Datasets:
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:
- enhancer and promoter identification;
- splice-site identification;
- taxonomic classification;
- histone-mark and chromatin-state prediction;
- transcription-factor binding-site prediction; and
- transcription-factor motif prediction.
Source and construction
This repository repackages the benchmark introduced in GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding (arXiv: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.
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.
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 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 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
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
@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:
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:
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:
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.