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Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences

Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

📚 For detailed installation instructions, usage guides, and troubleshooting, please visit the documentation.

Citing Jaeger

If you use Jaeger in your work, please consider citing its preprint:

• Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences Yasas Wijesekara, Ling-Yi Wu, Rick Beeloo, Piotr Rozwalak, Ernestina Hauptfeld, Swapnil P. Doijad, Bas E. Dutilh, Lars Kaderali bioRxiv, 2024.09.24.612722

To cite the code itself:

• Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences https://doi.org/10.5281/zenodo.20534106

• Installing Jaeger
  • One-liner install script
  • Bioconda
  • PyPI
  • git (main branch)
  • containers
• Downloading models

Installing Jaeger

option 1 : One-liner install script (recommended)

The easiest way to install Jaeger. This script auto-detects your platform (GPU, CPU, or Apple Silicon) and sets up the environment for you.

curl -sSL https://raw.githubusercontent.com/MGXlab/Jaeger/main/install.sh | bash

option 2 : bioconda

The performance of the Jaeger workflow can be significantly increased by utilizing GPUs. To enable GPU support, the CUDA Toolkit and cuDNN library must be accessible to conda.

# create conda environment and install jaeger
mamba create -n jaeger -c bioconda jaeger-bio==1.26

# activate environment
conda activate jaeger

Test the installation with test data

jaeger health

option 3 : Installing from PyPI (recommended)

# create a conda environment and activate
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.11,<3.14" pip
conda activate jaeger

# OR create a virtual environment using venv
python3 -m venv jaeger
source jaeger/bin/activate

# to install jaeger with GPU support
pip install jaeger-bio[gpu]

# to install without GPU support
pip install jaeger-bio[cpu]

# to install on a Mac(arm)
pip install jaeger-bio[darwin-arm]

# test the installation
jaeger health

option 4 : Installing from git (main branch)

# create a conda environment and activate
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.11,<3.14" pip
conda activate jaeger

# OR create a virtual environment using venv
python3 -m venv jaeger
source jaeger/bin/activate

# install jaeger

# to install with GPU support
pip install --no-cache-dir "jaeger-bio[gpu] @ git+https://github.com/MGXlab/Jaeger@main"

# to install without GPU support
pip3 install --root-user-action=ignore --no-cache-dir "jaeger-bio[cpu] @ git+https://github.com/MGXlab/Jaeger@main"

# to install on a Mac(arm)
pip3 install --root-user-action=ignore --no-cache-dir "jaeger-bio[darwin-arm] @ git+https://github.com/MGXlab/Jaeger@main"

# test the installation
jaeger health

option 5 : Apptainer (Singularity)

If you're using Apptainer on a cluster, it's recommended to build the container on your local machine and then transfer it to the cluster.

# get the container def
wget -O jaeger_singularity.def https://raw.githubusercontent.com/Yasas1994/Jaeger/main/singularity/jaeger_singularity.def
# get the configuration file
wget -O config.json https://raw.githubusercontent.com/Yasas1994/Jaeger/main/src/jaeger/data/config.json

# to build the container
apptainer build jaeger.sif singularity/jaeger_singularity.def

# test container
apptainer run --nv jaeger.sif jaeger --help

# test the installation
apptainer run --nv jaeger.sif jaeger health

# list jaeger models available for download
apptainer run --nv jaeger.sif jaeger download --list
# download jaeger models
apptainer run --nv jaeger.sif jaeger download --model_name jaeger_38341_1.4M_fragment --path /path/to/save/model --config /path/to/config.json

# run jaeger
apptainer run --nv jaeger.sif jaeger predict --model jaeger_38341_1.4M_fragment --config /path/to/config.json -i /path/to/input.fasta -o /path/to/save/results

Downloading models

Starting from version 1.26.0, users will need to download the new models separately after installing Jaeger. The bundled default model is deprecated and uses the legacy prediction workflow; modern SavedModels (e.g., jaeger_38341_1.4M_fragment) are recommended.

Use the --list flag to print out all models available for download

jaeger download --list

Then to download the model and add it to the model path run

jaeger download --path /path/to/store/models --model_name jaeger_38341_1.4M

If you decide to change the model path later, or if you have a directory with newly trained/tuned models register the path

jaeger register-models --path /new/model/path

Visualizing predictions

You can use phage_contig_annotator to annotate and visualize Jaeger predictions.

Acknowledgements

This work was supported by the European Union's Horizon 2020 research and innovation program, under the Marie Skłodowska-Curie Actions Innovative Training Networks grant agreement no. 955974 (VIROINF), the European Research Council (ERC) Consolidator grant 865694

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