--- 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.