_regions extension. Filter category != "noncoding_regions" to recover the exact 169,674-row, ten-category GAMBA paper dataset without storing those rows twice.

Choosing a context

The GAMBA paper uses context geometry matched to the model. Choose *-causal for Evo2 or another left-to-right model: the ROI is at the end of the strand-oriented window, so all preceding bases are usable context. Choose *-bidi for GAMBA encoders, the distilled student, GPN-Star, PhyloGPN, or another masked/bidirectional model: the ROI is centered so the model sees both flanks. Using a causal file for a bidirectional model removes its right-side context and is not the paper protocol.

Loading

from datasets import load_dataset

dataset = load_dataset(
    "Taykhoom/functional-random-noannot-gamba",
    "bidi",
    split="all",
)
paper = dataset.filter(lambda row: row["category"] != "noncoding_regions")
test = paper.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 causal or bidirectional context of at most 2,048 bp.
context_policy	causal for *-causal; symmetric for *-bidi.
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.