The official implementation of ViraLM: Empowering Virus Discovery through the Genome Foundation Model

News

🎉 ViraLM is accepted by Bioinformatics!

Contents

• 1. Introduction
• 2. Setup Environment
• 3. Quick Start
• 4. Output explanation
• 5. Citation
• 6. Contact

1. Overview

Viral Language Model (ViraLM) is a Python library for virus identification from metagenomic data. ViraLM employs the latest genome foundation model to capture complex genomic characteristics and is able to distinguish viral genomes from organisms.

2. Setup environment

Note: we suggest you install all the packages using mamba or conda.

# clone the repository to the local
git clone https://github.com/ChengPENG-wolf/ViraLM.git
cd ViraLM

# install and activate environment for ViraLM
conda env create -f viralm.yaml -n viralm
conda activate viralm

# download and setup the model
gdown --id 1EQVPmFbpLGrBLU0xCtZBpwvXrtrRxic1
tar -xzvf model.tar.gz -C .
rm model.tar.gz

3. Quick start

Note: we suggest you run ViraLM on GPU.

Run ViraLM in one command:

python viralm.py [-i INPUT_FA] [-o OUTPUT_PTH] [-d DATABASE_PATH] [-l MINIMUM_LEN] [-t THRESHOLD]

Options

Note: we recommend that MINIMUM_LEN be larger than 500 for reliable performance.

-i, --input INPUT_FA
                        The path of your input fasta file
-o, --output OUTPUT_PTH
			The path of your output diectory
-d --databse DATABASE_PATH        
                        Model directory
-l, --len MINIMUM_LEN
                        predict only for sequence >= len bp (default 500)
-t, --threshold THRESHOLD
                        Threshold to reject (default 0.5).

Example

Prediction on the example file:

export CUDA_VISIBLE_DEVICES=0,1,2,...,n 	# (option) n is the number of GPUs
python viralm.py --input test.fasta --out result --len 500 --threshold 0.5

If you prefer storing your models/databases in a different location, then you can use -d or --db parameter:

python viralm.py --input test.fasta --out result -d /path/database/downloaded --len 500 --threshold 0.5

4. Output explanation

1. OUTPUT_PTH/result_INPUT_FA_NAME.csv:

seq_name                                             prediction   virus_score           
--------------------------------------------------   ----------   -----------------
IMGVR_UViG_2531839437_000001|2531839437|2531897698   virus        0.845030747354031      
IMGVR_UViG_2529292823_000001|2529292823|2529351314   virus        0.844078302383422
IMGVR_UViG_2531839021_000001|2531839021|2531843197   virus        0.501383100927341
…

This tabular file lists all the inputs and ViraLM's prediction on each input:

• seq_name: The identifier of the sequence in the input FASTA file.
• prediction: The final prediction of the input sequence, virus or non-virus.
• virus_score: A value in [0, 1.0], indicates the likelihood of the input sequence being a viral sequence. The larger the more likely to be a virus.

2. OUTPUT_PTH/virus_INPUT_FA_NAME.fasta:

This FASTA file contains all the identified virus sequences that have virus_scores larger than THRESHOLD.

5. Citation

If you use ViraLM in your research, please kindly cite our paper:

@article{peng2024viralm,
  title={ViraLM: Empowering Virus Discovery through the Genome Foundation Model},
  author={Peng, Cheng and Shang, Jiayu and Guan, Jiaojiao and Wang, Donglin and Sun, Yanni},
  journal={Bioinformatics},
  pages={btae704},
  year={2024},
  publisher={Oxford University Press}
}

6. Contact

If you have any questions, please email us: cpeng29-c@my.cityu.edu.hk