Altenbuchinger, Michael

[["dc.bibliographiccitation.firstpage","452"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Metabolites"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Häckl, Martina"],["dc.contributor.author","Tauber, Philipp"],["dc.contributor.author","Schweda, Frank"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Gronwald, Wolfram"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.date.accessioned","2021-09-01T06:43:05Z"],["dc.date.available","2021-09-01T06:43:05Z"],["dc.date.issued","2021"],["dc.description.abstract","NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity."],["dc.description.abstract","NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft"],["dc.identifier.doi","10.3390/metabo11070452"],["dc.identifier.pii","metabo11070452"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89214"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.publisher","MDPI"],["dc.relation.eissn","2218-1989"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","460"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Metabolites"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Schultheiss, Ulla T."],["dc.contributor.author","Kosch, Robin"],["dc.contributor.author","Kotsis, Fruzsina"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.date.accessioned","2021-08-12T07:46:02Z"],["dc.date.available","2021-08-12T07:46:02Z"],["dc.date.issued","2021"],["dc.description.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."],["dc.description.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."],["dc.description.sponsorship","Bundesministerium für Bildung, Wissenschaft und Forschung"],["dc.identifier.doi","10.3390/metabo11070460"],["dc.identifier.pii","metabo11070460"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88601"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.publisher","MDPI"],["dc.relation.eissn","2218-1989"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","812"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Metabolites"],["dc.bibliographiccitation.volume","12"],["dc.contributor.affiliation","Altenbuchinger, Michael; 1Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Berndt, Henry; 2Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany"],["dc.contributor.affiliation","Kosch, Robin; 1Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Lang, Iris; 4Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany"],["dc.contributor.affiliation","Dönitz, Jürgen; 1Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany"],["dc.contributor.affiliation","Oefner, Peter J.; 4Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany"],["dc.contributor.affiliation","Gronwald, Wolfram; 4Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany"],["dc.contributor.affiliation","Zacharias, Helena U.; 2Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Berndt, Henry"],["dc.contributor.author","Kosch, Robin"],["dc.contributor.author","Lang, Iris"],["dc.contributor.author","Dönitz, Jürgen"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Gronwald, Wolfram"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Investigators GCKD Study,"],["dc.contributor.authorgroup","Investigators GCKD Study"],["dc.contributor.editor","Moseley, Hunter N. B."],["dc.date.accessioned","2022-10-04T10:22:18Z"],["dc.date.available","2022-10-04T10:22:18Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-11T13:14:55Z"],["dc.description.abstract","Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers."],["dc.description.sponsorship","German Federal Ministry of Education and Research (BMBF)"],["dc.identifier.doi","10.3390/metabo12090812"],["dc.identifier.pii","metabo12090812"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/114637"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-600"],["dc.publisher","MDPI"],["dc.relation.eissn","2218-1989"],["dc.rights","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/)."],["dc.title","Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]