ASaiM: a Galaxy-based framework to analyze microbiota data

Batut, Bérénice; Gravouil, Kévin; Defois, Clémence; Hiltemann, Saskia; Brugère, Jean-François; Peyretaillade, Éric; Peyret, Pierre

doi:10.1093/gigascience/giy057

Cited by 29 publications

(20 citation statements)

References 53 publications

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“…As such, we envision metaQuantome to fit into a variety of metapro- teomic workflows, Galaxy-based or otherwise. It also offers a chance for comparison to, or potentially integration with, other multi-omic workflows for microbiome characterization, such as existing quantitative metatranscriptomics workflows (50). metaQuantome should offer new possibilities and empower users to perform much deeper and advanced multiomic studies.…”

Section: Discussionmentioning

confidence: 99%

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes

Easterly

Sajulga

Mehta

et al. 2019

Molecular & Cellular Proteomics

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In Brief metaQuantome enables quantitative analysis of the taxonomic and functional state of a microbiome. Leveraging quantitative mass spectrometry data generated from metaproteomic samples along with taxonomic and functional annotations, metaQuantome unravels the complex and hierarchical data structure of taxonomic and functional ontologies. As a result, metaQuantome enables data exploration, tests hypotheses, and generates high-quality visualizations. metaQuantome deciphers the contribution of taxa to a functional process and vice versa. Its accessibility will pave the way for advanced multi-omic analysis of diverse microbiomes.

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Section: Discussionmentioning

confidence: 99%

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes

Easterly

Sajulga

Mehta

et al. 2019

Molecular & Cellular Proteomics

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“…The Illumina MiSeq sequence data were analyzed using bioinformatics modules present in the Mothur software package [21] that we previously integrated into Galaxy (i.e. Galaxy mothur Toolset, Gm [22]). In short, forward and reverse FASTQ-formatted sequence files were merged using the make.contigs command.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Illumina Versus Nanopore 16S rRNA Gene Sequencing of the Human Nasal Microbiota

Heikema¹,

Horst-Kreft²,

Boers³

et al. 2020

Preprint

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Illumina and nanopore sequencing technologies are powerful tools that can be used to determine the bacterial composition of complex microbial communities. In this study, we compared nasal microbiota results at genus level using both Illumina and nanopore 16S rRNA gene sequencing. We also monitored the progression of nanopore sequencing in the accurate identification of species, using pure, single species cultures, and evaluated the performance of the nanopore EPI2ME 16S data analysis pipeline. Fifty-nine nasal swabs were sequenced using Illumina MiSeq and Oxford Nanopore 16S rRNA gene sequencing technologies. In addition, five pure cultures of relevant bacterial species were sequenced with the nanopore sequencing technology. The Illumina MiSeq sequence data were processed using bioinformatics modules present in the Mothur software package. Albacore and Guppy base calling, a workflow in nanopore EPI2ME and an in house developed bioinformatics script were used to analyze the nanopore data. At genus level, similar bacterial diversity profiles were found, and five main and established genera were identified by both platforms. However, probably due to mismatching of the nanopore sequence primers, the nanopore sequencing platform identified Corynebacterium in much lower abundance compared to Illumina sequencing. Further, when using default settings in the EPI2ME workflow, almost all sequence reads that seem to belong to the bacterial genus Dolosigranulum and a considerable part to the genus Haemophilus were only identified at family level. Nanopore sequencing of single species cultures demonstrated at least 88% accurate identification of the species at genus and species level for 4/5 strains tested, including improvements in accurate sequence read identification when the basecaller Guppy and Albacore, and when flowcell versions R9.4 and R9.2 were compared.

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“…Several web applications were developed to analyze data of metataxonomic studies, notably, FROGS [ 19 ], ASaiM [ 20 ], Qiita [ 21 ] as well as MetaDEGalaxy [ 22 ] for bioinformatic data processing, Shiny-phyloseq [ 23 ] for statistical analysis, Metaviz [ 24 ] and VAMPS2 [ 25 ] that make a particular focus on data visualization. While these interfaces propose related functionalities, the main specificity of SHAMAN is to combine of all these steps in a single user-friendly application.…”

Section: Introductionmentioning

confidence: 99%

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

et al. 2020

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Background Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical display. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some cases, programming skills. Results Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide the largest panel of interactive visualizations among the applications that are currently available. SHAMAN is specifically designed for non-expert users. A strong benefit is to use an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/ , and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman . Using two different datasets (a mock community sequencing and a published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis. Conclusions With SHAMAN, we aim at providing the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

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ASaiM: a Galaxy-based framework to analyze microbiota data

Cited by 29 publications

References 53 publications

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes

metaQuantome: An Integrated, Quantitative Metaproteomics Approach Reveals Connections Between Taxonomy and Protein Function in Complex Microbiomes

Comparison of Illumina Versus Nanopore 16S rRNA Gene Sequencing of the Human Nasal Microbiota

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

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