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Flexible metagenome analysis using the MGX framework

BACKGROUND: The characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most comple...

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Autores principales: Jaenicke, Sebastian, Albaum, Stefan P., Blumenkamp, Patrick, Linke, Burkhard, Stoye, Jens, Goesmann, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937802/
https://www.ncbi.nlm.nih.gov/pubmed/29690922
http://dx.doi.org/10.1186/s40168-018-0460-1
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author Jaenicke, Sebastian
Albaum, Stefan P.
Blumenkamp, Patrick
Linke, Burkhard
Stoye, Jens
Goesmann, Alexander
author_facet Jaenicke, Sebastian
Albaum, Stefan P.
Blumenkamp, Patrick
Linke, Burkhard
Stoye, Jens
Goesmann, Alexander
author_sort Jaenicke, Sebastian
collection PubMed
description BACKGROUND: The characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most complex communities. Metagenome analysis, the assignment of sequences to taxonomic and functional entities, however, remains a tedious task: large amounts of data need to be processed. There are a number of approaches addressing particular aspects, but scientific questions are often too specific to be answered by a general-purpose method. RESULTS: We present MGX, a flexible and extensible client/server-framework for the management and analysis of metagenomic datasets; MGX features a comprehensive set of adaptable workflows required for taxonomic and functional metagenome analysis, combined with an intuitive and easy-to-use graphical user interface offering customizable result visualizations. At the same time, MGX allows to include own data sources and devise custom analysis pipelines, thus enabling researchers to perform basic as well as highly specific analyses within a single application. CONCLUSIONS: With MGX, we provide a novel metagenome analysis platform giving researchers access to the most recent analysis tools. MGX covers taxonomic and functional metagenome analysis, statistical evaluation, and a wide range of visualizations easing data interpretation. Its default taxonomic classification pipeline provides equivalent or superior results in comparison to existing tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-018-0460-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-59378022018-05-14 Flexible metagenome analysis using the MGX framework Jaenicke, Sebastian Albaum, Stefan P. Blumenkamp, Patrick Linke, Burkhard Stoye, Jens Goesmann, Alexander Microbiome Software BACKGROUND: The characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most complex communities. Metagenome analysis, the assignment of sequences to taxonomic and functional entities, however, remains a tedious task: large amounts of data need to be processed. There are a number of approaches addressing particular aspects, but scientific questions are often too specific to be answered by a general-purpose method. RESULTS: We present MGX, a flexible and extensible client/server-framework for the management and analysis of metagenomic datasets; MGX features a comprehensive set of adaptable workflows required for taxonomic and functional metagenome analysis, combined with an intuitive and easy-to-use graphical user interface offering customizable result visualizations. At the same time, MGX allows to include own data sources and devise custom analysis pipelines, thus enabling researchers to perform basic as well as highly specific analyses within a single application. CONCLUSIONS: With MGX, we provide a novel metagenome analysis platform giving researchers access to the most recent analysis tools. MGX covers taxonomic and functional metagenome analysis, statistical evaluation, and a wide range of visualizations easing data interpretation. Its default taxonomic classification pipeline provides equivalent or superior results in comparison to existing tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-018-0460-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-04-24 /pmc/articles/PMC5937802/ /pubmed/29690922 http://dx.doi.org/10.1186/s40168-018-0460-1 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Jaenicke, Sebastian
Albaum, Stefan P.
Blumenkamp, Patrick
Linke, Burkhard
Stoye, Jens
Goesmann, Alexander
Flexible metagenome analysis using the MGX framework
title Flexible metagenome analysis using the MGX framework
title_full Flexible metagenome analysis using the MGX framework
title_fullStr Flexible metagenome analysis using the MGX framework
title_full_unstemmed Flexible metagenome analysis using the MGX framework
title_short Flexible metagenome analysis using the MGX framework
title_sort flexible metagenome analysis using the mgx framework
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5937802/
https://www.ncbi.nlm.nih.gov/pubmed/29690922
http://dx.doi.org/10.1186/s40168-018-0460-1
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