Metadata-Version: 2.1
Name: deepbgc
Version: 0.1.4
Summary: DeepBGC - Biosynthetic Gene Cluster detection and classification
Home-page: https://github.com/Merck/DeepBGC
Author: David Příhoda, Geoffrey Hannigan
Author-email: david.prihoda1@merck.com
License: MIT
Description: # DeepBGC: Biosynthetic Gene Cluster detection and classification
        
        DeepBGC detects BGCs in bacterial and fungal genomes using deep learning. 
        DeepBGC employs a Bidirectional Long Short-Term Memory Recurrent Neural Network 
        and a word2vec-like vector embedding of Pfam protein domains. 
        Product class and activity of detected BGCs is predicted using a Random Forest classifier.
        
        [![BioConda Install](https://img.shields.io/conda/dn/bioconda/deepbgc.svg?style=flag&label=BioConda%20install&color=green)](https://anaconda.org/bioconda/deepbgc) 
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/deepbgc.svg?color=green&label=PyPI%20downloads)
        [![PyPI license](https://img.shields.io/pypi/l/deepbgc.svg)](https://pypi.python.org/pypi/deepbgc/)
        [![PyPI version](https://badge.fury.io/py/deepbgc.svg)](https://badge.fury.io/py/deepbgc)
        [![CI](https://api.travis-ci.org/Merck/deepbgc.svg?branch=master)](https://travis-ci.org/Merck/deepbgc)
        
        ![DeepBGC architecture](images/deepbgc.architecture.png?raw=true "DeepBGC architecture")
        
        ## Install using bioconda (recommended)
        
        - Install Bioconda by following Step 1 and 2 from: https://bioconda.github.io/
        - Run `conda install deepbgc` to install DeepBGC and all of its dependencies    
        
        ## Install using pip
        
        If you don't mind installing the HMMER and Prodigal dependencies manually, you can also install DeepBGC using pip:
        
        - Install Python version 2.7+ or 3.4+
        - Install Prodigal and put the `prodigal` binary it on your PATH: https://github.com/hyattpd/Prodigal/releases
        - Install HMMER and put the `hmmscan` and `hmmpress` binaries on your PATH: http://hmmer.org/download.html
        - Run `pip install deepbgc` to install DeepBGC   
        
        ## Use DeepBGC
        
        ### Download models and Pfam database
        
        Before you can use DeepBGC, download trained models and Pfam database:
        
        ```bash
        deepbgc download
        ```
        
        You can display downloaded dependencies and models using:
        
        ```bash
        deepbgc info
        ```
        
        ### Detection and classification
        
        ![DeepBGC pipeline](images/deepbgc.pipeline.png?raw=true "DeepBGC pipeline")
        
        Detect and classify BGCs in a genomic sequence. 
        Proteins and Pfam domains are detected automatically if not already annotated (HMMER and Prodigal needed)
        
        ```bash
        # Show command help docs
        deepbgc pipeline --help
        
        # Detect and classify BGCs in mySequence.fa using DeepBGC algorithm and save the output to mySequence directory.
        deepbgc pipeline mySequence.fa
        ```
        
        This will produce a directory with multiple files and a README.txt with file descriptions.
        
        #### Example output
        
        See the [DeepBGC Example Result Notebook](https://nbviewer.jupyter.org/urls/github.com/Merck/deepbgc/releases/download/v0.1.0/DeepBGC_Example_Result.ipynb).
        Data can be downloaded on the [releases page](https://github.com/Merck/deepbgc/releases)
        
        ![Detected BGC Regions](images/deepbgc.bgc.png?raw=true "Detected BGC regions")
        
        ### Model training
        
        You can train your own BGC detection and classification models, see `deepbgc train --help` for documentation and examples.
        
        DeepBGC positives, negatives and other training and validation data can be found on the [releases page](https://github.com/Merck/deepbgc/releases).
        
Keywords: biosynthetic gene clusters,bgc detection,deep learning,pfam2vec
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Requires-Python: >=2.7, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
Description-Content-Type: text/markdown
Provides-Extra: hmm
