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Repository gecco
Name: gecco
Owner: althonos
Synopsis: Biosynthetic Gene Cluster prediction with Conditional Random Fields with GECCO.
GECCO is a fast and scalable method for identifying putative novel Biosynthetic Gene Clusters (BGCs) in genomic and metagenomic data using Conditional Random Fields (CRFs).
It is developed in the Zeller group and is part of the suite of computational microbiome analysis tools hosted at EMBL.
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Repository README files - may contain important installation or license information

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).

GitLabCI License Coverage Docs Source Mirror Changelog Issues Preprint PyPI Bioconda Galaxy Versions Wheel

🔧 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 100MB 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, Linux and OSX are supported platforms, but GECCO will not be able to run 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 (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.

🔖 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 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.

Contents of this repository

Name Description Version Minimum Galaxy Version
is a fast and scalable method for identifying putative novel Biosynthetic Gene Clusters (BGCs) in genomic and metagenomic data using Conditional Random Fields (CRFs). 0.9.1 16.01

Genome annotation - Tools for annotating genomic information
Metagenomics - Tools enabling the study of metagenomes
Sequence Analysis - Tools for performing Protein and DNA/RNA analysis