Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota

View ORCID ProfileBérénice Batut, View ORCID ProfileKévin Gravouil, Clémence Defois, View ORCID ProfileSaskia Hiltemann, Jean-François Brugère, Eric Peyretaillade, Pierre Peyret
doi: https://doi.org/10.1101/183970
Bérénice Batut
1Bioinformatics Group, Department of Computer Science, University of Freiburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Bérénice Batut
  • For correspondence: berenice.batut@gmail.com pierre.peyret@uca.fr
Kévin Gravouil
2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, France
3Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France
4Université Clermont Auvergne, CNRS, LIMOS, F-63000 Clermont-Ferrand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kévin Gravouil
Clémence Defois
2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saskia Hiltemann
5Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3015 CE, Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Saskia Hiltemann
Jean-François Brugère
2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eric Peyretaillade
2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Pierre Peyret
2Université Clermont Auvergne, INRA, MEDIS, F-63000 Clermont-Ferrand, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: berenice.batut@gmail.com pierre.peyret@uca.fr
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Background New generation of sequencing platforms coupled to numerous bioinformatics tools has 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 a curated collection of tools to explore and visualize taxonomic and functional information from raw amplicon, metagenomic or metatranscriptomic sequences. To guide different analyses, several customizable workflows are included. All workflows are supported by tutorials and Galaxy interactive tours to guide the users through the analyses step by step. ASaiM is implemented as Galaxy Docker flavour. It is scalable to many thousand datasets, but also can be used 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 sophisticated analyses to scientists without command-line knowledge. ASaiM provides a powerful framework to easily and quickly explore microbiota data in a reproducible and transparent environment.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted September 04, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota
Bérénice Batut, Kévin Gravouil, Clémence Defois, Saskia Hiltemann, Jean-François Brugère, Eric Peyretaillade, Pierre Peyret
bioRxiv 183970; doi: https://doi.org/10.1101/183970
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
ASaiM: a Galaxy-based framework to analyze raw shotgun data from microbiota
Bérénice Batut, Kévin Gravouil, Clémence Defois, Saskia Hiltemann, Jean-François Brugère, Eric Peyretaillade, Pierre Peyret
bioRxiv 183970; doi: https://doi.org/10.1101/183970

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4380)
  • Biochemistry (9581)
  • Bioengineering (7086)
  • Bioinformatics (24844)
  • Biophysics (12596)
  • Cancer Biology (9951)
  • Cell Biology (14345)
  • Clinical Trials (138)
  • Developmental Biology (7944)
  • Ecology (12101)
  • Epidemiology (2067)
  • Evolutionary Biology (15983)
  • Genetics (10919)
  • Genomics (14732)
  • Immunology (9868)
  • Microbiology (23645)
  • Molecular Biology (9477)
  • Neuroscience (50836)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2681)
  • Physiology (4013)
  • Plant Biology (8655)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2391)
  • Systems Biology (6427)
  • Zoology (1346)