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GUE_v2

Size:
100K<n<1M
Tags:
genomics
benchmark
dna
binary-classification
License:
Dataset card Data Studio Files
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Publish GUE_v2 (96 splits)

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  1. README.md +91 -0
  2. v1/core_promoter_detection_all/core_promoter_detection_all_test.csv.gz +3 -0
  3. v1/core_promoter_detection_all/core_promoter_detection_all_train.csv.gz +3 -0
  4. v1/core_promoter_detection_all/core_promoter_detection_all_val.csv.gz +3 -0
  5. v1/core_promoter_detection_notata/core_promoter_detection_notata_test.csv.gz +3 -0
  6. v1/core_promoter_detection_notata/core_promoter_detection_notata_train.csv.gz +3 -0
  7. v1/core_promoter_detection_notata/core_promoter_detection_notata_val.csv.gz +3 -0
  8. v1/core_promoter_detection_tata/core_promoter_detection_tata_test.csv.gz +3 -0
  9. v1/core_promoter_detection_tata/core_promoter_detection_tata_train.csv.gz +3 -0
  10. v1/core_promoter_detection_tata/core_promoter_detection_tata_val.csv.gz +3 -0
  11. v1/enhancer_promoter_interaction_GM12878/enhancer_promoter_interaction_GM12878_test.csv.gz +3 -0
  12. v1/enhancer_promoter_interaction_GM12878/enhancer_promoter_interaction_GM12878_train.csv.gz +3 -0
  13. v1/enhancer_promoter_interaction_GM12878/enhancer_promoter_interaction_GM12878_val.csv.gz +3 -0
  14. v1/enhancer_promoter_interaction_HUVEC/enhancer_promoter_interaction_HUVEC_test.csv.gz +3 -0
  15. v1/enhancer_promoter_interaction_HUVEC/enhancer_promoter_interaction_HUVEC_train.csv.gz +3 -0
  16. v1/enhancer_promoter_interaction_HUVEC/enhancer_promoter_interaction_HUVEC_val.csv.gz +3 -0
  17. v1/enhancer_promoter_interaction_HeLa_S3/enhancer_promoter_interaction_HeLa_S3_test.csv.gz +3 -0
  18. v1/enhancer_promoter_interaction_HeLa_S3/enhancer_promoter_interaction_HeLa_S3_train.csv.gz +3 -0
  19. v1/enhancer_promoter_interaction_HeLa_S3/enhancer_promoter_interaction_HeLa_S3_val.csv.gz +3 -0
  20. v1/enhancer_promoter_interaction_IMR90/enhancer_promoter_interaction_IMR90_test.csv.gz +3 -0
  21. v1/enhancer_promoter_interaction_IMR90/enhancer_promoter_interaction_IMR90_train.csv.gz +3 -0
  22. v1/enhancer_promoter_interaction_IMR90/enhancer_promoter_interaction_IMR90_val.csv.gz +3 -0
  23. v1/enhancer_promoter_interaction_K562/enhancer_promoter_interaction_K562_test.csv.gz +3 -0
  24. v1/enhancer_promoter_interaction_K562/enhancer_promoter_interaction_K562_train.csv.gz +3 -0
  25. v1/enhancer_promoter_interaction_K562/enhancer_promoter_interaction_K562_val.csv.gz +3 -0
  26. v1/enhancer_promoter_interaction_NHEK/enhancer_promoter_interaction_NHEK_test.csv.gz +3 -0
  27. v1/enhancer_promoter_interaction_NHEK/enhancer_promoter_interaction_NHEK_train.csv.gz +3 -0
  28. v1/enhancer_promoter_interaction_NHEK/enhancer_promoter_interaction_NHEK_val.csv.gz +3 -0
  29. v1/epigenetic_marks_prediction_H3/epigenetic_marks_prediction_H3_test.csv.gz +3 -0
  30. v1/epigenetic_marks_prediction_H3/epigenetic_marks_prediction_H3_train.csv.gz +3 -0
  31. v1/epigenetic_marks_prediction_H3/epigenetic_marks_prediction_H3_val.csv.gz +3 -0
  32. v1/epigenetic_marks_prediction_H3K14ac/epigenetic_marks_prediction_H3K14ac_test.csv.gz +3 -0
  33. v1/epigenetic_marks_prediction_H3K14ac/epigenetic_marks_prediction_H3K14ac_train.csv.gz +3 -0
  34. v1/epigenetic_marks_prediction_H3K14ac/epigenetic_marks_prediction_H3K14ac_val.csv.gz +3 -0
  35. v1/epigenetic_marks_prediction_H3K36me3/epigenetic_marks_prediction_H3K36me3_test.csv.gz +3 -0
  36. v1/epigenetic_marks_prediction_H3K36me3/epigenetic_marks_prediction_H3K36me3_train.csv.gz +3 -0
  37. v1/epigenetic_marks_prediction_H3K36me3/epigenetic_marks_prediction_H3K36me3_val.csv.gz +3 -0
  38. v1/epigenetic_marks_prediction_H3K4me1/epigenetic_marks_prediction_H3K4me1_test.csv.gz +3 -0
  39. v1/epigenetic_marks_prediction_H3K4me1/epigenetic_marks_prediction_H3K4me1_train.csv.gz +3 -0
  40. v1/epigenetic_marks_prediction_H3K4me1/epigenetic_marks_prediction_H3K4me1_val.csv.gz +3 -0
  41. v1/epigenetic_marks_prediction_H3K4me2/epigenetic_marks_prediction_H3K4me2_test.csv.gz +3 -0
  42. v1/epigenetic_marks_prediction_H3K4me2/epigenetic_marks_prediction_H3K4me2_train.csv.gz +3 -0
  43. v1/epigenetic_marks_prediction_H3K4me2/epigenetic_marks_prediction_H3K4me2_val.csv.gz +3 -0
  44. v1/epigenetic_marks_prediction_H3K4me3/epigenetic_marks_prediction_H3K4me3_test.csv.gz +3 -0
  45. v1/epigenetic_marks_prediction_H3K4me3/epigenetic_marks_prediction_H3K4me3_train.csv.gz +3 -0
  46. v1/epigenetic_marks_prediction_H3K4me3/epigenetic_marks_prediction_H3K4me3_val.csv.gz +3 -0
  47. v1/epigenetic_marks_prediction_H3K79me3/epigenetic_marks_prediction_H3K79me3_test.csv.gz +3 -0
  48. v1/epigenetic_marks_prediction_H3K79me3/epigenetic_marks_prediction_H3K79me3_train.csv.gz +3 -0
  49. v1/epigenetic_marks_prediction_H3K79me3/epigenetic_marks_prediction_H3K79me3_val.csv.gz +3 -0
  50. v1/epigenetic_marks_prediction_H3K9ac/epigenetic_marks_prediction_H3K9ac_test.csv.gz +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ pretty_name: GUE_v2 (curated)
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+ size_categories:
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+ - 100K<n<1M
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+ tags:
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+ - genomics
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+ - benchmark
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+ - dna
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+ - binary-classification
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+ ---
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+
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+ # GUE_v2, curated
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+
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+ `GUE_v2`, as published in quality-curated genomic benchmarks - one format, fixed row order, a permanent ID on every row. 32 datasets, 96 files, 846,523 rows, one gzipped CSV per split.
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+
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+ ## Getting the data
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+
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+ Two packages are the way in: [`genomic-benchmarks-data`](https://pypi.org/project/genomic-benchmarks-data/) for people, [`genomic-benchmarks-data4agents`](https://pypi.org/project/genomic-benchmarks-data4agents/) for agents, the same functions either way. They resolve the URL, check the checksum, and carry each dataset's QC results, which this repository does not.
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+
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+ ```bash
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+ pip install genomic-benchmarks-data
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+ ```
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+
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+ ```python
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+ from genomic_benchmarks_data import load_dataset
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+
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+ df = load_dataset("GUE_v2", "core_promoter_detection_all", "train")
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+ ```
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+
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+ A number measured on these files belongs on the [leaderboard](https://genomic-benchmarks.github.io/genomic-benchmarks-leaderboard/), which is what makes two of them comparable, and which prints what QC found about a dataset above every table.
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+
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+ - [genomic-benchmarks.github.io](https://genomic-benchmarks.github.io/) - the project: the QC tool, the curated corpus and the leaderboard.
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+ - [github.com/genomic-benchmarks](https://github.com/genomic-benchmarks) - all three as source, and issues.
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+
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+ ## Datasets
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+
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+ | dataset | task | species | train | val | test |
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+ |---|---|---|---|---|---|
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+ | `core_promoter_detection_all` | Binary classification | Homo sapiens | 47356 | 5920 | 5920 |
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+ | `core_promoter_detection_notata` | Binary classification | Homo sapiens | 42452 | 5307 | 5307 |
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+ | `core_promoter_detection_tata` | Binary classification | Homo sapiens | 4904 | 613 | 613 |
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+ | `enhancer_promoter_interaction_GM12878` | Binary classification | Homo sapiens | 10000 | 2000 | 2000 |
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+ | `enhancer_promoter_interaction_HUVEC` | Binary classification | Homo sapiens | 10000 | 2000 | 2000 |
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+ | `enhancer_promoter_interaction_HeLa_S3` | Binary classification | Homo sapiens | 10000 | 2000 | 2000 |
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+ | `enhancer_promoter_interaction_IMR90` | Binary classification | Homo sapiens | 10000 | 2000 | 2000 |
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+ | `enhancer_promoter_interaction_K562` | Binary classification | Homo sapiens | 10000 | 2000 | 2000 |
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+ | `enhancer_promoter_interaction_NHEK` | Binary classification | Homo sapiens | 10000 | 2000 | 2000 |
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+ | `epigenetic_marks_prediction_H3` | Binary classification | Saccharomyces cerevisiae | 11971 | 1497 | 1497 |
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+ | `epigenetic_marks_prediction_H3K14ac` | Binary classification | Saccharomyces cerevisiae | 26438 | 3305 | 3305 |
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+ | `epigenetic_marks_prediction_H3K36me3` | Binary classification | Saccharomyces cerevisiae | 27904 | 3488 | 3488 |
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+ | `epigenetic_marks_prediction_H3K4me1` | Binary classification | Saccharomyces cerevisiae | 25341 | 3168 | 3168 |
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+ | `epigenetic_marks_prediction_H3K4me2` | Binary classification | Saccharomyces cerevisiae | 24545 | 3069 | 3069 |
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+ | `epigenetic_marks_prediction_H3K4me3` | Binary classification | Saccharomyces cerevisiae | 29439 | 3680 | 3680 |
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+ | `epigenetic_marks_prediction_H3K79me3` | Binary classification | Saccharomyces cerevisiae | 23069 | 2884 | 2884 |
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+ | `epigenetic_marks_prediction_H3K9ac` | Binary classification | Saccharomyces cerevisiae | 22224 | 2779 | 2779 |
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+ | `epigenetic_marks_prediction_H4` | Binary classification | Saccharomyces cerevisiae | 11679 | 1461 | 1461 |
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+ | `epigenetic_marks_prediction_H4ac` | Binary classification | Saccharomyces cerevisiae | 27275 | 3410 | 3410 |
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+ | `promoter_detection_300_all` | Binary classification | Homo sapiens | 47356 | 5920 | 5920 |
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+ | `promoter_detection_300_notata` | Binary classification | Homo sapiens | 42452 | 5307 | 5307 |
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+ | `promoter_detection_300_tata` | Binary classification | Homo sapiens | 4904 | 613 | 613 |
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+ | `transcription_factor_prediction_human_0` | Binary classification | Homo sapiens | 32378 | 1000 | 1000 |
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+ | `transcription_factor_prediction_human_1` | Binary classification | Homo sapiens | 30672 | 1000 | 1000 |
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+ | `transcription_factor_prediction_human_2` | Binary classification | Homo sapiens | 19000 | 1000 | 1000 |
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+ | `transcription_factor_prediction_human_3` | Binary classification | Homo sapiens | 27294 | 1000 | 1000 |
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+ | `transcription_factor_prediction_human_4` | Binary classification | Homo sapiens | 19000 | 1000 | 1000 |
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+ | `transcription_factor_prediction_mouse_0` | Binary classification | Mus musculus | 6478 | 810 | 810 |
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+ | `transcription_factor_prediction_mouse_1` | Binary classification | Mus musculus | 53952 | 6745 | 6745 |
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+ | `transcription_factor_prediction_mouse_2` | Binary classification | Mus musculus | 2620 | 328 | 328 |
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+ | `transcription_factor_prediction_mouse_3` | Binary classification | Mus musculus | 1904 | 239 | 239 |
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+ | `transcription_factor_prediction_mouse_4` | Binary classification | Mus musculus | 15064 | 1883 | 1883 |
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+
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+ Files are `v1/<dataset>/<dataset>_<split>.csv.gz`, where `v1` is the version of the data. A dataset whose sequences change appears under a new prefix rather than being rewritten here.
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+
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+ ## Licence and provenance
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+
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+ - Collection DOI: https://doi.org/10.48550/arXiv.2306.15006
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+ - Covers every dataset here: Apache-2.0 (https://github.com/MAGICS-LAB/DNABERT_2/blob/main/LICENSE)
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+
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+ ## Every row has an identity
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+
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+ Each file carries two columns the upstream release does not:
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+
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+ - `id` - `<collection>:<dataset>:<version>:<split>:<row index>`, the index zero-padded to 7 digits.
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+ - `row_index` - the row's 0-based position in this file.
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+
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+ Row order is fixed within a major version, so an `id` names the same sequence for as long as that version exists - which is what makes a reported number traceable to what it was computed on. The corpus metadata carries the SHA-256 of every split file, so a copy can be checked against the audited one.
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+
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+ ## Citation
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+
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+ Cite the collection's own publication (https://doi.org/10.48550/arXiv.2306.15006) for the data, and *Automated quality control for genomic sequence benchmarks reveals pervasive single-feature separability and train-test leakage* for the curation and the audit.
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