{
    "info": {
        "author": "David P\u0159\u00edhoda, Geoffrey Hannigan",
        "author_email": "david.prihoda1@merck.com",
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        "description": "# DeepBGC: Biosynthetic Gene Cluster detection and classification\n\nDeepBGC detects BGCs in bacterial and fungal genomes using deep learning. \nDeepBGC employs a Bidirectional Long Short-Term Memory Recurrent Neural Network \nand a word2vec-like vector embedding of Pfam protein domains. \nProduct class and activity of detected BGCs is predicted using a Random Forest classifier.\n\n[![BioConda Install](https://img.shields.io/conda/dn/bioconda/deepbgc.svg?style=flag&label=BioConda%20install&color=green)](https://anaconda.org/bioconda/deepbgc) \n![PyPI - Downloads](https://img.shields.io/pypi/dm/deepbgc.svg?color=green&label=PyPI%20downloads)\n[![PyPI license](https://img.shields.io/pypi/l/deepbgc.svg)](https://pypi.python.org/pypi/deepbgc/)\n[![PyPI version](https://badge.fury.io/py/deepbgc.svg)](https://badge.fury.io/py/deepbgc)\n[![CI](https://api.travis-ci.org/Merck/deepbgc.svg?branch=master)](https://travis-ci.org/Merck/deepbgc)\n\n![DeepBGC architecture](images/deepbgc.architecture.png?raw=true \"DeepBGC architecture\")\n\n## Publications\n\nA deep learning genome-mining strategy for biosynthetic gene cluster prediction <br>\nGeoffrey D Hannigan,  David Prihoda et al., Nucleic Acids Research, gkz654, https://doi.org/10.1093/nar/gkz654\n\n\n## Install using bioconda (recommended)\n\n- Install Bioconda by following Step 1 and 2 from: https://bioconda.github.io/\n- Run `conda install deepbgc` to install DeepBGC and all of its dependencies    \n\n## Install using pip\n\nIf you don't mind installing the HMMER and Prodigal dependencies manually, you can also install DeepBGC using pip:\n\n- Install Python version 2.7+ or 3.4+\n- Install Prodigal and put the `prodigal` binary it on your PATH: https://github.com/hyattpd/Prodigal/releases\n- Install HMMER and put the `hmmscan` and `hmmpress` binaries on your PATH: http://hmmer.org/download.html\n- Run `pip install deepbgc` to install DeepBGC   \n\n## Use DeepBGC\n\n### Download models and Pfam database\n\nBefore you can use DeepBGC, download trained models and Pfam database:\n\n```bash\ndeepbgc download\n```\n\nYou can display downloaded dependencies and models using:\n\n```bash\ndeepbgc info\n```\n\n### Detection and classification\n\n![DeepBGC pipeline](images/deepbgc.pipeline.png?raw=true \"DeepBGC pipeline\")\n\nDetect and classify BGCs in a genomic sequence. \nProteins and Pfam domains are detected automatically if not already annotated (HMMER and Prodigal needed)\n\n```bash\n# Show command help docs\ndeepbgc pipeline --help\n\n# Detect and classify BGCs in mySequence.fa using DeepBGC detector.\ndeepbgc pipeline mySequence.fa\n\n# Detect and classify BGCs in mySequence.fa using custom DeepBGC detector trained on your own data.\ndeepbgc pipeline --detector path/to/myDetector.pkl mySequence.fa\n```\n\nThis will produce a `mySequence` directory with multiple files and a README.txt with file descriptions.\n\nSee [Train DeepBGC on your own data](#train-deepbgc-on-your-own-data) section below for more information about training a custom detector or classifier.\n\n#### Example output\n\nSee the [DeepBGC Example Result Notebook](https://nbviewer.jupyter.org/urls/github.com/Merck/deepbgc/releases/download/v0.1.0/DeepBGC_Example_Result.ipynb).\nData can be downloaded on the [releases page](https://github.com/Merck/deepbgc/releases)\n\n![Detected BGC Regions](images/deepbgc.bgc.png?raw=true \"Detected BGC regions\")\n\n## Train DeepBGC on your own data\n\nYou can train your own BGC detection and classification models, see `deepbgc train --help` for documentation and examples.\n\nDeepBGC positives, negatives and other training and validation data can be found in [release 0.1.0](https://github.com/Merck/deepbgc/releases/tag/v0.1.0) and [release 0.1.5](https://github.com/Merck/deepbgc/releases/tag/v0.1.5).\n\nIf you have any questions about using or training DeepBGC, feel free to submit an issue.\n\n### JSON model training template files\n\nDeepBGC is using JSON template files to define model architecture and training parameters. All templates can be downloaded in [release 0.1.0](https://github.com/Merck/deepbgc/releases/tag/v0.1.0).\n\nJSON template for DeepBGC LSTM **detector** with pfam2vec is structured as follows:\n```\n{\n  \"type\": \"KerasRNN\", - Model architecture (KerasRNN/DiscreteHMM/GeneBorderHMM)\n  \"build_params\": { - Parameters for model architecture\n    \"batch_size\": 16, - Number of splits of training data that is trained in parallel \n    \"hidden_size\": 128, - Size of vector storing the LSTM inner state\n    \"stateful\": true - Remember previous sequence when training next batch\n  },\n  \"fit_params\": {\n    \"timesteps\": 256, - Number of pfam2vec vectors trained in one batch\n    \"validation_size\": 0, - Fraction of training data to use for validation (if validation data is not provided explicitly). Use 0.2 for 20% data used for testing.\n    \"verbose\": 1, - Verbosity during training\n    \"num_epochs\": 1000, - Number of passes over your training set during training. You probably want to use a lower number if not using early stopping on validation data.\n    \"early_stopping\" : { - Stop model training when at certain validation performance\n      \"monitor\": \"val_auc_roc\", - Use validation AUC ROC to observe performance\n      \"min_delta\": 0.0001, - Stop training when the improvement in the last epochs did not improve more than 0.0001\n      \"patience\": 20, - How many of the last epochs to check for improvement\n      \"mode\": \"max\" - Stop training when given metric stops increasing (use \"min\" for decreasing metrics like loss)\n    },\n    \"shuffle\": true, - Shuffle samples in each epoch. Will use \"sequence_id\" field to group pfam vectors belonging to the same sample and shuffle them together \n    \"optimizer\": \"adam\", - Optimizer algorithm\n    \"learning_rate\": 0.0001, - Learning rate\n    \"weighted\": true - Increase weight of less-represented class. Will give more weight to BGC training samples if the non-BGC set is larger.\n  },\n  \"input_params\": {\n    \"features\": [ - Array of features to use in model, see deepbgc/features.py\n      {\n        \"type\": \"ProteinBorderTransformer\" - Add two binary flags for pfam domains found at beginning or at end of protein\n      },\n      {\n        \"type\": \"Pfam2VecTransformer\", - Convert pfam_id field to pfam2vec vector using provided pfam2vec table\n        \"vector_path\": \"#{PFAM2VEC}\" - PFAM2VEC variable is filled in using command line argument --config\n      }\n    ]\n  }\n}\n```\n\nJSON template for Random Forest **classifier** is structured as follows:\n```\n{\n  \"type\": \"RandomForestClassifier\", - Type of classifier (RandomForestClassifier)\n  \"build_params\": {\n    \"n_estimators\": 100, - Number of trees in random forest\n    \"random_state\": 0 - Random seed used to get same result each time\n  },\n  \"input_params\": {\n    \"sequence_as_vector\": true, - Convert each sample into a single vector\n    \"features\": [\n      {\n        \"type\": \"OneHotEncodingTransformer\" - Convert each sequence of Pfams into a single binary vector (Pfam set)\n      }\n    ]\n  }\n}\n```\n\n### Using your trained model\n\nSince version `0.1.10` you can provide a direct path to the detector or classifier model like so:\n```bash\ndeepbgc pipeline \\\n    mySequence.fa \\\n    --detector path/to/myDetector.pkl \\\n    --classifier path/to/myClassifier.pkl \n```\n\n\n",
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