lsaBGC

⚠️ lsaBGC is being supported (e.g. will aim to fix issues with usability, bugs) but is no longer under active development (expect no new features). We ask you to please instead check out lsaBGC-Pan - a new version which features many of the core programs from lsaBGC as well as new modules! Note, also, the bioconda package lsabgc corresponds to lsaBGC-Pan.

Lineage Specific Analysis (lsa) of Biosynthetic Gene Clusters (BGC)

lsaBGC offers modular programs, as well as workflows, designed for investigating and mining for biosynthetic gene cluster diversity across a focal lineage/taxa of interest.

Compatible with BGC predictions from antiSMASH, GECCO, and DeepBGC.

Documentation and How to Get Started:

Documentation can currently be found on this Github repo's wiki: https://github.com/Kalan-Lab/lsaBGC/wiki

1. Background on lsaBGC - what it does and does not do
2. An Overview of Final Results from lsaBGC
3. Quick Start - Using the simple lsaBGC-Easy.py (bacterial) and lsaBGC-Euk-Easy.py (fungal) workflows
   • Use on any platform in just 3 steps via Docker
4. Modular Usage - Exploring BGCs in Cutibacterium
5. GSeeF - quick and simple visualization of GCFs/BGCs across a species phylogeny
6. visualize_BGC-ome - quick and simple visualization of a sample's BGC-ome
7. new: Investigate a single cluster of related BGCs using the sibling suite zol

IMPORTANT: PLEASE USE v1.52+:

Please make sure to use v1.52+ of the pipeline - if you are using lsaBGC-Easy.py with antiSMASH, the default settings for antiSMASH based BGC prediction from v1.38 to v1.51 included the argument --taxon fungi by mistake. It should only be the default for the analagous lsaBGC-Euk-Easy.py program.

In v1.53, we added missing singleton CDS features that were regarded as faulty by Panaroo analysis but might correspond to BGCs according to BGC prediction software. We also added a separate processing for resolved hierarchical ortholog groups from OrthoFinder analysis when working with fungal genomes to more directly use the results as is, rather than performing customized processing developed for investigation of bacterial genomes.

Installation:

Using Conda (for full usage of suite)

Installation can be performed via conda (see below for Docker) and should take ~5 minutes with mamba or ~10-20 minutes with conda and has been tested on both unix (specifically Ubuntu) and macOS. We are happy to attempt to address issues with installation if any arise, please open a Git Issues case:

# 1. clone Git repo and cd into it!
git clone https://github.com/Kalan-Lab/lsaBGC
cd lsaBGC/

# 2. create conda environment using yaml file and activate it!
# For a much faster installation replace "conda" in the following
# commands with "mamba" (after installing mamba in your base conda
# environment)
mamba env create -f lsaBGC_env.yml -p /path/to/lsaBGC_conda_env/
conda activate /path/to/lsaBGC_conda_env/

# 3. complete python installation with the following commands:
python setup.py install
pip install -e .

Optional, but recommended, command to download KOfams + PGAP HMMs + MIBiG protein FASTA for annotation:

# Warning: can take >10 minutes! 
# Can skip to run tests first to make sure things are working properly.
# within lsaBGC Git repo with conda environment activated:
setup_annotation_dbs.py

If clustering of BGCs into GCFs using BiG-SCAPE is preferred to lsaBGC-Cluster.py, setup BiG-SCAPE using the following:

setup_bigscape.py

A small test case is provided here and can be run after installation by simply issuing (takes around ~7 minutes using 4 cpus/threads):

# Warning: uses 4 cpus/threads! 
bash run_tests.sh

There are also additional test cases to demonstrate usage of individual programs along with expected outputs from commands. We also have a walk-through tutorial Wiki page to showcase the use of the suite and relations between core programs.

The major outputs of the final lsaBGC-AutoAnalyze.py run are in the resulting folder test_case/lsaBGC_AutoAnalyze_Results/Final_Results/ and described on this wiki page. Examples for the final AutoAnalyze results from an lsaBGC-Easy.py run on Cutibacterium avidum can be found here on Google Drive.

Using Docker (for major workflows only)

A docker image is provided for the lsaBGC-Easy.py and lsaBGC-Euk-Easy.py workflows together with a wrapper script. The image is pretty large (~26Gb) but includes all the databases and dependencies needed for lsaBGC, BiG-SCAPE, antiSMASH, and GECCO analysis. For lsaBGC, to save space, the KOfam database is not included. For antiSMASH, MEME is not incldued (for licensing reasons), thus RODEO and CASSIS analyses are not available.

To use the latest Docker image, please: (1) install Docker and (2) download the wrapper script:

# 1. download wrapper script for running image
wget https://raw.githubusercontent.com/Kalan-Lab/lsaBGC/main/docker/run_LSABGC.sh

# 2. run it
bash run_LSABGC.sh

Quick Start - using lsaBGC-Easy.py and lsaBGC-Euk-Easy.py

Check out how to use lsaBGC-Easy.py and lsaBGC-Euk-Easy.py on their wiki page!

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Acknowledgements:

We would like to thank members of the Kalan lab, Currie lab, Kwan lab, Anantharaman lab, and Pepperell lab at UW Madison for feedback on the development of lsaBGC.

Feedback:

Issues or suggestions for new features / changes to approaches? Please create an issue/ticket on GitHub issues and let us know!

License:

BSD 3-Clause License

Copyright (c) 2021, Kalan-Lab
All rights reserved.

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