DeePaC  0.12.2
Predicting pathogenic potentials of novel DNA reads with reverse-complement neural networks.
DeePaC Documentation


DeePaC is a python package and a CLI tool for predicting labels (e.g. pathogenic potentials) from short DNA sequences (e.g. Illumina reads) with interpretable reverse-complement neural networks. For details, see our preprint on bioRxiv: and the paper in Bioinformatics: For details regarding the interpretability functionalities of DeePaC, see the preprint here:

Documentation can be found here: See also the main repo here:



Basic version of DeePaC comes with built-in models trained to predict pathogenic potentials of NGS reads originating from novel bacteral species. If you want to predict pathogenicity of novel strains of known species, try the DeePaC-strain plugin available here:


If you want to detect novel human viruses, try the DeePaC-vir plugin:


If you want to run the predictions in real-time during an Illumina sequencing run, try DeePaC-Live:


Recommended: set up an environment

We recomment setting up an isolated conda environment:

conda create -n my_env
conda activate my_env

or, alternatively, a virtualenv:

virtualenv --system-site-packages my_env
source my_env/bin/activate

With conda (recommended)

install with bioconda

You can install DeePaC with bioconda. Set up the bioconda channel first, and then:

conda install deepac

If you want to install the plugins as well, use:

conda install deepacvir deepacstrain

With pip

You can also install DeePaC with pip:

pip install deepac

Note: TensorFlow 2.0 is not yet supported.

If you want to install the plugins, use:

pip install deepacvir deepacstrain

GPU support

To use GPUs, you need to install the GPU version of TensorFlow. In conda, install tensorflow-gpu before deepac:

conda remove tensorflow
conda install tensorflow-gpu
conda install deepac

If you're using pip, you need to install CUDA and CuDNN first (see TensorFlow installation guide for details). Then you can do the same as above:

pip uninstall tensorflow
pip install tensorflow-gpu

Optional: run tests

Optionally, you can run explicit tests of your installation. Note that it may take some time on a CPU.

# Run standard tests
deepac test
# Run quick tests (eg. on CPUs)
deepac test -q
# Test using specific GPUs (here: /device:GPU:0 and /device:GPU:1)
deepac test -g 0 1
# Test explainability and gwpa workflows
deepac test -xp
# Full tests
deepac test -a
# Full quick tests (eg. on GPUs with limited memory)
deepac test -aq


To see help, just use

deepac --help
deepac predict --help
deepac train --help
# Etc.

Basic use: prediction

You can predict pathogenic potentials with one of the built-in models out of the box:

# A rapid CNN (trained on IMG/M data)
deepac predict -r input.fasta
# A sensitive LSTM (trained on IMG/M data)
deepac predict -s input.fasta

The rapid and the sensitive models are trained to predict pathogenic potentials of novel bacterial species. For details, see or

To quickly filter your data according to predicted pathogenic potentials, you can use:

deepac predict -r input.fasta
deepac filter input.fasta input_predictions.npy -t 0.5

Note that after running predict, you can use the input_predictions.npy to filter your fasta file with different thresholds. You can also add pathogenic potentials to the fasta headers in the output files:

deepac filter input.fasta input_predictions.npy -t 0.75 -p -o output-75.fasta
deepac filter input.fasta input_predictions.npy -t 0.9 -p -o output-90.fasta

Advanced use

Config templates

To get the config templates in the current working directory, simply use:

deepac templates


For more complex analyzes, it can be useful to preprocess the fasta files by converting them to binary numpy arrays. Use:

deepac preproc preproc_config.ini

See the config_templates directory of the GitLab repository ( for a sample configuration file.


You can use the built-in architectures to train a new model:

deepac train -r -T train_data.npy -t train_labels.npy -V val_data.npy -v val_labels.npy
deepac train -s -T train_data.npy -t train_labels.npy -V val_data.npy -v val_labels.npy

To train a new model based on you custom configuration, use

deepac train -c nn_train_config.ini

If you train an LSTM on a GPU, a CUDNNLSTM implementation will be used. To convert the resulting model to be CPU-compatible, use deepac convert. You can also use it to save the weights of a model, or recompile a model from a set of weights:

# Save model weights and convert the model to an equivalent with the same architecture and weights.
# Other config parameters can be adjusted
deepac convert model_config.ini saved_model.h5
# Recompile the model
deepac convert saved_model_config.ini saved_model_weights.h5 -w


To evaluate a trained model, use

# Read-by-read performance
deepac eval -r eval_config.ini
# Species-by-species performance
deepac eval -s eval_species_config.ini
# Ensemble performance
deepac eval -e eval_ens_config.ini

See the configs directory for sample configuration files. Note that deepac eval -s requires precomputed predictions and a csv file with a number of DNA reads for each species in each of the classes.

TPU (experimental)

If you want to use a TPU, run DeePaC with the --tpu parameter:

# Test a TPU
deepac --tpu colab test

Intepretability workflows

Filter visualization

To find the most relevant filters and visualize them, use the following minimum workflow:

# Calculate filter and nucleotide contibutions (partial Shapley values) for the first convolutional layer
# using mean-centered weight matrices and "easy" calculation mode
deepac explain fcontribs -m model.h5 -eb -t test_data.npy -N test_nonpatho.fasta -P test_patho.fasta -o fcontribs
# Create filter ranking
deepac explain franking -f fcontribs/filter_scores -y test_labels.npy -p test_predictions.npy -o franking
# Prepare transfac files for filter visualization (weighted by filter contribution)
deepac explain fa2transfac -i fcontribs/fasta -o fcontribs/transfac -w -W fcontribs/filter_scores
# Visualize nucleotide contribution sequence logos
deepac explain xlogos -i fcontribs/fasta -s fcontribs/nuc_scores -I fcontribs/transfac -t train_data.npy -o xlogos

You can browse through other supplementary functionalities and parameters by checking the help:

deepac explain -h
deepac explain fcontribs -h
deepac explain xlogos -h
# etc.

Genome-wide phenotype potential analysis (GWPA)

To find interesting regions of a whole genome, use this workflow to generate nucleotide-resolution maps of predicted phenotype potentials and nucleotide contributions:

# Fragment the genomes into pseudoreads
deepac gwpa fragment -g genomes_fasta -o fragmented_genomes
# Predict the pathogenic potential of each pseudoread
deepac predict -r -a fragmented_genomes/sample1_fragmented_genomes.npy -o predictions/sample1_pred.npy
# Create bedgraphs of mean pathogenic potential at each position of the genome
# Can be visualized in IGV
deepac gwpa genomemap -f fragmented_genomes -p predictions -g genomes_genome -o bedgraph
# Rank genes by mean pathogenic potential
deepac gwpa granking -p bedgraph -g genomes_gff -o granking
# Create bedgraphs of mean nuclotide contribution at each position of the genome
# Can be visualized in IGV
deepac gwpa ntcontribs -m model.h5 -f fragmented_genomes -g genomes_genome -o bedgraph_nt

You can browse through other supplementary functionalities and parameters by checking the help:

deepac gwpa -h
deepac gwpa genomemap -h
deepac gwpa ntcontribs -h
# etc.

Filter enrichment analysis

Finally, you can check for filter enrichment in annotated genes or other genomic features:

# Get filter activations, genome-wide
deepac gwpa factiv -m model.h5 -t fragmented_genomes/sample1_fragmented_genomes.npy -f fragmented_genomes/sample1_fragmented_genomes.fasta -o factiv
# Check for enrichment within annotated genomic features
deepac gwpa fenrichment -i factiv -g genomes_gff/sample1.gff -o fenrichment

Supplementary data and scripts

Datasets are available here: (bacteria) and here: (viruses). In the supplement_paper directory you can find the R scripts and data files used in the papers for dataset preprocessing and benchmarking.

Cite us

If you find DeePaC useful, please cite:

author = {Bartoszewicz, Jakub M and Seidel, Anja and Rentzsch, Robert and Renard, Bernhard Y},
title = "{DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks}",
journal = {Bioinformatics},
year = {2019},
month = {07},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz541},
url = {},
eprint = {},
@article {Bartoszewicz2020.01.29.925354,
author = {Bartoszewicz, Jakub M. and Seidel, Anja and Renard, Bernhard Y.},
title = {Interpretable detection of novel human viruses from genome sequencing data},
elocation-id = {2020.01.29.925354},
year = {2020},
doi = {10.1101/2020.01.29.925354},
publisher = {Cold Spring Harbor Laboratory},
URL = {},
eprint = {},
journal = {bioRxiv}