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SUMMARY: Recent advances in high-throughput technology have increased the quantity of available data on protein complexes and stimulated the development of many new prediction methods. In this article, we present ProCope, a Java software suite for the prediction and evaluation of protein complexes from affinity purification experiments which integrates the major methods for calculating interaction scores and predicting protein complexes published over the last years. Methods can be accessed via a graphical user interface, command line tools and a Java API. Using ProCope, existing algorithms can be applied quickly and reproducibly on new experimental results, individual steps of the different algorithms can be combined in new and innovative ways and new methods can be implemented and integrated in the existing prediction framework. AVAILABILITY: Source code and executables are available at http://www.bio.ifi.lmu.de/Complexes/ProCope/. 相似文献
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Kieu Trinh Do Simone Wahl Johannes Raffler Sophie Molnos Michael Laimighofer Jerzy Adamski Karsten Suhre Konstantin Strauch Annette Peters Christian Gieger Claudia Langenberg Isobel D. Stewart Fabian J. Theis Harald Grallert Gabi Kastenmüller Jan Krumsiek 《Metabolomics : Official journal of the Metabolomic Society》2018,14(10):128
Background
Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.Methods
We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.Results
Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.Conclusion
Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.3.
Gepard provides a user-friendly, interactive application for the quick creation of dotplots. It utilizes suffix arrays to reduce the time complexity of dotplot calculation to Theta(m*log n). A client-server mode, which is a novel feature for dotplot creation software, allows the user to calculate dotplots and color them by functional annotation without any prior downloading of sequence or annotation data. AVAILABILITY: Both source codes and executable binaries are available at http://mips.gsf.de/services/analysis/gepard 相似文献
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Jan Krumsiek Sebastian Pölsterl Dominik M Wittmann Fabian J Theis 《BMC bioinformatics》2010,11(1):233
Background
Phenomenological information about regulatory interactions is frequently available and can be readily converted to Boolean models. Fully quantitative models, on the other hand, provide detailed insights into the precise dynamics of the underlying system. In order to connect discrete and continuous modeling approaches, methods for the conversion of Boolean systems into systems of ordinary differential equations have been developed recently. As biological interaction networks have steadily grown in size and complexity, a fully automated framework for the conversion process is desirable. 相似文献5.
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Ulrike E. Rolle-Kampczyk Jan Krumsiek Wolfgang Otto Stefan W. Röder Tibor Kohajda Michael Borte Fabian Theis Irina Lehmann M. von Bergen 《Metabolomics : Official journal of the Metabolomic Society》2016,12(4):76
Introduction
A general detrimental effect of smoking during pregnancy on the health of newborn children is well-documented, but the detailed mechanisms remain elusive.Objectives
Beside the specific influence of environmental tobacco smoke derived toxicants on developmental regulation the impact on the metabolism of newborn children is of particular interest, first as a general marker of foetal development and second due to its potential predictive value for the later occurrence of metabolic diseases.Methods
Tobacco smoke exposure information from a questionnaire was confirmed by measuring the smoking related metabolites S-Phenyl mercapturic acid, S-Benzyl mercapturic acid and cotinine in maternal urine by LC–MS/MS. The impact of smoking on maternal endogenous serum metabolome and children’s cord blood metabolome was assessed in a targeted analysis of 163 metabolites by an LC–MS/MS based assay. The anti-oxidative status of maternal serum samples was analysed by chemoluminiscence based method.Results
Here we present for the first time results of a metabolomic assessment of the cordblood of 40 children and their mothers. Several analytes from the group of phosphatidylcholines, namely PCaaC28:1, PCaaC32:3, PCaeC30:1, PCaeC32:2, PCaeC40:1, and sphingomyelin SM C26:0, differed significantly in mothers and children’s sera depending on smoking status. In serum of smoking mothers the antioxidative capacity of water soluble compounds was not significantly changed while there was a significant decrease in the lipid fraction.Conclusion
Our data give evidence that smoking during pregnancy alters both the maternal and children’s metabolome. Whether the different pattern found in adults compared to newborn children could be related to different disease outcomes should be in the focus of future studies.7.
Mittelstrass K Ried JS Yu Z Krumsiek J Gieger C Prehn C Roemisch-Margl W Polonikov A Peters A Theis FJ Meitinger T Kronenberg F Weidinger S Wichmann HE Suhre K Wang-Sattler R Adamski J Illig T 《PLoS genetics》2011,7(8):e1002215
Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation. 相似文献
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Albrecht Eva Waldenberger Melanie Krumsiek Jan Evans Anne M. Jeratsch Ulli Breier Michaela Adamski Jerzy Koenig Wolfgang Zeilinger Sonja Fuchs Christiane Klopp Norman Theis Fabian J. Wichmann H.-Erich Suhre Karsten Illig Thomas Strauch Konstantin Peters Annette Gieger Christian Kastenmüller Gabi Doering Angela Meisinger Christa 《Metabolomics : Official journal of the Metabolomic Society》2014,10(1):141-151
Metabolomics - Serum urate, the final breakdown product of purine metabolism, is causally involved in the pathogenesis of gout, and implicated in cardiovascular disease and type 2 diabetes. Serum... 相似文献
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Dominik M Wittmann Jan Krumsiek Julio Saez-Rodriguez Douglas A Lauffenburger Steffen Klamt Fabian J Theis 《BMC systems biology》2009,3(1):98-21