Dataset Information

MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

ABSTRACT: As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1-3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community's data analysis tasks.

SUBMITTER: Meyer F

PROVIDER: S-EPMC6781595 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

Meyer Folker F Bagchi Saurabh S Chaterji Somali S Gerlach Wolfgang W Grama Ananth A Harrison Travis T Paczian Tobias T Trimble William L WL Wilke Andreas A

Briefings in bioinformatics 20190701 4

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1-3] is an example; we use existin ...[more]

PMID: 29028869

Similar Datasets

Project description:BackgroundOral microbiota is considered as the second most complex in the human body and its dysbiosis can be responsible for oral diseases. Interactions between the microorganism communities and the host allow establishing the microbiological proles. Identifying the core microbiome is essential to predicting diseases and changes in environmental behavior from microorganisms.MethodsProjects containing the term "SALIVA", deposited between 2014 and 2019 were recovered on the MG-RAST portal. Quality (Failed), taxonomic prediction (Unknown and Predicted), species richness (Rarefaction), and species diversity (Alpha) were analyzed according to sequencing approaches (Amplicon sequencing and Shotgun metagenomics). All data were checked for normality and homoscedasticity. Metagenomic projects were compared using the Mann-Whitney U test and Spearman's correlation. Microbiome cores were inferred by Principal Component Analysis. For all statistical tests, p < 0.05 was used.ResultsThe study was performed with 3 projects, involving 245 Amplicon and 164 Shotgun metagenome datasets. All comparisons of variables, according to the type of sequencing, showed significant differences, except for the Predicted. In Shotgun metagenomics datasets the highest correlation was between Rarefaction and Failed (r = - 0.78) and the lowest between Alpha and Unknown (r = - 0.12). In Amplicon sequencing datasets, the variables Rarefaction and Unknown (r = 0.63) had the highest correlation and the lowest was between Alpha and Predicted (r = - 0.03). Shotgun metagenomics datasets showed a greater number of genera than Amplicon. Propionibacterium, Lactobacillus, and Prevotella were the most representative genera in Amplicon sequencing. In Shotgun metagenomics, the most representative genera were Escherichia, Chitinophaga, and Acinetobacter.ConclusionsCore of the salivary microbiome and genera diversity are dependent on the sequencing approaches. Available data suggest that Shotgun metagenomics and Amplicon sequencing have similar sensitivities to detect the taxonomic level investigated, although Shotgun metagenomics allows a deeper analysis of the microorganism diversity. Microbiome studies must consider characteristics and limitations of the sequencing approaches. Were identified 20 genera in the core of saliva microbiome, regardless of the health condition of the host. Some bacteria of the core need further study to better understand their role in the oral cavity.

Dataset Information

MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

Publications

MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets