Publish corrected RepeatMasker processing

README.md

@@ -1,130 +1,32 @@
 ---
-pretty_name: "GAMBA Functional Regions: Feature vs Random"
+pretty_name: "GAMBA Functional Regions: Feature vs Category-Matched Random"
 license: mit
-tags:
-- biology
-- genomics
-- dna
-- genome-language-model
-- benchmark
-- functional-genomics
-- hg38
-- parquet
+tags: [biology, genomics, genome-language-model, benchmark, hg38, parquet]
 configs:
 - config_name: causal
-  data_files:
-  - split: all
-    path: functional-random-gamba-causal.parquet
+  data_files: [{split: all, path: functional-random-gamba-causal.parquet}]
 - config_name: bidi
-  data_files:
-  - split: all
-    path: functional-random-gamba-bidi.parquet
+  data_files: [{split: all, path: functional-random-gamba-bidi.parquet}]
 ---
 
-# GAMBA Functional Regions: Feature vs Random
+# GAMBA feature-vs-random benchmark
 
-This dataset packages GAMBA's binary functional-region benchmark comparing
-annotated genomic features with chromosome- and length-matched random
-controls. Random controls avoid retained anchors from the same functional
-category.
+Each feature is paired with a chromosome- and length-matched random control
+that avoids retained anchors from the same category. Both configs contain
+188,568 rows; filtering `category != "noncoding_regions"` gives the
+169,682-row corrected paper view.
 
-## Configs
+Use `causal` for left-to-right models and `bidi` for masked/bidirectional
+models. The parquet `split` column marks `chr2`, `chr3`, `chr16`, and `chr22`
+as held-out `test`; Hugging Face split `all` loads the physical file.
 
-| Config | Train rows | Test rows | Total |
-|---|---:|---:|---:|
-| `causal`, `bidi` | 150,728 | 37,834 | 188,562 |
+Coordinates are zero-based, half-open hg38. `sequence` is strand-oriented,
+`roi_*` marks the feature/control, and `pool_*_in_window` is the evaluation
+span. Repeat rows preserve `repeat_class`; emitted strands are only `+` or
+`-`. The phyloP columns use the pinned Zoonomia 241-mammal track.
 
-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.
+Processing is derived from Microsoft GAMBA commit
+`e83984ea20bb4ee017993144fbe17e7bae3cdddc` and corrects the source
+RepeatMasker strand/class field mapping.
 
-## 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
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset(
-    "Taykhoom/functional-random-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`. |
-| `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 random 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_in_window`, `pool_end_in_window` | 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.
+Please cite Consens et al. (2026), doi:10.64898/2026.02.02.703275.

functional-random-gamba-bidi.parquet

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functional-random-gamba-causal.parquet

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