MicroGenomer

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A genome language foundation model for microbial genome representation

MicroGenomer 최신버젼 다운로드

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# MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction
## Introduction
MicroGenomer is a foundation model for transferable microbial genome representations, enabling multi-scale genomic comprehension and ecophysiological trait prediction. It adopts a hierarchical training strategy that integrates large-scale genomic sequence pre-training (234.5 billion base pairs), domain-specific mid-training with the GTDB-curated marker gene set, and task-specific post-training. Surpassing existing gene-scale encoders, MicroGenomer generates robust embeddings at the whole-genome scale.

## Schematic Diagram
<div style="text-align: center;">
    <img src="img/Fig1.jpg" alt="fig1" width="800">
</div>
Figure 1. Overview of MicroGenomer.

## GPU Hardware Requirements
Since the model leverages FlashAttention for computational acceleration to enhance the efficiency of genomic representation extraction, we recommend the following hardware configurations to ensure optimal model performance:
- NVIDIA GPUs with Ampere architecture or newer are required (e.g., RTX 30/40 series consumer-grade GPUs, A10/A100/A800 professional-grade GPUs). 
- A minimum of 16GB VRAM is recommended (32GB or higher for batch processing of genomic data).

## Quick Start
### Download the GitHub Repository
[Download](https://github.com/BGIResearch/MicroGenomer/archive/refs/heads/main.zip) the GitHub repository and extract the files to a designated folder.

### Data Description
The input data required for MicroGenomer model inference and genomic representation extraction is formatted as a comma-separated values (CSV) file, which exclusively contains gene sequences with annotated Coding Sequences (CDS) — the core genomic regions that encode functional proteins in microbial genomes. The input data format is as follows:
- `genome_id`: Unique identifier for the current genome
- `aa_seq`: DNA sequence corresponding to CDS in the current genome
- `unique_id`: Unique ID for identifying the DNA sequence

Example:
```csv
unique_id,aa_seq,genome_id
RS_GCF_013393365.1@TIGR00168,TTGCCGTCCGTAG...GTAGTAAATAA,RS_GCF_013393365.1
RS_GCF_013393365.1@TIGR00382,TTGGCAAAAGATA...TGAAATCGTAA,RS_GCF_013393365.1
...
```

## Installation
```bash
# Create a Conda Python environment
conda create -n MicroGenomer python=3.10
conda activate MicroGenomer
git clone https://github.com/BGIResearch/MicroGenomer.git
cd MicroGenomer
pip install -r requirements.txt
```

## Usage
### Foundation Model Weights
* Download the weights for [MicroGenomer](https://huggingface.co/sunhaotong0605/MicroGenomer-470M/tree/main).
* Place the `MicroGenomer-470M` and `downstream_tasks` folders in the `weights/` directory.

### Run Inference for Different Downstream Tasks
Six downstream tasks available: maximum growth rate, oxygen tolerance, salinity tolerance, optimal pH, optimal temperature and probiotic prediction. It also supports extracting embeddings.
```bash
bash run.sh \
    --input_path '/path/to/input/' \
    --output_dir '/path/to/output/' \
    [--task 'downstream_task'] \ # Options: test_growth, test_oxygen, test_salinity, test_pH, test_temperature, test_probiotic, extract_embed or none
    [--level 'level_of_probiotic']  \ # For test_probiotic task only. Options: family, genus.
```
* `--input_path`: Path to the input file/folder.
* `--output_dir`: Path for saving output files.
* `--task`: Downstream task option.
* `--level`: Level of probiotic prediction. Default is family.

The input path can be a single CSV file or a folder. If it is a folder, all CSV files in that folder will be processed in batches. If the task parameter is extract_embed or is empty, the model will only extract embedding and will not perform any specific downstream tasks.

### Docker Deployment
* First, pull the Docker image:
```bash
docker pull sunhaotong0605/microgenomer:0.1
```
* Run Inference for Different Downstream Tasks:
```bash
docker run --rm --gpus all \
    -v /path/to/input/:/data/input \
    -v /path/to/output/:/data/output \
    -e INPUT_PATH="/data/input" \
    -e OUTPUT_PATH="/data/output" \
    sunhaotong0605/microgenomer:0.1 \
    sh -c 'bash run.sh --input_path $INPUT_PATH --output_dir $OUTPUT_PATH --task test_growth'
```
Please replace `/path/to/input/`, `/path/to/output/`, and the downstream task option(e.g., `test_growth`) with your actual paths and desired task.

## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) for more details.

## Citation
Kang Q, Guo Y, Hu B, et al. MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction. bioRxiv. doi: https://doi.org/10.64898/2025.12.28.696777.

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