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README.md
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---
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pretty_name: "GAMBA Functional Regions: Feature vs Category-Matched Random"
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license: mit
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tags:
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configs:
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- config_name: causal
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data_files:
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- config_name: bidi
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data_files:
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---
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# GAMBA
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169,682-row corrected paper view.
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`roi_*` marks the feature/control, and `pool_*_in_window` is the evaluation
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span. Repeat rows preserve `repeat_class`; emitted strands are only `+` or
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`-`. The phyloP columns use the pinned Zoonomia 241-mammal track.
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`e83984ea20bb4ee017993144fbe17e7bae3cdddc` and corrects the source
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RepeatMasker strand/class field mapping.
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---
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pretty_name: "GAMBA Functional Regions: Feature vs Category-Matched Random"
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license: mit
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tags:
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- biology
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- genomics
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- dna
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- regulatory-genomics
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- genome-language-model
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- benchmark
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- hg38
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- parquet
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configs:
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- config_name: causal
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data_files:
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- split: all
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path: functional-random-gamba-causal.parquet
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- config_name: bidi
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data_files:
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- split: all
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path: functional-random-gamba-bidi.parquet
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---
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# GAMBA Functional Regions: Feature vs Category-Matched Random
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This paired binary representation benchmark asks whether a model can
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distinguish an annotated functional region from a chromosome- and
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length-matched random control.
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For this dataset, a random control avoids retained anchors from the **same
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functional category**. It may overlap annotations from other categories.
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Use the annotation-free random dataset if controls must avoid every retained
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annotation category.
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Each feature/control pair shares `pair_id`.
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## Loading
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```python
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from datasets import load_dataset
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bidi = load_dataset(
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"Taykhoom/functional-random-gamba",
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"bidi",
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split="all",
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)
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paper_test = bidi.filter(
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lambda row: row["split"] == "test"
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and row["category"] != "noncoding_regions"
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)
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```
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## Dataset size and splits
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| Scope | Rows | Held-out `test` rows |
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|---|---:|---:|
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| Complete release | 188,568 | 37,832 |
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| Corrected paper categories | 169,682 | 33,984 |
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Labels are balanced: 94,284 `feature` and 94,284 `random` rows.
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Rows on `chr2`, `chr3`, `chr16`, and `chr22` are marked `test`; remaining
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included chromosomes are `train`. The Hugging Face split `all` loads the
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whole physical file.
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## Categories
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The complete release contains 94,284 source features across:
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```text
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repeats, UCNE, vista_enhancer, promoters, UTR5, UTR3,
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coding_regions, exons, introns, upstream_TSS, noncoding_regions
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```
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`category != "noncoding_regions"` yields the corrected ten-category paper
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view. `repeat_class` is populated on the 19,982 repeat-derived rows
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(feature and matched control).
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## Context configurations
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| Config | Geometry |
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|---|---|
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| `causal` | ROI end-anchored after strand orientation for autoregressive models. |
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| `bidi` | ROI centered for bidirectional/masked models. |
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All sequences are 2,048 bp and use uppercase hg38 bases.
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## Evaluation
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For the paper protocol:
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1. filter `split == "test"`;
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2. exclude `category == "noncoding_regions"`;
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3. pool each representation over
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`[pool_start_in_window, pool_end_in_window)`;
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4. evaluate cosine leave-one-out 1-nearest-neighbor balanced accuracy.
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## Columns
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| Column group | Description |
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| `split`, `sequence`, `label`, `pair_id` | Chromosome partition, exact input, `feature`/`random` class, and matched pair. |
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| `category`, `repeat_class`, `scope` | Source category, nullable repeat class, and `full` scope. |
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| `context_policy` | `causal` or `symmetric`. |
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| `chrom`, `start`, `end`, `strand` | Zero-based, half-open hg38 coordinates. |
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| `context_start`, `context_end` | Forward-genome sequence context coordinates. |
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| `roi_start`, `roi_end` | Feature/control offsets inside oriented `sequence`. |
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| `pool_start_in_window`, `pool_end_in_window` | Evaluation pooling offsets. |
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| `name` | Source/control identifier. |
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| `phylop_*`, `phylop_context_*` | Six ROI and six symmetric-context float32 phyloP summaries. |
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## Corrected repeat processing
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RepeatMasker genomic strand and repeat class are separate fields. The release
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uses UCSC `rmsk` field 9 as `strand`, field 11 as `repeat_class`, and rebuilds
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repeat-derived controls and sequence orientation. Labels remain unchanged.
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Published pair identities and deterministic 100 bp selections for unaffected
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features are preserved.
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## Processing and citation
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Processing and verification:
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https://github.com/TaykhoomDalal/Gamba-Processing/tree/main/Functional-Regions
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Consens, M. E. et al. *Predicting evolutionary rate as a pretraining task
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improves genome language model representations*. bioRxiv (2026).
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https://doi.org/10.64898/2026.02.02.703275
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## License
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GAMBA benchmark code/data are MIT licensed. Upstream resources retain their
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own terms.
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