项目作者: zellerlab

项目描述 :
GEne Cluster prediction with COnditional random fields.
高级语言: Python
项目地址: git://github.com/zellerlab/GECCO.git
创建时间: 2021-01-11T15:29:00Z
项目社区:https://github.com/zellerlab/GECCO

开源协议:GNU General Public License v3.0

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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).

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🔧 Installing GECCO

GECCO is implemented in Python, and supports all
versions
from Python 3.7. It requires
additional libraries that can be installed directly from
PyPI, the Python Package Index.

Use pip to install GECCO on your
machine:

  1. $ pip install gecco-tool

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

  1. $ 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:

  1. $ 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 0.8 was selected to optimize precision/recall on a test set
    of 364 BGCs from MIBiG 2.0.
  • --cds-feature, which can be supplied a feature name to extract genes
    if the input file already contains gene annotations instead of predicting
    genes with Pyrodigal. A common value
    for records downloaded from GenBank is --cds-feature CDS.

🔎 Results

GECCO will create the following files:

  • {genome}.genes.tsv: The genes file, containing the genes extracted
    or predicted from the input file, and per-gene BGC probabilities
    predicted by the CRF.
  • {genome}.features.tsv: The features file, containing the identified
    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.

GECCO can also convert results to other formats that may be more convenient
depending on the downstream usage. GECCO can convert results into:

  • GFF3 format so they can be loaded into a genomic viewer
    (gecco convert clusters --format gff).
  • GenBank files with antiSMASH-style features so they can be loaded into
    BiG-SLiCE for further analysis
    (gecco convert gbk --format bigslice).
  • FASTA files with the sequences of all the predicted BGCs (gecco convert gbk --format fna)
    or with the sequences of all their proteins (gecco convert gbk --format faa).

To get a more visual way of exploring of the predictions, you
can open the GenBank files in a genome editing software like UGENE.
You can otherwise 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.