ASaiM: a Galaxy-based framework to analyze microbiota data

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Abstract

Background

New generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.

Findings

We therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io).

Conclusions

Based on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.

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  1. Now published in GigaScience doi: 10.1093/gigascience/giy057

    Bérénice Batut 1Bioinformatics Group, Department of Computer Science, University of Freiburg, GermanyFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Bérénice BatutFor correspondence: berenice.batut@gmail.com pierre.peyret@uca.frKévin Gravouil 2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, France3Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France4Université Clermont Auvergne, CNRS, LIMOS, F-63000 Clermont-Ferrand, FranceFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Kévin GravouilClémence Defois 2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, FranceFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteSaskia Hiltemann 5Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3015 CE, NetherlandsFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Saskia HiltemannJean-François Brugère 2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, FranceFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteEric Peyretaillade 2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, FranceFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePierre Peyret 2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, FranceFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: berenice.batut@gmail.com pierre.peyret@uca.fr

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giy057 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101163 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101164 Reviewer 3: http://dx.doi.org/10.5524/REVIEW.101165