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      Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

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          Abstract

          Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Gene Ontology: tool for the unification of biology

              Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                16 July 2021
                July 2021
                : 11
                : 7
                : 460
                Affiliations
                [1 ]Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany; ulla.schultheiss@ 123456uniklinik-freiburg.de (U.T.S.); fruzsina.kotsis@ 123456uniklinik-freiburg.de (F.K.)
                [2 ]Department of Medicine IV—Nephrology and Primary Care, Faculty of Medicine and Medical Center, University of Freiburg, 79106 Freiburg, Germany
                [3 ]Computational Biology, University of Hohenheim, 70599 Stuttgart, Germany; robin.kosch@ 123456uni-hohenheim.de
                [4 ]Institute of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany; michael.altenbuchinger@ 123456med.uni-goettingen.de
                [5 ]Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
                [6 ]Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
                Author notes
                [* ]Correspondence: h.zacharias@ 123456ikmb.uni-kiel.de ; Tel.: +49-(0)431-500-22274
                Author information
                https://orcid.org/0000-0002-7541-5310
                https://orcid.org/0000-0001-5127-3912
                https://orcid.org/0000-0001-5018-9305
                Article
                metabolites-11-00460
                10.3390/metabo11070460
                8304377
                e4e41f3c-49cf-44d7-80d1-fe18ba23f1f1
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 15 June 2021
                : 12 July 2021
                Categories
                Review

                metabolomics study design,nephrology,chronic kidney disease,human cohort studies,epidemiology,kidney disease etiologies

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