README.md

metadata

license: mit
task_categories:
  - text-classification
tags:
  - biology
  - genomics
  - long-context
papers: 2502.07272
configs:
  - config_name: drug_resistence_prediction
    data_files:
      - split: train
        path: drug_resistence_prediction/train.parquet
      - split: test
        path: drug_resistence_prediction/test.parquet
  - config_name: fitness_prediction_Ammonium-chloride_N
    data_files:
      - split: train
        path: fitness_prediction_Ammonium-chloride_N/train.parquet
      - split: test
        path: fitness_prediction_Ammonium-chloride_N/test.parquet
  - config_name: fitness_prediction_Cisplatin_stress
    data_files:
      - split: train
        path: fitness_prediction_Cisplatin_stress/train.parquet
      - split: test
        path: fitness_prediction_Cisplatin_stress/test.parquet
  - config_name: fitness_prediction_D-Alanine_N
    data_files:
      - split: train
        path: fitness_prediction_D-Alanine_N/train.parquet
      - split: test
        path: fitness_prediction_D-Alanine_N/test.parquet
  - config_name: fitness_prediction_L-Arabinose_C
    data_files:
      - split: train
        path: fitness_prediction_L-Arabinose_C/train.parquet
      - split: test
        path: fitness_prediction_L-Arabinose_C/test.parquet
  - config_name: fitness_prediction_L-Histidine_nutrient
    data_files:
      - split: train
        path: fitness_prediction_L-Histidine_nutrient/train.parquet
      - split: test
        path: fitness_prediction_L-Histidine_nutrient/test.parquet
  - config_name: fitness_prediction_LB-10C
    data_files:
      - split: train
        path: fitness_prediction_LB-10C/train.parquet
      - split: test
        path: fitness_prediction_LB-10C/test.parquet
  - config_name: fitness_prediction_LB-20C
    data_files:
      - split: train
        path: fitness_prediction_LB-20C/train.parquet
      - split: test
        path: fitness_prediction_LB-20C/test.parquet
  - config_name: fitness_prediction_LB-30C
    data_files:
      - split: train
        path: fitness_prediction_LB-30C/train.parquet
      - split: test
        path: fitness_prediction_LB-30C/test.parquet
  - config_name: fitness_prediction_LB-pH6
    data_files:
      - split: train
        path: fitness_prediction_LB-pH6/train.parquet
      - split: test
        path: fitness_prediction_LB-pH6/test.parquet
  - config_name: fitness_prediction_LB-pH8
    data_files:
      - split: train
        path: fitness_prediction_LB-pH8/train.parquet
      - split: test
        path: fitness_prediction_LB-pH8/test.parquet
  - config_name: fitness_prediction_Min-media-glucose_nutrient
    data_files:
      - split: train
        path: fitness_prediction_Min-media-glucose_nutrient/train.parquet
      - split: test
        path: fitness_prediction_Min-media-glucose_nutrient/test.parquet
  - config_name: fitness_prediction_Pyruvate_C
    data_files:
      - split: train
        path: fitness_prediction_Pyruvate_C/train.parquet
      - split: test
        path: fitness_prediction_Pyruvate_C/test.parquet
  - config_name: fitness_prediction_perchlorate_stress
    data_files:
      - split: train
        path: fitness_prediction_perchlorate_stress/train.parquet
      - split: test
        path: fitness_prediction_perchlorate_stress/test.parquet
  - config_name: gene_classification_bacteria
    data_files:
      - split: train
        path: gene_classification_bacteria/train.parquet
      - split: test
        path: gene_classification_bacteria/test.parquet
  - config_name: taxonomic_classification_mixed_marker
    data_files:
      - split: train
        path: taxonomic_classification_mixed_marker/train.parquet
      - split: test
        path: taxonomic_classification_mixed_marker/test.parquet
  - config_name: taxonomic_classification_random_fragment
    data_files:
      - split: train
        path: taxonomic_classification_random_fragment/train.parquet
      - split: test
        path: taxonomic_classification_random_fragment/test.parquet
  - config_name: taxonomic_classification_ssu
    data_files:
      - split: train
        path: taxonomic_classification_ssu/train.parquet
      - split: test
        path: taxonomic_classification_ssu/test.parquet

Prokaryotic Gener Tasks

Paper | GitHub

Prokaryotic Gener Tasks is a suite of biologically meaningful benchmark tasks in the prokaryotic domain, introduced as part of the GENERator project. GENERator is a generative genomic foundation model for long-context DNA modeling, pre-trained on expansive DNA datasets derived from the RefSeq database.

Dataset Summary

This collection includes various downstream tasks designed to evaluate the sequence understanding (classification and regression) capabilities of genomic foundation models in the prokaryotic domain. The benchmark includes:

• Drug Resistance Prediction: Predicting the resistance profiles of prokaryotic sequences to various drugs.
• Fitness Prediction: Estimating the biological fitness of organisms under diverse environmental conditions, including temperature variations (LB-10C, LB-20C, LB-30C), pH levels (pH6, pH8), and nutrient stresses (Ammonium-chloride, Pyruvate, etc.).
• Gene Classification: Classification of genes within the bacterial domain.
• Taxonomic Classification: Classifying sequences into phylogenetic categories using various markers such as Small Subunit (SSU) sequences, random fragments, or mixed markers.

Usage

To evaluate a model on these tasks using the official GENERator implementation, you can use the following command structure:

# Using single GPU for sequence understanding
python src/tasks/downstream/sequence_understanding.py \
    --model_name GenerTeam/GENERator-eukaryote-1.2b-base \
    --dataset_name GenerTeam/prokaryotic-gener-tasks \
    --subset_name gene_classification_bacteria \
    --batch_size 32 \
    --problem_type classification \
    --main_metrics accuracy

Replace --subset_name with one of the configurations (e.g., taxonomic_classification_ssu or fitness_prediction_LB-10C) and adjust --problem_type (classification or regression) as appropriate.

Citation

@misc{wu2025generator,
      title={GENERator: A Long-Context Generative Genomic Foundation Model}, 
      author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang},
      year={2025},
      eprint={2502.07272},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.07272}, 
}