What is BioCompass?¶

An emergent need in the field of natural products is to dereplicate biosynthetic pathways at the genomic level. This dereplication consists of grouping the biosynthetic gene clusters (BGCs) into families according to their nucleotide sequence homology, and a procedure known as ‘gene cluster networking’. Since the source code from published networking approaches are still not publicly available, we adapted our own strategy for the discovery of gene cluster families, named Biosynthetic Gene Cluster Comparative Synteny Software (BioCompass).Please note that this is a beta version of BioCompass which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an “as is” and “as available” basis.

How BioCompass Works?¶

BioCompass groups BGCs into gene cluster families based on synteny and homology. These clusters need to be identified by antiSMASH, with the ClusterFinder option preferentially turned off. A similarity matrix is used to divide each given BGC into subclusters based on synteny with the best MultiGeneBLAST hits (obtained using antiSMASH 3.0) and the functional annotation of each gene in the queried cluster. This information is then incorporated into a query-specific database to search for the best matches for each subcluster. This newly created database includes microbial BGCs identified by antiSMASH (downloaded from NCBI database, Genbank NR) and the latest version of the well annotated MIBiG database of known gene clusters. Additional gene clusters, for example missing from NCBI and MIBiG, can also be added in by the user. Final similarity scores are calculated by MultiGeneBLAST for each subcluster and then stored as tables. The outputs can be displayed as a network diagram using Cytoscape v3.2.1.

Future Implementations¶

One of the issues of using networking approaches in the natural products research area concerns the concept of networking itself. For accurate dereplication of families (both molecular families, as used in GnPS (link), and gene cluster families), it’s required to define a threshold for which once trespassed, two gene clusters are not part of the same family anymore. Analogously to the species definition when using the 16S rRNA gene, this threshold is empirical and can be imprecise in some cases. Hence, BioCompass envisions to implement a cutoff calibration feature to minimize this issue. The new feature consists on the user evaluating the network diagram for both gene homology (scored via multigene blast) and domain homology (score via Jaccard index, another feature to be implemented soon), visually deciding which cutoff would better represent those scores for the particular query. The user will use an internal standard to aid in the decision making process.

Stable release¶

To install BioCompass, run this command in your terminal:

$ pip install BioCompass

This is the preferred method to install BioCompass, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

If you install BioCompass from pip, you still need a copy of the source (instructions below) to be able to run it.

From sources¶

The sources for BioCompass can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/NP-Omix/BioCompass

Or download the tarball:

$ curl  -OL https://github.com/NP-Omix/BioCompass/tarball/master

Once you have a copy of the source, you can install it with:

$ cd BioCompass/

$ python setup.py install

Using BioCompass in your queried genome at your machine¶

If your installation of BioCompass succeeded, follow the instructions below. If not, please report an issue on our GitHub repo and include the error message you obtained during your installation.

1. Submit your query genome to antiSMASH. Once the run is complete, download results using “Download all results” (top right corner, an icon shaped as a download arrow). Decompress and save the results into a folder named “antiSMASH_input”. We advise renaming the subfolder inside antiSMASH_input to a shorter name representing your strain (hereafter generically referred to as “REFNAME”);
2. Download and decompress multigeneBLAST for command line;

For linux:

wget 'http://downloads.sourceforge.net/project/multigeneblast/multigeneblast_1.1.13_linux64.tar.gz'
tar -xvzf multigeneblast_1.1.13_linux64.tar.gz

For mac:

wget 'https://superb-sea2.dl.sourceforge.net/project/multigeneblast/multigeneblast_1.1.14_macosx_commandline.zip'
unzip multigeneblast_1.1.14_macosx_commandline.zip -d multigeneblast

3. Now, execute BioCompass using the following comand:

   cd path/to/BioCompass/BioCompass
   make INPUTDIR='path/to/antiSMASH_input' REFNAME='NAME' MULTIGENEBLASTDIR='path/to/multigeneblast' ALL
   

4. Towards the end of the run, three questions will be prompted asking to select the cutoff you would like to use for filtering your results. We advise using a low cutoff first (near the minimum), checking the network diagram and then revisiting this step using the code below (that only reruns this step, not the whole pipeline) to find the best cutoff for your data:

   make INPUTDIR='path/to/antiSMASH_input' REFNAME='NAME' MULTIGENEBLASTDIR='path/to/multigeneblast' step_H
   

5. The table outputs (REFNAME_edges.txt and REFNAME_nodes.txt) can be visualized as a network diagram using Cytoscape 3.2.1.

Using BioCompass in your project¶

To use BioCompass in a project:

import BioCompass

Types of Contributions¶

Get Started!¶

Ready to contribute? Here’s how to set up gene_cluster_network for local development.

1. Fork the gene_cluster_network repo on GitHub.

2. Clone your fork locally:

   $ git clone git@github.com:your_name_here/gene_cluster_network.git
   

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

   $ mkvirtualenv gene_cluster_network
   $ cd gene_cluster_network/
   $ python setup.py develop
   

4. Create a branch for local development:

   $ git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

   $ flake8 gene_cluster_network tests
   $ python setup.py test or py.test
   $ tox
   

   To get flake8 and tox, just pip install them into your virtualenv.

6. Commit your changes and push your branch to GitHub:

   $ git add .
   $ git commit -m "Your detailed description of your changes."
   $ git push origin name-of-your-bugfix-or-feature
   

7. Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check https://travis-ci.org/castelao/gene_cluster_network/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ py.test tests.test_gene_cluster_network

Development Lead¶

• Tiago Leao <tferreir@ucsd.edu>
• Gui Castelão <guilherme@castelao.net>

Contributors¶

None yet. Why not be the first?

0.0.1 (2016-06-24)¶

• First python package prototype.