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Liebsch C, Pitchika V, Pink C, Samietz S, Kastenmüller G, Artati A, Suhre K, Adamski J, Nauck M, Völzke H, Friedrich N, Kocher T, Holtfreter B, Pietzner M. The Saliva Metabolome in Association to Oral Health Status. J Dent Res 2019; 98:642-651. [PMID: 31026179 DOI: 10.1177/0022034519842853] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites ( n = 284) obtained by mass spectrometry among a subsample ( n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites ( n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.
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Affiliation(s)
- C Liebsch
- 1 Unit of Periodontology, Department of Restorative Dentistry, Periodontology, Endodontology, and Pediatric and Preventive Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - V Pitchika
- 1 Unit of Periodontology, Department of Restorative Dentistry, Periodontology, Endodontology, and Pediatric and Preventive Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - C Pink
- 1 Unit of Periodontology, Department of Restorative Dentistry, Periodontology, Endodontology, and Pediatric and Preventive Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - S Samietz
- 2 Department of Prosthetic Dentistry, Gerodontology and Biomaterials, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - G Kastenmüller
- 3 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - A Artati
- 4 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany
| | - K Suhre
- 3 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.,5 Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - J Adamski
- 4 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany.,6 Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany.,7 German Center for Diabetes Research (DZD e.V.), München-Neuherberg, Germany
| | - M Nauck
- 8 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.,9 DZHK (German Center for Cardiovascular Research), Greifswald, Germany
| | - H Völzke
- 9 DZHK (German Center for Cardiovascular Research), Greifswald, Germany.,10 Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - N Friedrich
- 8 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.,9 DZHK (German Center for Cardiovascular Research), Greifswald, Germany
| | - T Kocher
- 1 Unit of Periodontology, Department of Restorative Dentistry, Periodontology, Endodontology, and Pediatric and Preventive Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - B Holtfreter
- 1 Unit of Periodontology, Department of Restorative Dentistry, Periodontology, Endodontology, and Pediatric and Preventive Dentistry, Dental School, University Medicine Greifswald, Greifswald, Germany
| | - M Pietzner
- 8 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.,9 DZHK (German Center for Cardiovascular Research), Greifswald, Germany
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Tsepilov YA, Sharapov SZ, Zaytseva OO, Krumsiek J, Prehn C, Adamski J, Kastenmüller G, Wang-Sattler R, Strauch K, Gieger C, Aulchenko YS. A network-based conditional genetic association analysis of the human metabolome. Gigascience 2018; 7:5214749. [PMID: 30496450 PMCID: PMC6287100 DOI: 10.1093/gigascience/giy137] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 11/06/2018] [Indexed: 12/24/2022] Open
Abstract
Background Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics (“omics”), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods. Results To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites. Conclusions We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.
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Affiliation(s)
- Y A Tsepilov
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Lavrentieva Ave. 10, 630090, Russia.,Natural Scince Department, Novosibirsk State University, Novosibirsk, Pirogova Str. 1, 630090, Russia
| | - S Z Sharapov
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Lavrentieva Ave. 10, 630090, Russia.,Natural Scince Department, Novosibirsk State University, Novosibirsk, Pirogova Str. 1, 630090, Russia
| | - O O Zaytseva
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Lavrentieva Ave. 10, 630090, Russia.,Natural Scince Department, Novosibirsk State University, Novosibirsk, Pirogova Str. 1, 630090, Russia
| | - J Krumsiek
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany
| | - C Prehn
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany
| | - J Adamski
- Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.,Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Arcisstrasse 21, 80333, Germany.,German Center for Diabetes Research, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany
| | - G Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany
| | - R Wang-Sattler
- German Center for Diabetes Research, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.,Institute of Epidemiology II, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany
| | - K Strauch
- Institute of Genetic Epidemiology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Butenandstrasse 5, 81377, Germany
| | - C Gieger
- German Center for Diabetes Research, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.,Research Unit of Molecular Epidemiology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany.,Institute of Epidemiology II, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Ingolstadter Landtrasse 1, 85764, Germany
| | - Y S Aulchenko
- Institute of Cytology and Genetics SB RAS, Novosibirsk, Lavrentieva Ave. 10, 630090, Russia.,Natural Scince Department, Novosibirsk State University, Novosibirsk, Pirogova Str. 1, 630090, Russia.,PolyOmica, 's-Hertogenbosch, Het Vlaggeschip 61, 5237 PA, The Netherlands
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Koch M, Freitag-Wolf S, Schlesinger S, Borggrefe J, Hov JR, Jensen MK, Pick J, Markus MRP, Höpfner T, Jacobs G, Siegert S, Artati A, Kastenmüller G, Römisch-Margl W, Adamski J, Illig T, Nothnagel M, Karlsen TH, Schreiber S, Franke A, Krawczak M, Nöthlings U, Lieb W. Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample. Eur J Clin Nutr 2017; 71:995-1001. [DOI: 10.1038/ejcn.2017.43] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 02/22/2017] [Accepted: 03/01/2017] [Indexed: 01/02/2023]
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Much D, Beyerlein A, Kindt A, Krumsiek J, Stückler F, Rossbauer M, Hofelich A, Wiesenäcker D, Hivner S, Herbst M, Römisch-Margl W, Prehn C, Adamski J, Kastenmüller G, Theis F, Ziegler AG, Hummel S. Lactation is associated with altered metabolomic signatures in women with gestational diabetes. DIABETOL STOFFWECHS 2016. [DOI: 10.1055/s-0036-1580927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Mook-Kanamori DO, Römisch-Margl W, Kastenmüller G, Prehn C, Petersen AK, Illig T, Gieger C, Wang-Sattler R, Meisinger C, Peters A, Adamski J, Suhre K. Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up. J Endocrinol Invest 2014; 37:369-74. [PMID: 24682914 PMCID: PMC3972444 DOI: 10.1007/s40618-013-0044-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Accepted: 12/10/2013] [Indexed: 02/01/2023]
Abstract
BACKGROUND Recently, five branched-chain and aromatic amino acids were shown to be associated with the risk of developing type 2 diabetes (T2D). AIM We set out to examine whether amino acids are also associated with the development of hypertriglyceridemia. MATERIALS AND METHODS We determined the serum amino acids concentrations of 1,125 individuals of the KORA S4 baseline study, for which follow-up data were available also at the KORA F4 7 years later. After exclusion for hypertriglyceridemia (defined as having a fasting triglyceride level above 1.70 mmol/L) and diabetes at baseline, 755 subjects remained for analyses. RESULTS Increased levels of leucine, arginine, valine, proline, phenylalanine, isoleucine and lysine were significantly associated with an increased risk of hypertriglyceridemia. These associations remained significant when restricting to those individuals who did not develop T2D in the 7-year follow-up. The increase per standard deviation of amino acid level was between 26 and 40 %. CONCLUSIONS Seven amino acids were associated with an increased risk of developing hypertriglyceridemia after 7 years. Further studies are necessary to elucidate the complex role of these amino acids in the pathogenesis of metabolic disorders.
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Affiliation(s)
- D. O. Mook-Kanamori
- Department of Physiology and Biophysics, Weill Cornell Medical College, Qatar, PO Box 24144 Doha, Qatar
- Department of Endocrinology and Metabolic Diseases, Leiden University Medical Centre, Leiden, The Netherlands
| | - W. Römisch-Margl
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Bioinformatics and Systems Biology, Neuherberg, Germany
| | - G. Kastenmüller
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Bioinformatics and Systems Biology, Neuherberg, Germany
| | - C. Prehn
- Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Neuherberg, Germany
| | - A. K. Petersen
- Research Unit of Molecular Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - T. Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - C. Gieger
- Research Unit of Molecular Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - R. Wang-Sattler
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Genetic Epidemiology, Neuherberg, Germany
| | - C. Meisinger
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany
| | - A. Peters
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany
| | - J. Adamski
- Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics, Neuherberg, Germany
- Technische Universität München, Munich, Germany
| | - K. Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College, Qatar, PO Box 24144 Doha, Qatar
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Bioinformatics and Systems Biology, Neuherberg, Germany
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Menni C, Kastenmüller G, Petersen AK, Bell JT, Psatha M, Tsai PC, Gieger C, Schulz H, Erte I, John S, Brosnan MJ, Wilson SG, Tsaprouni L, Lim EM, Stuckey B, Deloukas P, Mohney R, Suhre K, Spector TD, Valdes AM. Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol 2013; 42:1111-9. [PMID: 23838602 PMCID: PMC3781000 DOI: 10.1093/ije/dyt094] [Citation(s) in RCA: 180] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age. METHODS Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels. RESULTS We identified a panel of 22 metabolites which combined are strongly correlated with age (R(2) = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10(-157)) and lung function (FEV1 beta = -0.04, SE = 0.008, P = 1.8 × 10(-8) adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = -0.01, SE = 0.002, P = 1.9 × 10(-6)) and birthweight (beta = -0.06, SE = 0.01, P = 2.5 × 10(-9)). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10(-6)). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = -0.20, SE = 0.04, P = 2.9 × 10(-8)). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes. CONCLUSIONS Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.
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Affiliation(s)
- Cristina Menni
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK, Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany, Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany, Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg, Germany, Pfizer Research Laboratories, Groton, CT, USA, Worldwide R&D, Pfizer Inc., Cambridge, MA, USA, School of Medicine and Pharmacology, University of Western Australia, Crawley, WA, Australia, Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, WA, Australia, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK, Metabolon Inc., 617 Davis Drive, Durham, NC 27713, USA; Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, State of Qatar and Academic Rheumatology, University of Nottingham, Nottingham City Hospital, Nottingham, UK
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Flexeder C, Karrasch S, Kastenmüller G, Meisinger C, Petersen AP, Prehn C, Wang-Sattler R, Weidinger S, Gieger C, Heinrich J, Holle R, Peters A, Illig T, Adamski J, Suhre K, Schulz H. Are metabolomic markers associated with spirometric lung function indices? Results of the KORAF4 Study. Pneumologie 2013. [DOI: 10.1055/s-0033-1334761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Güldener U, Münsterkötter M, Kastenmüller G, Strack N, van Helden J, Lemer C, Richelles J, Wodak SJ, García-Martínez J, Pérez-Ortín JE, Michael H, Kaps A, Talla E, Dujon B, André B, Souciet JL, De Montigny J, Bon E, Gaillardin C, Mewes HW. CYGD: the Comprehensive Yeast Genome Database. Nucleic Acids Res 2005; 33:D364-8. [PMID: 15608217 PMCID: PMC540007 DOI: 10.1093/nar/gki053] [Citation(s) in RCA: 208] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/.
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Affiliation(s)
- U Güldener
- Institute for Bioinformatics, GSF National Research Center for Environment and Health, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
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Abstract
SUMMARY The HumanInfoBase (HIB) is a database of putative human gene transcripts. UniGene clusters are assembled, and the resulting consensus sequences are submitted to the PEDANT software system (Frishman,D., Albermann,K., Hani,J., Heumann,K., Metanomski,A., Zollner,A. and Mewes,H.-W., 2001, Bioinformatics, 17, 44--57) for fully automatic sequence analysis and annotation. Predicted transcripts are classified using a variety of functional and structural categories, and hyperlinks to various databases are provided for additional information. A WWW-based graphical user interface represents the assembly process as well as functionally important sites in the putative transcripts.
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Affiliation(s)
- B Geier
- MIPS Instituti für Bioinformatik GSF--Forschungszentrum für Umwelt und Gesundheit, GmbH Ingolstädter Landstrasse, 1 85764 Neuherberg, Germany
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Ankerst M, Kastenmüller G, Kriegel HP, Seidl T. Nearest neighbor classification in 3D protein databases. Proc Int Conf Intell Syst Mol Biol 2000:34-43. [PMID: 10786284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
In molecular databases, structural classification is a basic task that can be successfully approached by nearest neighbor methods. The underlying similarity models consider spatial properties such as shape and extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful approach to model similarity for solid objects such as molecules. Errors of measurement, sampling, and numerical rounding may result in small displacements of atomic coordinates. These effects may be handled by using quadratic form distance functions. An efficient processing of similarity queries based on quadratic forms is supported by a filter-refinement architecture. Experiments on our 3D protein database demonstrate the high classification accuracy of more than 90% and the good performance of the technique.
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Affiliation(s)
- M Ankerst
- University of Munich, Institute for Computer Science, Germany
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