README.md

---
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](https://github.com/TaykhoomDalal/Gamba-Processing), not an
additional category emitted by GAMBA's current `create_eval_data.py`.

## Loading

```python
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](https://github.com/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](https://doi.org/10.64898/2026.02.02.703275).
A conference version is available on
[OpenReview](https://openreview.net/forum?id=dkgM2ale4U).

```bibtex
@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.