jaeger-bio 1.26.4

A quick and precise pipeline for detecting phages in sequence assemblies.

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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.

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Citing Jaeger

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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

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

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Installing Jaeger

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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

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Downloading models

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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

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Visualizing predictions

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You can use phage_contig_annotator to annotate and visualize Jaeger predictions.

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Acknowledgements

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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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