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Article

General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

by 1, 2 and 2,3,4,5,*
1
Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia
2
Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, SI-1000 Ljubljana, Slovenia
3
Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, SI-1000 Ljubljana, Slovenia
4
Department of Automation, Jožef Stefan Institute, Biocybernetics and Robotics, Jamova 39, SI-1000 Ljubljana, Slovenia
5
Department of Microbiology, University of Innsbruck, Technikerstrasse 25d, A-6020 Innsbruck, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Marika Cordaro, Rosalba Siracusa and Cholsoon Jang
Metabolites 2021, 11(6), 336; https://doi.org/10.3390/metabo11060336
Received: 9 April 2021 / Revised: 14 May 2021 / Accepted: 23 May 2021 / Published: 24 May 2021
(This article belongs to the Special Issue Metabolites: From Physiology to Pathology)
General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine. View Full-Text
Keywords: 16S rRNA; amplicon; Mothur; PICRUSt 2; Piphillin; genus; OTU; ASV; predicted metagenomes; predicted enzymatic reactions; predicted metabolic pathways; reproducible analyses; human microbiome; gut; intestine; mice 16S rRNA; amplicon; Mothur; PICRUSt 2; Piphillin; genus; OTU; ASV; predicted metagenomes; predicted enzymatic reactions; predicted metabolic pathways; reproducible analyses; human microbiome; gut; intestine; mice
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MDPI and ACS Style

Murovec, B.; Deutsch, L.; Stres, B. General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways. Metabolites 2021, 11, 336. https://doi.org/10.3390/metabo11060336

AMA Style

Murovec B, Deutsch L, Stres B. General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways. Metabolites. 2021; 11(6):336. https://doi.org/10.3390/metabo11060336

Chicago/Turabian Style

Murovec, Boštjan, Leon Deutsch, and Blaž Stres. 2021. "General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways" Metabolites 11, no. 6: 336. https://doi.org/10.3390/metabo11060336

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