LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language.

• 2024/10/01: optimized embedding inference code: src/get_embedding.py
• 2024/08/01: add checkpoint=17600000, location: checkpoint-step17600000
• 2024/07/24: feature: add continue training when failure

Fig. 1 The workflow of LucaOne.

Fig. 2 The data and tasks for pre-training LucaOne, and T-SNE on four embedding models.

Fig. 3 Downstream task network with three input types and results comparison of 8 verification tasks.

sudo yum update
sudo yum install git-all

sudo apt-get update
sudo apt install git-all

wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh

sh Anaconda3-2022.05-Linux-x86_64.sh

source ~/.bashrc

conda create -n lucaone python=3.9.13

conda activate lucaone

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

You can use the project: LucaOneApp Github or LucaOneApp FTP for embedding inference. For details, please refer to the README of the LucaOneApp project.

The project will download automatically LucaOne Trained-CheckPoint from FTP.

This project: LucaOneTasks Github or LucaOneTasks FTP is all the downstream tasks used in our paper(based on LucaOne's Embedding), and you can use this project to run other tasks, please refer to the README of this project.

Pretraining Dataset FTP: Dataset for LucaOne

Copy the dataset from http://47.93.21.181/lucaone/PreTrainingDataset/dataset/lucagplm into the directory: ./dataset/

The training dataset(dataset/lucagplm/v2.0/train/) whose file names start with '2023112418163521' are gene data(DNA RNA), and those that start with '2023112314061479' are protein data.

The validation dataset(dataset/lucagplm/v2.0/dev/) whose file names start with '2023112418224620' are gene data(DNA RNA), and those that start with '2023112314080544' are protein data.

The testing dataset(dataset/lucagplm/v2.0/test/) whose file names start with '2023112418231445' are gene data(DNA RNA), and those that start with '2023112314083364' are protein data.

Notice
If you want to train individual nucleic acid or protein LucaOne(LucaOne-Gene or LucaOne-Prot), please separate the datasets as described above.

Training scripts are under the directory src/training, including 4 shell scripts:
run_multi_v2.0.sh: nucleic acid(DNA RNA) and protein mixed training with 10 pre-training tasks.
run_multi_mask_v2.0.sh: nucleic acid(DNA RNA) and protein mixed training with only 2 mask pre-training tasks.
run_multi_v2.0_gene.sh: individual nucleic acid training with 3 pre-training tasks.
run_multi_v2.0_prot.sh: individual protein training with 7 pre-training tasks.

run_multi_v2.0_continue.sh: continue training when failure.

FTP:
Pre-training data, code, and trained checkpoint of LucaOne, embedding inference code, downstream validation tasks data & code, and other materials are available: FTP.

Details:

The LucaOne's model code is available at: LucaOne Github or LucaOne.

The trained-checkpoint files are available at: TrainedCheckPoint.

LucaOne's representational inference code is available at: LucaOneApp Github or LucaOneApp.

The project of 8 downstream tasks is available at: LucaOneTasks Github or LucaOneTasks.

The pre-training dataset of LucaOne is opened at: PreTrainingDataset.

The datasets of downstream tasks are available at: DownstreamTasksDataset .

The trained models of downstream tasks are available at: DownstreamTasksTrainedModels .

Other supplementary materials are available at: Others .

Yong He, Zhaorong Li, Yongtao Shan, Yanhong Wei, Yuan-Fei Pan Pan Fang,

@article {LucaOne,
author = {Yong He and Pan Fang and Yongtao Shan and Yuanfei Pan and Yanhong Wei and Yichang Chen and Yihao Chen and Yi Liu and Zhenyu Zeng and Zhan Zhou and Feng Zhu and Edward C. Holmes and Jieping Ye and Jun Li and Yuelong Shu and Mang Shi and Zhaorong Li},
title = {LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language},
elocation-id = {2024.05.10.592927},
year = {2024},
doi = {10.1101/2024.05.10.592927},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2024/05/14/2024.05.10.592927},
eprint = {https://www.biorxiv.org/content/early/2024/05/14/2024.05.10.592927.full.pdf},
journal = {bioRxiv}
}