Altenbuchinger, Michael

[["dc.bibliographiccitation.firstpage","3596"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Journal of Proteome Research"],["dc.bibliographiccitation.lastpage","3605"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Rehberg, Thorsten"],["dc.contributor.author","Mehrl, Sebastian"],["dc.contributor.author","Richtmann, Daniel"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Gronwald, Wolfram"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.date.accessioned","2021-09-17T09:47:02Z"],["dc.date.available","2021-09-17T09:47:02Z"],["dc.date.issued","2017"],["dc.description.abstract","Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to 1H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum ."],["dc.identifier.arxiv","1703.07724v1"],["dc.identifier.doi","10.1021/acs.jproteome.7b00325"],["dc.identifier.pmid","28825821"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89638"],["dc.language.iso","en"],["dc.relation.eissn","1535-3907"],["dc.relation.issn","1535-3893"],["dc.title","Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","75"],["dc.bibliographiccitation.lastpage","89"],["dc.contributor.author","Görtler, Franziska"],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.editor","Raphael, Benjamin J."],["dc.date.accessioned","2021-09-17T09:46:30Z"],["dc.date.available","2021-09-17T09:46:30Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1007/978-3-319-89929-9_5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89631"],["dc.publisher","Springer"],["dc.relation.conference","22nd Annual International Conference, RECOMB 2018"],["dc.relation.eventend","2018-04-24"],["dc.relation.eventlocation","Paris"],["dc.relation.eventstart","2018-04-21"],["dc.relation.isbn","978-3-319-89928-2"],["dc.relation.isbn","978-3-319-89929-9"],["dc.relation.ispartof","Research in Computational Molecular Biology"],["dc.title","Loss-Function Learning for Digital Tissue Deconvolution"],["dc.type","conference_paper"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1796"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of Proteome Research"],["dc.bibliographiccitation.lastpage","1805"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Schultheiss, Ulla T."],["dc.contributor.author","Samol, Claudia"],["dc.contributor.author","Kotsis, Fruzsina"],["dc.contributor.author","Poguntke, Inga"],["dc.contributor.author","Sekula, Peggy"],["dc.contributor.author","Krumsiek, Jan"],["dc.contributor.author","Köttgen, Anna"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Gronwald, Wolfram"],["dc.date.accessioned","2021-09-17T09:47:06Z"],["dc.date.available","2021-09-17T09:47:06Z"],["dc.date.issued","2019"],["dc.description.abstract","Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca2+-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment."],["dc.identifier.doi","10.1021/acs.jproteome.8b00983"],["dc.identifier.pmid","30817158"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89639"],["dc.language.iso","en"],["dc.relation.eissn","1535-3907"],["dc.relation.issn","1535-3893"],["dc.title","A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["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.artnumber","13954"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Schäfer, Andreas"],["dc.contributor.author","Büyüközkan, Mustafa"],["dc.contributor.author","Schultheiß, Ulla T."],["dc.contributor.author","Kotsis, Fruzsina"],["dc.contributor.author","Köttgen, Anna"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Krumsiek, Jan"],["dc.contributor.author","Gronwald, Wolfram"],["dc.date.accessioned","2021-09-17T09:47:11Z"],["dc.date.available","2021-09-17T09:47:11Z"],["dc.date.issued","2019"],["dc.description.abstract","Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To this end, omics data are integrated with other data types, e.g., clinical, phenotypic, and demographic parameters of categorical or continuous nature. We exemplify this data integration issue for a chronic kidney disease (CKD) study, comprising complex clinical, demographic, and one-dimensional 1H nuclear magnetic resonance metabolic variables. Routine analysis screens for associations of single metabolic features with clinical parameters while accounting for confounders typically chosen by expert knowledge. This knowledge can be incomplete or unavailable. We introduce a framework for data integration that intrinsically adjusts for confounding variables. We give its mathematical and algorithmic foundation, provide a state-of-the-art implementation, and evaluate its performance by sanity checks and predictive performance assessment on independent test data. Particularly, we show that discovered associations remain significant after variable adjustment based on expert knowledge. In contrast, we illustrate that associations discovered in routine univariate screening approaches can be biased by incorrect or incomplete expert knowledge. Our data integration approach reveals important associations between CKD comorbidities and metabolites, including novel associations of the plasma metabolite trimethylamine-N-oxide with cardiac arrhythmia and infarction in CKD stage 3 patients."],["dc.identifier.doi","10.1038/s41598-019-50346-2"],["dc.identifier.pmid","31562371"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89640"],["dc.language.iso","en"],["dc.relation.issn","2045-2322"],["dc.title","A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","386"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Computational Biology"],["dc.bibliographiccitation.lastpage","389"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Schön, Marian"],["dc.contributor.author","Simeth, Jakob"],["dc.contributor.author","Heinrich, Paul"],["dc.contributor.author","Görtler, Franziska"],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Spang, Rainer"],["dc.date.accessioned","2021-09-17T09:47:29Z"],["dc.date.available","2021-09-17T09:47:29Z"],["dc.date.issued","2020"],["dc.description.abstract","Digital tissue deconvolution (DTD) estimates the cellular composition of a tissue from its bulk gene-expression profile. For this, DTD approximates the bulk as a mixture of cell-specific expression profiles. Different tissues have different cellular compositions, with cells in different activation states, and embedded in different environments. Consequently, DTD can profit from tailoring the deconvolution model to a specific tissue context. Loss-function learning adapts DTD to a specific tissue context, such as the deconvolution of blood, or a specific type of tumor tissue. We provide software for loss-function learning, for its validation and visualization, and for applying the DTD models to new data."],["dc.identifier.doi","10.1089/cmb.2019.0469"],["dc.identifier.pmid","31995409"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89643"],["dc.language.iso","en"],["dc.relation.issn","1557-8666"],["dc.title","DTD: An R Package for Digital Tissue Deconvolution"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","7876"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Reinders, Jörg"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Limm, Katharina"],["dc.contributor.author","Schwarzfischer, Philipp"],["dc.contributor.author","Scheidt, Tamara"],["dc.contributor.author","Strasser, Lisa"],["dc.contributor.author","Richter, Julia"],["dc.contributor.author","Szczepanowski, Monika"],["dc.contributor.author","Huber, Christian G."],["dc.contributor.author","Klapper, Wolfram"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Oefner, Peter J."],["dc.date.accessioned","2021-09-17T09:47:17Z"],["dc.date.available","2021-09-17T09:47:17Z"],["dc.date.issued","2020"],["dc.description.abstract","Diffuse large B-cell lymphoma (DLBCL) is commonly classified by gene expression profiling according to its cell of origin (COO) into activated B-cell (ABC)-like and germinal center B-cell (GCB)-like subgroups. Here we report the application of label-free nano-liquid chromatography - Sequential Window Acquisition of all THeoretical fragment-ion spectra - mass spectrometry (nanoLC-SWATH-MS) to the COO classification of DLBCL in formalin-fixed paraffin-embedded (FFPE) tissue. To generate a protein signature capable of predicting Affymetrix-based GCB scores, the summed log2-transformed fragment ion intensities of 780 proteins quantified in a training set of 42 DLBCL cases were used as independent variables in a penalized zero-sum elastic net regression model with variable selection. The eight-protein signature obtained showed an excellent correlation (r = 0.873) between predicted and true GCB scores and yielded only 9 (21.4%) minor discrepancies between the three classifications: ABC, GCB, and unclassified. The robustness of the model was validated successfully in two independent cohorts of 42 and 31 DLBCL cases, the latter cohort comprising only patients aged >75 years, with Pearson correlation coefficients of 0.846 and 0.815, respectively, between predicted and NanoString nCounter based GCB scores. We further show that the 8-protein signature is directly transferable to both a triple quadrupole and a Q Exactive quadrupole-Orbitrap mass spectrometer, thus obviating the need for proprietary instrumentation and reagents. This method may therefore be used for robust and competitive classification of DLBCLs on the protein level."],["dc.identifier.doi","10.1038/s41598-020-64212-z"],["dc.identifier.pmid","32398793"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89641"],["dc.language.iso","en"],["dc.relation.issn","2045-2322"],["dc.title","Platform independent protein-based cell-of-origin subtyping of diffuse large B-cell lymphoma in formalin-fixed paraffin-embedded tissue"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["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"]]

[["dc.bibliographiccitation.firstpage","133"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Cell Systems"],["dc.bibliographiccitation.lastpage","135"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Cho, Hyunghoon"],["dc.contributor.author","Berger, Bonnie"],["dc.contributor.author","Peng, Jian"],["dc.contributor.author","Galitzine, Cyril"],["dc.contributor.author","Vitek, Olga"],["dc.contributor.author","Beltran, Pierre M. Jean"],["dc.contributor.author","Cristea, Ileana M."],["dc.contributor.author","Görtler, Franziska"],["dc.contributor.author","Solbrig, Stefan"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Basso, Rebecca Sarto"],["dc.contributor.author","Hochbaum, Dorit"],["dc.contributor.author","Vandin, Fabio"],["dc.contributor.author","Silverbush, Dana"],["dc.contributor.author","Cristea, Simona"],["dc.contributor.author","Yanovich, Gali"],["dc.contributor.author","Geiger, Tamar"],["dc.contributor.author","Beerenwinkel, Niko"],["dc.contributor.author","Sharan, Roded"],["dc.contributor.author","Zhou, Zhemin"],["dc.contributor.author","Luhmann, Nina"],["dc.contributor.author","Alikhan, Nabil-Fareed"],["dc.contributor.author","Achtman, Mark"],["dc.date.accessioned","2021-09-17T09:46:42Z"],["dc.date.available","2021-09-17T09:46:42Z"],["dc.date.issued","2018"],["dc.description.abstract","This month: selected work from the 2018 RECOMB meeting, organized by Ecole Polytechnique and held last April in Paris."],["dc.identifier.doi","10.1016/j.cels.2018.08.005"],["dc.identifier.pmid","30138580"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89634"],["dc.language.iso","en"],["dc.relation.issn","2405-4712"],["dc.title","Principles of Systems Biology, No. 31"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]