<?xml version="1.0"?>
<macros>

    <token name="@TOOL_VERSION@">3.2.3</token>

    <xml name="requirements">
        <requirements>
            <requirement type="package" version="@TOOL_VERSION@">frogs</requirement>
            <yield />
        </requirements>
    </xml>

    <xml name="requirements_phyloseq">
        <expand macro="requirements">
            <requirement type="package" version="2.11.4">pandoc</requirement>
            <requirement type="package" version="4.0">r-essentials</requirement>
            <requirement type="package" version="1.34.0">bioconductor-phyloseq</requirement>
            <yield />
        </expand>
    </xml>


    <token name="@HELP_LOGO@">
.. image:: static/images/FROGS_logo.png
   :height: 144
   :width: 110

    </token>

    <token name="@HELP_CONTACT@">

----

**Contact**

Contacts: frogs-support@inrae.fr

Repositories: https://github.com/geraldinepascal/FROGS, https://github.com/geraldinepascal/FROGS-wrappers

Website: http://frogs.toulouse.inrae.fr/

Depending on which kind of amplicon you are working on, please cite one of the two FROGS publications:

    </token>

    <xml name="citations">
        <citation type="bibtex">
            @article{10.1093/bioinformatics/btx791,
                author = {Escudié, Frédéric and Auer, Lucas and Bernard, Maria and Mariadassou, Mahendra and Cauquil, Laurent and Vidal, Katia and Maman, Sarah and Hernandez-Raquet, Guillermina and Combes, Sylvie and Pascal, Géraldine},
                title = "{FROGS: Find, Rapidly, OTUs with Galaxy Solution}",
                journal = {Bioinformatics},
                volume = {34},
                number = {8},
                pages = {1287-1294},
                year = {2018},
                month = {04},
                abstract = "{Metagenomics leads to major advances in microbial ecology and biologists need user friendly tools to analyze their data on their own.This Galaxy-supported pipeline, called FROGS, is designed to analyze large sets of amplicon sequences and produce abundance tables of Operational Taxonomic Units (OTUs) and their taxonomic affiliation. The clustering uses Swarm. The chimera removal uses VSEARCH, combined with original cross-sample validation. The taxonomic affiliation returns an innovative multi-affiliation output to highlight databases conflicts and uncertainties. Statistical results and numerous graphical illustrations are produced along the way to monitor the pipeline. FROGS was tested for the detection and quantification of OTUs on real and in silico datasets and proved to be rapid, robust and highly sensitive. It compares favorably with the widespread mothur, UPARSE and QIIME.Source code and instructions for installation: https://github.com/geraldinepascal/FROGS.git. A companion website: http://frogs.toulouse.inra.fr.Supplementary data are available at Bioinformatics online.}",
                issn = {1367-4803},
                doi = {10.1093/bioinformatics/btx791},
                url = {https://doi.org/10.1093/bioinformatics/btx791},
                eprint = {https://academic.oup.com/bioinformatics/article-pdf/34/8/1287/25120140/btx791\_supplementary\_file.pdf},
            }
        </citation>
        <citation type="bibtex">
            @article{10.1093/bib/bbab318,
                author = {Bernard, Maria and Rué, Olivier and Mariadassou, Mahendra and Pascal, Géraldine},
                title = "{FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers}",
                journal = {Briefings in Bioinformatics},
                year = {2021},
                month = {08},
                abstract = "{Fungi are present in all environments. They fulfil important ecological functions and play a crucial role in the food industry. Their accurate characterization is thus indispensable, particularly through metabarcoding. The most frequently used markers to monitor fungi are ITSs. These markers are the best documented in public databases but have one main weakness: polymerase chain reaction amplification may produce non-overlapping reads in a significant fraction of the fungi. When these reads are filtered out, traditional metabarcoding pipelines lose part of the information and consequently produce biased pictures of the composition and structure of the environment under study. We developed a solution that enables processing of the entire set of reads including both overlapping and non-overlapping, thus providing a more accurate picture of fungal communities. Our comparative tests using simulated and real data demonstrated the effectiveness of our solution, which can be used by both experts and non-specialists on a command line or through the Galaxy-based web interface.}",
                issn = {1477-4054},
                doi = {10.1093/bib/bbab318},
                url = {https://doi.org/10.1093/bib/bbab318},
                note = {bbab318},
                eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab318/39805849/bbab318.pdf},
            }
        </citation>
    </xml>


</macros>