README.md

---
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
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  - name: answer_index
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  - name: answer_text
    dtype: string
  - name: metadata
    dtype: string
  splits:
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    num_examples: 2600
  download_size: 828753
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  - name: subtask
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  - name: question
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  - name: options
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  - name: answer_text
    dtype: string
  - name: metadata
    dtype: string
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- config_name: mcq_carbon
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  - name: subtask
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  - name: question
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  - name: options
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  - name: answer_text
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  - name: metadata
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- config_name: mcq_carbon_600
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  - name: subtask
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  - name: question
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  - name: options
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  - name: answer_text
    dtype: string
  - name: metadata
    dtype: string
  splits:
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    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
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  - name: answer_text
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  - name: metadata
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- config_name: binary_600
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  - name: answer_text
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  - name: metadata
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- config_name: mcq_600
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  - name: metadata
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- config_name: binary_eukaryote
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  - name: options
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  - name: answer_text
    dtype: string
  - name: metadata
    dtype: string
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    num_examples: 170
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- config_name: binary_prokaryote
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- config_name: mcq_eukaryote
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- config_name: mcq_prokaryote
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  - name: subtask
    dtype: string
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    dtype: string
  - name: metadata
    dtype: string
  splits:
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    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.