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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.
If you use Jaeger in your work, please consider citing its repository:
Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences
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.
# setup bioconda
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict
# create conda environment and install jaeger
conda create -n jaeger python=3.9 pip jaeger-bio
# activate environment
conda activate jaeger
Test the installation with test data
# download test data
wget https://raw.githubusercontent.com/Yasas1994/Jaeger/main/test/bin.460.fna
# run jaeger on test data
Jaeger -i bin.460.fna -o outpath --progressbar
If you have a NVIDIA GPU on the system, and jaeger fails to detect it, try these steps.
- If you are on a HPC check whether cuda-toolkit is available as a module. (Skip this step if you are trying this out on your PC)
module avail
angsd/0.937 boost/1.71.0 clang/14.0.4 fastp/0.23.1 gcc/13.2.0 julia/1.9.2 modeller/9.23 proj/7.0.1 structure/2.3.4 vcftools/0.1.16
autodockvina/1.1.2 boost/1.79.0 clang/17.0.5 fastqc/0.11.9 hdf5/1.12.1 kalign/1.04 mrbayes/3.2.7 r/4.1.1 superlu-dist/8.1.2
bamutil/1.0.15 bowtie/2.4.2 colmap/3.8 fgsl/1.5.0 hdf5/1.14.0 likwid/5.2.0 openmpi/4.1.1 r/4.3.1 superlu-dist/8.2.0
baypass/2.2 bwa/0.7.17 cuda/11.4 fsl/6.0.2 hhsuite/3.3.0 likwid/5.2.1 openpmix/3.1.5 samtools/1.12 superlu/4.3
bcftools/1.15 cdhit/4.8.1 cuda/11.7 gams/36.2.0 I-TASSER/5.1 mathematica/13.2.1 petsc-real/3.18.1 singularity/3.10.0 transdecoder/5.7.0
bedtools/2.30.0 ceres-solver/2.1.0 cuda/12.0.0 gcc/12.2.0
If so, load it
module load cuda/12.0.0
- Next, check whether the NVIDIA GPU driver is properly configured.
nvidia-smi
Above command returns the following output if everything is properly set-up. You can also determine the cuda version from it. For example here it is 11.7 (for step 3)
Mon Apr 8 14:26:43 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.171.04 Driver Version: 535.171.04 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce GTX 1660 Ti Off | 00000000:01:00.0 Off | N/A |
| N/A 47C P8 2W / 80W | 6MiB / 6144MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 2196 G /usr/lib/xorg/Xorg 4MiB |
+---------------------------------------------------------------------------------------+
Check whether Jaeger detects the GPU now.
If that fails you will have to manually configure the conda environment as shown in step 3.
-
- cuda-toolkit for cuda>=11.1 can be found here https://anaconda.org/nvidia/cuda-toolkit
Following example shows the installation process for cuda=11.3.0. Simply change the version number on the second "nvidia/label/cuda-11.x.x" command to install a different version
libcudnn_cnn_infer.so.8
# create a conda environment
conda create -n jaeger -c conda-forge -c bioconda -c defaults python=3.9 pip
# cudatoolkit and cudnn
conda install -n jaeger -c "nvidia/label/cuda-11.3.0" cudatoolkit=11
conda install -n jaeger -c conda-forge cudnn
# install jaeger
conda install -n jaeger -c conda-forge -c bioconda -c defaults jaeger-bio
# activate environment
conda activate jaeger
More information on properly setting setting up tensorflow can be found here
# create a conda environment and activate
conda create -n jaeger -c conda-forge -c bioconda -c defaults python=3.9 pip
conda activate jaeger
#install jaeger
pip install jaeger-bio
# install build dependencies
brew install autoconf automake libtool
# clone the git repository
git clone https://github.com/Yasas1994/Jaeger.git
# create a conda environment and activate
conda create -c conda-forge -c apple -c bioconda -c defaults -n jaeger python=3.9 pip
conda activate jaeger
# install jaeger
cd Jaeger
pip install .
Once the environment is properly set up, using Jaeger is straightforward. The program can accept both compressed and uncompressed .fasta files containing the contigs as input. It will output a table containing the predictions and various statistics calculated during runtime.
Jaeger -i input_file.fasta -o output_dir --batch 128
We provide a new program that allows users to automatically run multiple instances of Jaeger on several GPUs allowing maximum utilization of state-of-the-art hardware. This program accepts a file with a list of paths to all input FASTA files. --ngpu flag can be used to set the number of GPUs at your disposal. --maxworkers flag can be used to set the number of samples that should be processed parallaly per GPU. All other arguments remains similar to 'Jaeger' program.
# to generate a list of fasta files in a dir
ls ./files/*.fna | xargs realpath > input_file_list
# to process eight samples in parallel on two GPUs
Jaeger_parallel -i input_file_list -o output_dir --batch 128 --maxworkers 4 --ngpu 2
You can control the number of parallel computations using this parameter. By default it is set to 96. If you run into OOM errors, please consider setting the --bactch option to a lower value. for example 96 is good enough for a graphics card with 4 Gb of memory.
All predictions are summarized in a table located at output_dir/<input_file>_default.jaeger.tsv
┌───────────────────────────────────┬────────┬────────────┬─────────┬───┬─────────────┬────────────────┬──────────────────┬───────────────┐
│ contig_id ┆ length ┆ prediction ┆ entropy ┆ … ┆ Archaea_var ┆ window_summary ┆ terminal_repeats ┆ repeat_length │
╞═══════════════════════════════════╪════════╪════════════╪═════════╪═══╪═════════════╪════════════════╪══════════════════╪═══════════════╡
│ NODE_1109_length_9622_cov_23.163… ┆ 9622 ┆ Phage ┆ 0.43 ┆ … ┆ 0.143 ┆ 1V1n2V ┆ null ┆ null │
│ NODE_1181_length_9275_cov_26.864… ┆ 9275 ┆ Phage ┆ 0.327 ┆ … ┆ 0.504 ┆ 4V ┆ null ┆ null │
│ NODE_123_length_36569_cov_24.228… ┆ 36569 ┆ Phage ┆ 0.503 ┆ … ┆ 1.554 ┆ 9V1n7V ┆ null ┆ null │
│ NODE_149_length_32942_cov_23.754… ┆ 32942 ┆ Phage ┆ 0.458 ┆ … ┆ 3.229 ┆ 3V1n1n11V ┆ null ┆ null │
│ NODE_231_length_24276_cov_21.832… ┆ 24276 ┆ Phage ┆ 0.502 ┆ … ┆ 1.467 ┆ 1V1n3V1n5V ┆ null ┆ null │
└───────────────────────────────────┴────────┴────────────┴─────────┴───┴─────────────┴────────────────┴──────────────────┴───────────────┘
This table provides information about various contigs in a metagenomic assembly. Each row represents a single contig, and the columns provide information about the contig's ID, length, the number of windows identified as prokaryotic, viral, eukaryotic, and archaeal, the prediction of the contig (Phage or Non-phage), the score of the contig for each category (bacterial, viral, eukaryotic and archaeal), and a summary of the windows. The table can be used to identify potential phage sequences in the metagenomic assembly based on the prediction column. The score columns can be used to further evaluate the confidence of the prediction and the window summary column can be used to understand the count of windows that contributed to the final prediction.
Jaeger --help
## Jaeger 1.1.30 (yet AnothEr phaGe idEntifier) Deep-learning based bacteriophage discovery
https://github.com/Yasas1994/Jaeger.git
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
path to input file
-o OUTPUT, --output OUTPUT
path to output directory
--fsize [FSIZE] length of the sliding window (value must be 2^n). default:2048
--stride [STRIDE] stride of the sliding window. default:2048 (stride==fsize)
-m {default,experimental_1,experimental_2}, --model {default,experimental_1,experimental_2}
select a deep-learning model to use. default:default
-p, --prophage extract and report prophage-like regions. default:False
-s [SENSITIVITY], --sensitivity [SENSITIVITY]
sensitivity of the prophage extraction algorithm (between 0 - 4). default: 1.5
--lc [LC] minimum contig length to run prophage extraction algorithm. default: 500000 bp
--batch [BATCH] parallel batch size, set to a lower value if your gpu runs out of memory. default:96
--workers [WORKERS] number of threads to use. default:4
--getalllogits return position-wise logits for each prediction window as a .npy file
--usecutoffs use cutoffs to obtain the class prediction
--cpu ignore available gpus and explicitly run jaeger on cpu. default: False
--virtualgpu create and run jaeger on a virtualgpu. default: False
--physicalid [PHYSICALID]
sets the default gpu device id (for multi-gpu systems). default:0
--getalllabels get predicted labels for Non-Viral contigs. default:False
Misc. Options:
-v, --verbose Verbosity level : -v warning, -vv info, -vvv debug, (default info)
-f, --overwrite Overwrite existing files
--progressbar show progress bar
Jaeger can be integrated into python scripts using the jaegeraa python library as follows. currently the predict function accepts 4 different input types.
- Nucleotide sequence -> str
- List of Nucleotide sequences -> list(str,str,..)
- python file object -> (io.TextIOWrapper)
- python generator object that yields Nucleotide sequences as str (types.GeneratorType)
- Biopython Seq object
from jaegeraa.api import Predictions
model=Predictor()
predictions=model.predict(input,stride=2048,fragsize=2048,batch=100)
model.predict()returns a dictionary of lists in the following format
{'contig_id': ['seq_0', 'seq_1'],
'length': [19000, 10503],
'#num_prok_windows': [0, 0],
'#num_vir_windows': [9, 0],
'#num_fun_windows': [0, 5],
'#num_arch_windows': [0, 0],
'prediction': ['Phage', 'Non-phage'],
'bac_score': [-1.9552012549506292, -1.9441368103027343],
'vir_score': [6.6312947273254395, -3.097817325592041],
'fun_score': [-5.712721400790745, -0.6870137214660644],
'arch_score': [-2.4369852013058133, -0.8941479325294495],
'window_summary': ['9V', '5n']}
This dictionary can be easily converted to a pandas dataframe using DataFrame.from_dict() method
import pandas as pd
df = DataFrame.from_dict(predictions)- The program expects the input file to be in .fasta format.
- The program uses a sliding window approach to scan the input sequences, so the stride argument determines how far the window will move after each scan.
- The batch argument determines how many sequences will be processed in parallel.
- The program is compatible with both CPU and GPU. By default, it will run on the GPU, but if the --cpu option is provided, it will use the specified number of threads for inference.
- The program uses a pre-trained neural network model for phage genome prediction.
- The --getalllabels option will output predicted labels for Non-Viral contigs, which can be useful for further analysis. It's recommended to use the output of this program in conjunction with other methods for phage genome identification.
Jaeger -p -i NC_002695.fna -o outdir
The outdir will contain the following files
|____Escherichia_coli_O157-H7_prophages
| |____plots
| | |____NC_002695_Escherichia_coli_O157-H7_jaeger.pdf
| |____prophages_jaeger.tsv
|____Escherichia_coli_O157-H7_jaeger.log
|____Escherichia_coli_O157-H7_default_jaeger.tsv
users can find the following visulaization in the plots directory
list of prophage coordinates can be found in prophages_jaeger.tsv
┌─────────────┬────────────┬──────────┬──────────┬───┬──────────┬────────┬────────────┬────────────┐
│ contig_id ┆ alignment_ ┆ identiti ┆ identity ┆ … ┆ gc% ┆ reject ┆ attL ┆ attR │
│ ┆ length ┆ es ┆ ┆ ┆ ┆ ┆ ┆ │
╞═════════════╪════════════╪══════════╪══════════╪═══╪══════════╪════════╪════════════╪════════════╡
│ NC_002695 ┆ 16.0 ┆ 16.0 ┆ 1.0 ┆ … ┆ 0.435049 ┆ false ┆ GCACCATTTA ┆ GCACCATTTA │
│ Escherichia ┆ ┆ ┆ ┆ ┆ ┆ ┆ AATCAA ┆ AATCAA │
│ coli O157-… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ NC_002695 ┆ 15.0 ┆ 15.0 ┆ 1.0 ┆ … ┆ 0.493497 ┆ false ┆ GCTTTTTTAT ┆ GCTTTTTTAT │
│ Escherichia ┆ ┆ ┆ ┆ ┆ ┆ ┆ ACTAA ┆ ACTAA │
│ coli O157-… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ NC_002695 ┆ 60.0 ┆ 60.0 ┆ 1.0 ┆ … ┆ 0.511819 ┆ false ┆ TGGCGGAAGC ┆ TGGCGGAAGC │
│ Escherichia ┆ ┆ ┆ ┆ ┆ ┆ ┆ GCAGAGATTC ┆ GCAGAGATTC │
│ coli O157-… ┆ ┆ ┆ ┆ ┆ ┆ ┆ GAACTCTGGA ┆ GAACTCTGGA │
│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ AC… ┆ AC… │
│ NC_002695 ┆ 16.0 ┆ 16.0 ┆ 1.0 ┆ … ┆ 0.499516 ┆ false ┆ TTCTTTATTA ┆ TTCTTTATTA │
│ Escherichia ┆ ┆ ┆ ┆ ┆ ┆ ┆ CCGGCG ┆ CCGGCG │
│ coli O157-… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
│ NC_002695 ┆ 14.0 ┆ 14.0 ┆ 1.0 ┆ … ┆ 0.529465 ┆ false ┆ CGTCATCAAG ┆ CGTCATCAAG │
│ Escherichia ┆ ┆ ┆ ┆ ┆ ┆ ┆ TGCA ┆ TGCA │
│ coli O157-… ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
└─────────────┴────────────┴──────────┴──────────┴───┴──────────┴────────┴────────────┴────────────┘
You can use phage_contig_annotator to annotate and visualize Jaeger predictions.
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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