| --- |
| pretty_name: GenomeQA |
| language: |
| - en |
| task_categories: |
| - question-answering |
| tags: |
| - biology |
| - bioinformatics |
| - genomics |
| - dna |
| - benchmark |
| - multiple-choice |
| dataset_info: |
| - config_name: binary |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 2101672 |
| num_examples: 2600 |
| download_size: 828753 |
| dataset_size: 2101672 |
| - config_name: mcq |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 6130755 |
| num_examples: 2600 |
| download_size: 4880581 |
| dataset_size: 6130755 |
| - config_name: mcq_carbon |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 15000459 |
| num_examples: 2600 |
| download_size: 11786125 |
| dataset_size: 15000459 |
| - config_name: mcq_carbon_600 |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 3012902 |
| num_examples: 600 |
| download_size: 2377835 |
| dataset_size: 3012902 |
| - config_name: eukaryote_cls |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 21610676 |
| num_examples: 1000 |
| download_size: 16997912 |
| dataset_size: 21610676 |
| - config_name: binary_600 |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 440655 |
| num_examples: 600 |
| download_size: 186982 |
| dataset_size: 440655 |
| - config_name: mcq_600 |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 1243475 |
| num_examples: 600 |
| download_size: 996782 |
| dataset_size: 1243475 |
| - config_name: binary_eukaryote |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 198070 |
| num_examples: 170 |
| download_size: 92539 |
| dataset_size: 198070 |
| - config_name: binary_prokaryote |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 174961 |
| num_examples: 150 |
| download_size: 84047 |
| dataset_size: 174961 |
| - config_name: mcq_eukaryote |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 681920 |
| num_examples: 177 |
| download_size: 567585 |
| dataset_size: 681920 |
| - config_name: mcq_prokaryote |
| features: |
| - name: id |
| dtype: int64 |
| - name: subtask |
| dtype: string |
| - name: question |
| dtype: string |
| - name: options |
| list: string |
| - name: answer_index |
| dtype: int64 |
| - name: answer_text |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 599809 |
| num_examples: 165 |
| download_size: 495373 |
| dataset_size: 599809 |
| configs: |
| - config_name: binary |
| data_files: |
| - split: train |
| path: binary/train-* |
| - config_name: mcq |
| data_files: |
| - split: train |
| path: mcq/train-* |
| - config_name: mcq_carbon |
| data_files: |
| - split: train |
| path: mcq_carbon/train-* |
| - config_name: mcq_carbon_600 |
| data_files: |
| - split: train |
| path: mcq_carbon_600/train-* |
| - config_name: eukaryote_cls |
| data_files: |
| - split: train |
| path: mcq/eukaryote_cls_mcq.parquet |
| - config_name: binary_600 |
| data_files: |
| - split: train |
| path: binary_600/train-* |
| - config_name: mcq_600 |
| data_files: |
| - split: train |
| path: mcq_600/train-* |
| - config_name: binary_eukaryote |
| data_files: |
| - split: train |
| path: binary_eukaryote/train-* |
| - config_name: binary_prokaryote |
| data_files: |
| - split: train |
| path: binary_prokaryote/train-* |
| - config_name: mcq_eukaryote |
| data_files: |
| - split: train |
| path: mcq_eukaryote/train-* |
| - config_name: mcq_prokaryote |
| data_files: |
| - split: train |
| path: mcq_prokaryote/train-* |
| --- |
| |
| # GenomeQA |
|
|
| GenomeQA tests whether a general-purpose language model can answer questions about raw nucleotide sequences, with no |
| specialised DNA encoder and no task-specific training. Each question is English text containing one or more DNA |
| sequences, answerable from signals such as sequence composition, motifs, and motif context. |
|
|
| 5,200 examples — 2,600 binary-choice and 2,600 four-option — with sequences from 6 to 1,000 bp, across six task |
| families: |
|
|
| 1. enhancer and promoter identification; |
| 2. splice-site identification; |
| 3. taxonomic classification; |
| 4. histone-mark and chromatin-state prediction; |
| 5. transcription-factor binding-site prediction; and |
| 6. transcription-factor motif prediction. |
|
|
| ## Source and construction |
|
|
| This repository repackages the benchmark introduced in |
| [GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding](https://huggingface.co/papers/2604.05774) |
| ([arXiv:2604.05774](https://arxiv.org/abs/2604.05774)) by Weicai Long, Yusen Hou, Junning Feng, Houcheng Su, Shuo |
| Yang, Donglin Xie, and Yanlin Zhang, as Parquet with the extra subsets listed below. We use it to evaluate Carbon-X. |
|
|
| The paper constructs GenomeQA from established genomic resources: |
|
|
| | Task family | Primary source | Sequence length | |
| | --- | --- | ---: | |
| | Enhancer and promoter identification | EPDnew and ENCODE SCREEN | 400 bp | |
| | Splice-site identification | Nucleotide Transformer downstream tasks | 600 bp | |
| | Taxonomic classification | NCBI RefSeq | 1,000 bp | |
| | Histone-mark prediction | Nucleotide Transformer downstream tasks | 1,000 bp | |
| | TFBS prediction | ENCODE and JASPAR | 100 bp | |
| | TF motif prediction | ENCODE and JASPAR | 6–20 bp | |
|
|
| The authors standardize sequence windows, remove overlapping examples to reduce ambiguity, exclude sequences with |
| ambiguous nucleotide bases, and convert the resulting annotations into binary-choice and multiple-choice question |
| templates. Refer to the paper for the full construction procedure and source-database citations. |
|
|
| ## Configurations |
|
|
| All configurations contain a single `train` split. The full benchmark configurations are intentionally not equal in |
| size across subtasks: each contains 500 examples for the first five task families and 100 motif examples. Benchmark |
| results should therefore report per-subtask accuracy and use an unweighted mean across the six subtasks when a single |
| headline score is needed. |
|
|
| | Configuration | Rows | Meaning | |
| | --- | ---: | --- | |
| | `binary` | 2,600 | Full benchmark in binary-choice format, with two ordered options per question. | |
| | `mcq` | 2,600 | Full benchmark in four-option multiple-choice format. | |
| | `mcq_carbon` | 2,600 | MCQ benchmark with the 500 broad taxonomic-classification (`3_species_cls`) rows replaced by 500 stratified within-eukaryote `eukaryote_cls` rows. | |
| | `mcq_carbon_600` | 600 | Stratified 100-row sample from each `mcq_carbon` subtask. | |
| | `binary_600` | 600 | Lower-cost binary subset with 100 examples from each of the six subtasks. | |
| | `mcq_600` | 600 | Lower-cost MCQ subset with 100 examples from each of the six subtasks. | |
| | `binary_eukaryote` | 170 | Binary `3_species_cls` questions whose target organism is eukaryotic. | |
| | `binary_prokaryote` | 150 | Binary `3_species_cls` questions whose target organism is prokaryotic. | |
| | `mcq_eukaryote` | 177 | MCQ `3_species_cls` questions whose target organism is eukaryotic. | |
| | `mcq_prokaryote` | 165 | MCQ `3_species_cls` questions whose target organism is prokaryotic. | |
| | `eukaryote_cls` | 1,000 | Eukaryote classification *within* eukaryotes: all four options are eukaryotic taxa, so the model must tell eukaryotic lineages apart rather than separate eukaryote from prokaryote. Auxiliary set, stored separately from the original 5,200-example benchmark. | |
|
|
| The `mcq_carbon` configuration preserves the other `mcq` rows and inserts 500 `eukaryote_cls` questions at the |
| original species-classification position. From source revision `ae30ecec16a7669f342f7d87439552142cdebe40`, seed 42 selects 300 |
| `basic_class`, 100 `reverse_search`, and 100 `odd_one_out` rows, sampling approximately half of each taxon label |
| within each question type. See [`scripts/create_mcq_carbon.py`](scripts/create_mcq_carbon.py). |
|
|
| The `mcq_carbon_600` configuration samples without replacement from `mcq_carbon` at revision |
| `c0a7d5b8ccfe4c0d64bc3ff5dd171ff4365c16f6` using seed 42. It retains 100 rows per subtask and preserves the mix of question |
| dimensions within each subtask. Within dimensions, it samples proportionally by target label where available; eukaryote odd-one-out |
| questions are also stratified by majority taxon, and answer position is the final stratum. The 100 motif rows are all |
| retained. See |
| [`scripts/create_mcq_carbon_600.py`](scripts/create_mcq_carbon_600.py). |
|
|
| The `eukaryote_cls` configuration uses the `mcq` feature schema, including JSON-serialized `metadata`. |
|
|
| The `binary_600` and `mcq_600` configurations were sampled without replacement using seed 42 from source revision |
| `a71ab7e0e72b8eca7acd4475744dccd0159b20be`. The 100 selected examples from each subtask were then combined and |
| shuffled so the rows are not stored in subtask blocks. These subsets are balanced by task family, but not necessarily |
| by every internal label or question dimension. See |
| [`scripts/create_600_subsets.py`](scripts/create_600_subsets.py) for the reproducible generation script. |
|
|
| The organism-specific configurations contain only rows from the `3_species_cls` subtask and preserve their source |
| order. The target organism is read from `metadata`: `true` for binary `1_identity` and MCQ `basic_class` questions, |
| `target` for MCQ `reverse_search` questions, and `minority` for MCQ `odd_one_out` questions because the minority class |
| is the sequence the model must identify. Eukaryote and prokaryote subsets are disjoint within each answer format; |
| virus rows are not included. See |
| [`scripts/create_organism_subsets.py`](scripts/create_organism_subsets.py) for the reproducible filtering script. |
|
|
| ## Data fields |
|
|
| | Field | Description | |
| | --- | --- | |
| | `id` | Integer identifier within the source subtask. | |
| | `subtask` | Task-family identifier used for grouped evaluation. | |
| | `question` | Natural-language question containing the relevant DNA sequence or sequences. | |
| | `options` | Ordered list of candidate answers. | |
| | `answer_index` | Zero-based index of the correct entry in `options`. | |
| | `answer_text` | Correct answer text. For sequence-selection questions, this may itself be a DNA sequence. | |
| | `metadata` | Serialized task-specific metadata describing the question dimension and source label. | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| |
| binary = load_dataset("HuggingFaceBio/genomeqa", "binary", split="train") |
| representative_mcq = load_dataset("HuggingFaceBio/genomeqa", "mcq_600", split="train") |
| eukaryote_mcq = load_dataset("HuggingFaceBio/genomeqa", "mcq_eukaryote", split="train") |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{long2026genomeqa, |
| title={GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding}, |
| author={Long, Weicai and Hou, Yusen and Feng, Junning and Su, Houcheng and Yang, Shuo and Xie, Donglin and Zhang, Yanlin}, |
| journal={arXiv preprint arXiv:2604.05774}, |
| year={2026} |
| } |
| ``` |
|
|
| ## Reproducing |
|
|
| To rebuild and publish the normalized `eukaryote_cls` configuration from the pinned source revision, run: |
|
|
| ```bash |
| python scripts/create_eukaryote_cls.py --source-revision 18f68fabbcd43e8c227869a833027c969b39d7fe --output-dataset HuggingFaceBio/genomeqa --publish |
| ``` |
|
|
| The script preserves the source rows, converts `metadata` from Python dictionary strings to JSON, and casts the |
| columns to the `mcq` feature schema. It publishes the Parquet file, this card, and the builder script in one commit. |
|
|
| ### mcq_carbon |
| |
| To rebuild and publish `mcq_carbon` from the pinned source revision, run: |
|
|
| ```bash |
| python scripts/create_mcq_carbon.py --source-revision ae30ecec16a7669f342f7d87439552142cdebe40 --output-dataset HuggingFaceBio/genomeqa --publish |
| ``` |
|
|
| The script publishes the Parquet file, this card, and the builder script together. |
|
|
| ### mcq_carbon_600 |
|
|
| To rebuild and publish `mcq_carbon_600` from the pinned source revision, run: |
|
|
| ```bash |
| python scripts/create_mcq_carbon_600.py --source-revision c0a7d5b8ccfe4c0d64bc3ff5dd171ff4365c16f6 --output-dataset HuggingFaceBio/genomeqa --seed 42 --publish |
| ``` |
|
|
| The script publishes the Parquet file, this card, and the builder script together. |
|
|
