Dataset Information

GcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data.

ABSTRACT: Meta-omics approaches have been increasingly used to study the structure and function of the microbial communities. A variety of large-scale collaborative projects are being conducted to encompass samples from diverse environments and habitats. This change has resulted in enormous demands for long-term data maintenance and capacity for data analysis. The Global Catalogue of Metagenomics (gcMeta) is a part of the 'Chinese Academy of Sciences Initiative of Microbiome (CAS-CMI)', which focuses on studying the human and environmental microbiome, establishing depositories of samples, strains and data, as well as promoting international collaboration. To accommodate and rationally organize massive datasets derived from several thousands of human and environmental microbiome samples, gcMeta features a database management system for archiving and publishing data in a standardized way. Another main feature is the integration of more than ninety web-based data analysis tools and workflows through a Docker platform which enables data analysis by using various operating systems. This platform has been rapidly expanding, and now hosts data from the CAS-CMI and a number of other ongoing research projects. In conclusion, this platform presents a powerful and user-friendly service to support worldwide collaborative efforts in the field of meta-omics research. This platform is freely accessible at https://gcmeta.wdcm.org/.

SUBMITTER: Shi W

PROVIDER: S-EPMC6324004 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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gcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data.

Shi Wenyu W Qi Heyuan H Sun Qinglan Q Fan Guomei G Liu Shuangjiang S Wang Jun J Zhu Baoli B Liu Hongwei H Zhao Fangqing F Wang Xiaochen X Hu Xiaoxuan X Li Wei W Liu Jia J Tian Ye Y Wu Linhuan L Ma Juncai J

Nucleic acids research 20190101 D1

Meta-omics approaches have been increasingly used to study the structure and function of the microbial communities. A variety of large-scale collaborative projects are being conducted to encompass samples from diverse environments and habitats. This change has resulted in enormous demands for long-term data maintenance and capacity for data analysis. The Global Catalogue of Metagenomics (gcMeta) is a part of the 'Chinese Academy of Sciences Initiative of Microbiome (CAS-CMI)', which focuses on s ...[more]

PMID: 30365027

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Project description:BackgroundSeveral studies have examined gut microbiota and sarcopenia using 16S ribosomal RNA amplicon sequencing; however, this technique may not be able to identify altered specific species and functional capacities of the microbes. We performed shotgun metagenomic sequencing to compare the gut microbiome composition and function between individuals with and without sarcopenia.MethodsParticipants were from a community-based observational study conducted among the residents of rural areas in China. Appendicular skeletal muscle mass was assessed using direct segmental multi-frequency bioelectrical impedance and grip strength using a Jamar Hydraulic Hand dynamometer. Physical performance was evaluated using the Short Physical Performance Battery, 5-time chair stand test and gait speed with the 6 m walk test. Sarcopenia and its severity were diagnosed according to the Asian Working Group for Sarcopenia 2019 algorithm. The gut microbiome was profiled by shotgun metagenomic sequencing to determine the microbial composition and function. A gut microbiota-based model for classification of sarcopenia was constructed using the random forest model, and its performance was assessed using the area under receiver-operating characteristic curve (AUC).ResultsThe study sample included 1417 participants (women: 58.9%; mean age: 63.3 years; sarcopenia prevalence: 10.0%). β-diversity indicated by Bray-Curtis distance (genetic level: P = 0.004; taxonomic level of species: P = 0.020), but not α-diversity indicated by Shannon index (genetic level: P = 0.962; taxonomic level of species: P = 0.922), was significantly associated with prevalent sarcopenia. After adjusting for potential confounders, participants with sarcopenia had higher relative abundance of Desulfovibrio piger (P = 0.003, Q = 0.090), Clostridium symbiosum (P < 0.001, Q = 0.035), Hungatella effluvii (P = 0.003, Q = 0.090), Bacteroides fluxus (P = 0.002, Q = 0.089), Absiella innocuum (P = 0.002, Q = 0.072), Coprobacter secundus (P = 0.002, Q = 0.085) and Clostridium citroniae (P = 0.001, Q = 0.060) than those without sarcopenia. The relative abundance of six species (Desulfovibrio piger, Clostridium symbiosum, Hungatella effluvii, Bacteroides fluxus, Absiella innocuum, and Clostridium citroniae) was also positively associated with sarcopenia severity. A differential species-based model was constructed to separate participants with sarcopenia from controls. The value of the AUC was 0.852, suggesting that model has a decent discriminative performance. Desulfovibrio piger ranked the highest in this model. Functional annotation analysis revealed that the phenylalanine, tyrosine, and tryptophan biosynthesis were depleted (P = 0.006, Q = 0.071), while alpha-Linolenic acid metabolism (P = 0.008, Q = 0.094), furfural degradation (P = 0.001, Q = 0.029) and staurosporine biosynthesis (P = 0.006, Q = 0.072) were enriched in participants with sarcopenia. Desulfovibrio piger was significantly associated with staurosporine biosynthesis (P < 0.001).ConclusionsThis large population-based observational study provided empirical evidence that alterations in the gut microbiome composition and function were observed among individuals with sarcopenia.

Project description:BackgroundThe rapid advances in next-generation sequencing technologies have revolutionized the microbiome research by greatly increasing our ability to understand diversity of microbes in a given sample. Over the past decade, several computational pipelines have been developed to efficiently process and annotate these microbiome data. However, most of these pipelines require an implementation of additional tools for downstream analyses as well as advanced programming skills.ResultsHere we introduce a user-friendly microbiome analysis platform, EzMAP (Easy Microbiome Analysis Platform), which was developed using Java Swings, Java Script and R programming language. EzMAP is a standalone package providing graphical user interface, enabling easy access to all the functionalities of QIIME2 (Quantitative Insights Into Microbial Ecology) as well as streamlined downstream analyses using QIIME2 output as input. This platform is designed to give users the detailed reports and the intermediate output files that are generated progressively. The users are allowed to download the features/OTU table (.biom;.tsv;.xls), representative sequences (.fasta) and phylogenetic tree (.nwk), taxonomy assignment file (optional). For downstream analyses, users are allowed to perform relative abundances (at all taxonomical levels), community comparison (alpha and beta diversity, core microbiome), differential abundances (DESeq2 and linear discriminant analysis) and functional prediction (PICRust, Tax4Fun and FunGuilds). Our case study using a published rice microbiome dataset demonstrates intuitive user interface and great accessibility of the EzMAP.ConclusionsThis EzMAP allows users to consolidate the microbiome analysis processes from raw sequence processing to downstream analyses specific for individual projects. We believe that this will be an invaluable tool for the beginners in their microbiome data analysis. This platform is freely available at https://github.com/gnanibioinfo/EzMAP and will be continually updated for adoption of changes in methods and approaches.

Dataset Information

GcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data.

Publications

gcMeta: a Global Catalogue of Metagenomics platform to support the archiving, standardization and analysis of microbiome data.

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