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gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms

The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in th...

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Autores principales: Muñoz-Benavent, Maria, Hartkopf, Felix, Van Den Bossche, Tim, Piro, Vitor C, García-Ferris, Carlos, Latorre, Amparo, Renard, Bernhard Y, Muth, Thilo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671378/
https://www.ncbi.nlm.nih.gov/pubmed/33575609
http://dx.doi.org/10.1093/nargab/lqaa058
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author Muñoz-Benavent, Maria
Hartkopf, Felix
Van Den Bossche, Tim
Piro, Vitor C
García-Ferris, Carlos
Latorre, Amparo
Renard, Bernhard Y
Muth, Thilo
author_facet Muñoz-Benavent, Maria
Hartkopf, Felix
Van Den Bossche, Tim
Piro, Vitor C
García-Ferris, Carlos
Latorre, Amparo
Renard, Bernhard Y
Muth, Thilo
author_sort Muñoz-Benavent, Maria
collection PubMed
description The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.
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spelling pubmed-76713782021-02-10 gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms Muñoz-Benavent, Maria Hartkopf, Felix Van Den Bossche, Tim Piro, Vitor C García-Ferris, Carlos Latorre, Amparo Renard, Bernhard Y Muth, Thilo NAR Genom Bioinform Standard Article The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms. Oxford University Press 2020-08-05 /pmc/articles/PMC7671378/ /pubmed/33575609 http://dx.doi.org/10.1093/nargab/lqaa058 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Standard Article
Muñoz-Benavent, Maria
Hartkopf, Felix
Van Den Bossche, Tim
Piro, Vitor C
García-Ferris, Carlos
Latorre, Amparo
Renard, Bernhard Y
Muth, Thilo
gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
title gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
title_full gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
title_fullStr gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
title_full_unstemmed gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
title_short gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
title_sort gnomo: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms
topic Standard Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671378/
https://www.ncbi.nlm.nih.gov/pubmed/33575609
http://dx.doi.org/10.1093/nargab/lqaa058
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