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    "info": {
        "author": "Jakub Bartoszewicz",
        "author_email": "bartoszewiczj@rki.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "<!-- {#mainpage} -->\n\n# DeePaC\n\nDeePaC is a python package and a CLI tool for predicting labels (e.g. pathogenic potentials) from short DNA sequences (e.g. Illumina \nreads) with reverse-complement neural networks. For details, see our preprint on bioRxiv: \n<https://www.biorxiv.org/content/10.1101/535286v2>.\n\nDocumentation can be found here:\n<https://rki_bioinformatics.gitlab.io/DeePaC/>.\n\n\n## Installation\n\n### Recommended: virtual environment\n\nWe recomment setting up an isolated `conda` environment:\n```\nconda create -n my_env\nconda activate my_env\n```\n\nor a `virtualenv`:\n```\nvirtualenv --system-site-packages my_env\nsource my_env/bin/activate\n```\n\n\n### With conda\n [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/deepac/README.html)\n\nYou can install DeePaC with `bioconda`. Set up the [bioconda channel](\n<https://bioconda.github.io/index.html#set-up-channels>) first, and then:\n```\nconda install deepac\n```\n\n### With pip\n\nYou can also install DeePaC with `pip`:\n```\npip install deepac\n```\n\n### GPU support\n\nTo use GPUs, you need to install the GPU version of TensorFlow. In conda, install tensorflow-gpu from the `defaults` channel before deepac:\n```\nconda remove tensorflow\nconda install -c defaults tensorflow-gpu \nconda install deepac\n```\n\nIf you're using `pip`, you need to install CUDA and CuDNN first (see TensorFlow installation guide for details). Then\nyou can do the same as above:\n```\npip uninstall tensorflow\npip install tensorflow-gpu\n```\n\n### Optional: run tests\nOptionally, you can run explicit tests of your installation. Note that it may take some time on a CPU.\n```\ndeepac test \n# Test using a GPU\ndeepac test -g 1\n```\n\n### Help\n\nTo see help, just use\n```\ndeepac --help\ndeepac predict --help\ndeepac train --help\n# Etc.\n```\n\n## Prediction\n\nYou can predict pathogenic potentials with one of the built-in models out of the box:\n```\n# A rapid CNN (trained on IMG/M data)\ndeepac predict -r input.fasta\n# A sensitive LSTM (trained on IMG/M data)\ndeepac predict -s input.fasta\n# With GPU support\ndeepac predict -s -g 1 input.fasta\n```\n\nThe rapid and the sensitive models are trained to predict pathogenic potentials of novel bacterial species.\nFor details, see <https://www.biorxiv.org/content/10.1101/535286v2>.\n\nTo quickly filter your data according to predicted pathogenic potentials, you can use:\n```\ndeepac predict -r input.fasta\ndeepac filter input.fasta input_predictions.npy -t 0.5\n```\nNote that after running `predict`, you can use the `input_predictions.npy` to filter your fasta file with different\nthresholds. You can also add pathogenic potentials to the fasta headers in the output files:\n```\ndeepac filter input.fasta input_predictions.npy -t 0.75 -p -o output-75.fasta\ndeepac filter input.fasta input_predictions.npy -t 0.9 -p -o output-90.fasta\n```\n\n## Preprocessing\n\nFor more complex analyzes, it can be useful to preprocess the fasta files by converting them to binary numpy arrays. Use:\n```\ndeepac preproc preproc_config.ini\n```\nSee the `config_templates` directory of the GitLab repository (https://gitlab.com/rki_bioinformatics/DeePaC/) for a sample configuration file.\n\n## Training\nYou can use the built-in architectures to train a new model:\n```\ndeepac train -r -g 1 -T train_data.npy -t train_labels.npy -V val_data.npy -v val_labels.npy\ndeepac train -s -g 1 -T train_data.npy -t train_labels.npy -V val_data.npy -v val_labels.npy\n\n```\n\nTo train a new model based on you custom configuration, use\n```\ndeepac train -c nn_train_config.ini\n```\n\nIf you train an LSTM on a GPU, a CUDNNLSTM implementation will be used. To convert the resulting model to be \nCPU-compatible, use `deepac convert`. You can also use it to save the weights of a model, or recompile a model \nfrom a set of weights to use it with a different Python binary.\n\n## Evaluation\n\nTo evaluate a trained model, use\n```\n# Read-by-read performance\ndeepac eval -r eval_config.ini\n# Species-by-species performance\ndeepac eval -s eval_species_config.ini\n# Ensemble performance\ndeepac eval -e eval_ens_config.ini\n```\nSee the configs directory for sample configuration files. Note that `deepac eval -s` requires precomputed predictions \nand a csv file with a number of DNA reads for each species in each of the classes.\n\n## Supplementary data and scripts\n\nIn the supplement_paper directory you can find the R scripts and data files used in the paper for dataset preprocessing and benchmarking.\n\n",
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        "keywords": "deep learning DNA sequencing synthetic biology pathogenicity prediction",
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