gecco-tool 0.8.8

Gene cluster prediction with Conditional random fields.

Hi, I'm GECCO!

🦎 ️Overview

GECCO (Gene Cluster prediction with Conditional Random Fields) is a fast and scalable method for identifying putative novel Biosynthetic Gene Clusters (BGCs) in genomic and metagenomic data using Conditional Random Fields (CRFs).

🔧 Installing GECCO

GECCO is implemented in Python, and supports all versions from Python 3.6. It requires additional libraries that can be installed directly from PyPI, the Python Package Index.

Use pip to install GECCO on your machine:

$ pip install gecco-tool

If you'd rather use Conda, a package is available in the bioconda channel. You can install with:

$ conda install -c bioconda gecco

This will install GECCO, its dependencies, and the data needed to run predictions. This requires around 40MB of data to be downloaded, so it could take some time depending on your Internet connection. Once done, you will have a gecco command available in your $PATH.

Note that GECCO uses HMMER3, which can only run on PowerPC and recent x86-64 machines running a POSIX operating system. Therefore, GECCO will work on Linux and OSX, but not on Windows.

🧬 Running GECCO

Once gecco is installed, you can run it from the terminal by giving it a FASTA or GenBank file with the genomic sequence you want to analyze, as well as an output directory:

$ gecco run --genome some_genome.fna -o some_output_dir

Additional parameters of interest are:

• --jobs, which controls the number of threads that will be spawned by GECCO whenever a step can be parallelized. The default, 0, will autodetect the number of CPUs on the machine using os.cpu_count.
• --cds, controlling the minimum number of consecutive genes a BGC region must have to be detected by GECCO. The default is 3.
• --threshold, controlling the minimum probability for a gene to be considered part of a BGC region. Using a lower number will increase the number (and possibly length) of predictions, but reduce accuracy. The default of 0.3 was selected to optimize precision/recall on a test set of 364 BGCs from MIBiG 2.0.

🔎 Results

GECCO will create the following files:

• {genome}.features.tsv: The features file, containing the identified proteins and domains in the input sequences, in tabular format.
• {genome}.clusters.tsv: If any were found, a clusters file, containing the coordinates of the predicted clusters along their putative biosynthetic type, in tabular format.
• {genome}_cluster_{N}.gbk: If any were found, a GenBank file per cluster, containing the cluster sequence annotated with its member proteins and domains.

To get a more visual way of exploring of the predictions, you can open the GenBank files in a genome editing software like UGENE, or you can load the results into an AntiSMASH report. Check the Integrations page of the documentation for a step-by-step guide.

🔖 Reference

GECCO can be cited using the following preprint:

Accurate de novo identification of biosynthetic gene clusters with GECCO. Laura M Carroll, Martin Larralde, Jonas Simon Fleck, Ruby Ponnudurai, Alessio Milanese, Elisa Cappio Barazzone, Georg Zeller. bioRxiv 2021.05.03.442509; doi:10.1101/2021.05.03.442509

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

⚖️ License

This software is provided under the GNU General Public License v3.0 or later. GECCO is developped by the Zeller Team at the European Molecular Biology Laboratory in Heidelberg.