deepbgc 0.1.3

DeepBGC - Biosynthetic Gene Cluster detection and classification

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.

Install 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 the DeepBGC package

Use DeepBGC

Download models and Pfam database

Before you can use DeepBGC, download trained models and Pfam database:

deepbgc download

You can display downloaded dependencies and models using:

deepbgc info

Detection and classification

Detect and classify BGCs in a genomic sequence. Proteins and Pfam domains are detected automatically if not already annotated (HMMER and Prodigal needed)

# 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. Data can be downloaded on the releases page

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.