1
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Kolker E, Özdemir V, Martens L, Hancock W, Anderson G, Anderson N, Aynacioglu S, Baranova A, Campagna SR, Chen R, Choiniere J, Dearth SP, Feng WC, Ferguson L, Fox G, Frishman D, Grossman R, Heath A, Higdon R, Hutz MH, Janko I, Jiang L, Joshi S, Kel A, Kemnitz JW, Kohane IS, Kolker N, Lancet D, Lee E, Li W, Lisitsa A, Llerena A, MacNealy-Koch C, Marshall JC, Masuzzo P, May A, Mias G, Monroe M, Montague E, Mooney S, Nesvizhskii A, Noronha S, Omenn G, Rajasimha H, Ramamoorthy P, Sheehan J, Smarr L, Smith CV, Smith T, Snyder M, Rapole S, Srivastava S, Stanberry L, Stewart E, Toppo S, Uetz P, Verheggen K, Voy BH, Warnich L, Wilhelm SW, Yandl G. Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 2014; 18:10-4. [PMID: 24456465 PMCID: PMC3903324 DOI: 10.1089/omi.2013.0149] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Affiliation(s)
- Eugene Kolker
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Vural Özdemir
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Office of the President, Gaziantep University, International Affairs and Global Development Strategy
- Faculty of Communications, Universite Bulvarı, Kilis Yolu, Turkey
| | - Lennart Martens
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - William Hancock
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, Barnett Institute, Northeastern University, Boston, Massachusetts
| | - Gordon Anderson
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington
| | - Nathaniel Anderson
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Sukru Aynacioglu
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Pharmacology, Gaziantep University, Gaziantep, Turkey
| | - Ancha Baranova
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- School of Systems Biology, George Mason University, Manassas, Virginia
| | - Shawn R. Campagna
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Rui Chen
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - John Choiniere
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Stephen P. Dearth
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Wu-Chun Feng
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Computer Science, Virginia Tech, Blacksburg, Virginia
- Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia
- Department of SyNeRGy Laboratory, Virginia Tech, Blacksburg, Virginia
| | - Lynnette Ferguson
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Nutrition, Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
| | - Geoffrey Fox
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- School of Informatics and Computing, Indiana University, Bloomington, Indiana
| | - Dmitrij Frishman
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Technische Universitat Munchen, Wissenshaftzentrum Weihenstephan, Freising, Germany
| | - Robert Grossman
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
- Department of Medicine, University of Chicago, Chicago, Illinois
| | - Allison Heath
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
- Knapp Center for Biomedical Discovery, University of Chicago, Chicago, Illinois
| | - Roger Higdon
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Mara H. Hutz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Departamento de Genetica, Instituto de Biociencias, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Imre Janko
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Lihua Jiang
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - Sanjay Joshi
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Life Sciences, EMC, Hopkinton, Massachusetts
| | - Alexander Kel
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- GeneXplain GmbH, Wolfenbüttel, Germany
| | - Joseph W. Kemnitz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Isaac S. Kohane
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Pediatrics and Health Sciences Technology, Children's Hospital and Harvard Medical School, Boston, Massachusetts
- HMS Center for Biomedical Informatics, Countway Library of Medicine, Boston, Massachusetts
| | - Natali Kolker
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Doron Lancet
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Molecular Genetics, Crown Human Genome Center, Weizmann Institute of Science, Rehovot, Israel
| | - Elaine Lee
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
| | - Weizhong Li
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Research in Biological Systems, University of California, San Diego, La Jolla, California
| | - Andrey Lisitsa
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Russian Human Proteome Organization (RHUPO), Moscow, Russia
- Institute of Biomedical Chemistry, Moscow, Russia
| | - Adrian Llerena
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Clinical Research Center, Extremadura University Hospital and Medical School, Badajoz, Spain
| | - Courtney MacNealy-Koch
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Jean-Claude Marshall
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Translational Research, Catholic Health Initiatives, Towson, Maryland
| | - Paola Masuzzo
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - Amanda May
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemistry, University of Tennessee Knoxville, Knoxville, Tennessee
| | - George Mias
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
| | - Matthew Monroe
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Elizabeth Montague
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Sean Mooney
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- The Buck Institute for Research on Aging, Novato, California
| | - Alexey Nesvizhskii
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
- Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Santosh Noronha
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India
| | - Gilbert Omenn
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan
- Department of Molecular Medicine & Genetics and Human Genetics, University of Michigan, Ann Arbor Michigan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor Michigan
- School of Public Health, University of Michigan, Ann Arbor Michigan
| | - Harsha Rajasimha
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Jeeva Informatics Solutions LLC, Derwood, Maryland
| | - Preveen Ramamoorthy
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Molecular Diagnostics Department, National Jewish Health, Denver, Colorado
| | - Jerry Sheehan
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- California Institute for Telecommunications and Information Technology, University of California-San Diego, La Jolla, California
| | - Larry Smarr
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- California Institute for Telecommunications and Information Technology, University of California-San Diego, La Jolla, California
| | - Charles V. Smith
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
| | - Todd Smith
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Digital World Biology, Seattle, Washington
| | - Michael Snyder
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Stanford University, Stanford, California
- Stanford Center for Genomics and Personalized Medicine, Stanford University, Stanford, California
| | - Srikanth Rapole
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Proteomics Laboratory, National Centre for Cell Science, University of Pune, Pune, India
| | - Sanjeeva Srivastava
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Proteomics Laboratory, Indian Institute of Technology Bombay, Mumbai, India
| | - Larissa Stanberry
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Elizabeth Stewart
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Stefano Toppo
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Molecular Medicine, University of Padova, Padova, Italy
| | - Peter Uetz
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Center for the Study of Biological Complexity (CSBC), Virginia Commonwealth University, Richmond, Virginia
| | - Kenneth Verheggen
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie, Ghent, Belgium
- Department of Biochemistry, Ghent University; Ghent, Belgium
| | - Brynn H. Voy
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Animal Science, University of Tennessee Institute of Agriculture, Knoxville, Tennessee
| | - Louise Warnich
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Genetics, Faculty of AgriSciences, University of Stellenbosch, Stellenbosch, South Africa
| | - Steven W. Wilhelm
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Department of Microbiology, University of Tennessee-Knoxville, Knoxville, Tennessee
| | - Gregory Yandl
- Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
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Kolker E, Özdemir V, Martens L, Hancock W, Anderson G, Anderson N, Aynacioglu S, Baranova A, Campagna SR, Chen R, Choiniere J, Dearth SP, Feng WC, Ferguson L, Fox G, Frishman D, Grossman R, Heath A, Higdon R, Hutz MH, Janko I, Jiang L, Joshi S, Kel A, Kemnitz JW, Kohane IS, Kolker N, Lancet D, Lee E, Li W, Lisitsa A, Llerena A, MacNealy-Koch C, Marshall JC, Masuzzo P, May A, Mias G, Monroe M, Montague E, Mooney S, Nesvizhskii A, Noronha S, Omenn G, Rajasimha H, Ramamoorthy P, Sheehan J, Smarr L, Smith CV, Smith T, Snyder M, Rapole S, Srivastava S, Stanberry L, Stewart E, Toppo S, Uetz P, Verheggen K, Voy BH, Warnich L, Wilhelm SW, Yandl G. Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications. Big Data 2013; 1:196-201. [PMID: 27447251 DOI: 10.1089/big.2013.0039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
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Affiliation(s)
- Eugene Kolker
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Vural Özdemir
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 4 Office of the President, Gaziantep University , International Affairs and Global Development Strategy
- 5 Faculty of Communications, Universite Bulvarı , Kilis Yolu, Turkey
| | - Lennart Martens
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - William Hancock
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 8 Department of Chemistry, Barnett Institute, Northeastern University , Boston, Massachusetts
| | - Gordon Anderson
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 9 Fundamental & Computational Sciences Directorate, Pacific Northwest National Laboratory , Richland, Washington
| | - Nathaniel Anderson
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Sukru Aynacioglu
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 10 Department of Pharmacology, Gaziantep University , Gaziantep, Turkey
| | - Ancha Baranova
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 11 School of Systems Biology, George Mason University , Manassas, Virginia
| | - Shawn R Campagna
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - Rui Chen
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - John Choiniere
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Stephen P Dearth
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - Wu-Chun Feng
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 14 Department of Computer Science, Virginia Tech, Blacksburg Virginia
- 15 Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg Virginia
- 16 SyNeRGy Laboratory, Virginia Tech, Blacksburg, Virginia
| | - Lynnette Ferguson
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 17 Department of Nutrition, Auckland Cancer Society Research Centre, University of Auckland , Auckland, New Zealand
| | - Geoffrey Fox
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 18 School of Informatics and Computing, Indiana University , Bloomington, Indiana
| | - Dmitrij Frishman
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 19 Technische Universitat Munchen , Wissenshaftzentrum Weihenstephan, Freising, Germany
| | - Robert Grossman
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 20 Institute for Genomics and Systems Biology, University of Chicago , Chicago Illinois
- 21 Department of Medicine, University of Chicago , Chicago, Illinois
| | - Allison Heath
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 20 Institute for Genomics and Systems Biology, University of Chicago , Chicago Illinois
- 22 Knapp Center for Biomedical Discovery, University of Chicago , Chicago, Illinois
| | - Roger Higdon
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Mara H Hutz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 23 Departamento de Genetica, Instituto de Biociencias, Federal University of Rio Grande do Sul , Porto Alegre, Brazil
| | - Imre Janko
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Lihua Jiang
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - Sanjay Joshi
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 25 Life Sciences , EMC, Hopkinton, Massachusetts
| | - Alexander Kel
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 26 GeneXplain GmbH , Wolfenbüttel, Germany
| | - Joseph W Kemnitz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 27 Department of Cell and Regenerative Biology, University of Wisconsin-Madison , Madison, Wisconsin
- 28 Wisconsin National Primate Research Center, University of Wisconsin-Madison , Madison, Wisconsin
| | - Isaac S Kohane
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 29 Pediatrics and Health Sciences Technology, Children's Hospital and Harvard Medical School , Boston, Massachusetts
- 30 HMS Center for Biomedical Informatics, Countway Library of Medicine , Boston, Massachusetts
| | - Natali Kolker
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Doron Lancet
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 31 Department of Molecular Genetics, Crown Human Genome Center , Weizmann Institute of Science, Rehovot, Israel
| | - Elaine Lee
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 24 High-Throughput Analysis Core, Seattle Children's Research Institute , Seattle, Washington
| | - Weizhong Li
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 32 Center for Research in Biological Systems, University of California , San Diego, La Jolla, California
| | - Andrey Lisitsa
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 33 Russian Human Proteome Organization (RHUPO) , Moscow, Russia
- 34 Institute of Biomedical Chemistry , Moscow, Russia
| | - Adrian Llerena
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 35 Clinical Research Center, Extremadura University Hospital and Medical School , Badajoz, Spain
| | - Courtney MacNealy-Koch
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Jean-Claude Marshall
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 36 Center for Translational Research, Catholic Health Initiatives , Towson, Maryland
| | - Paola Masuzzo
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - Amanda May
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 12 Department of Chemistry, University of Tennessee Knoxville , Knoxville, Tennessee
| | - George Mias
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
| | - Matthew Monroe
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 37 Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington
| | - Elizabeth Montague
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Sean Mooney
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 38 The Buck Institute for Research on Aging , Novato, California
| | - Alexey Nesvizhskii
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 39 Department of Pathology, University of Michigan , Ann Arbor, Michigan
- 40 Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
| | - Santosh Noronha
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 41 Department of Chemical Engineering, Indian Institute of Technology Bombay , Powai, Mumbai, India
| | - Gilbert Omenn
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 42 Center for Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
- 43 Departments of Molecular Medicine & Genetics and Human Genetics, University of Michigan , Ann Arbor Michigan
- 44 Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan
- 45 School of Public Health, University of Michigan , Ann Arbor, Michigan
| | - Harsha Rajasimha
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 46 J eeva Informatics Solutions LLC , Derwood, Maryland
| | - Preveen Ramamoorthy
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 47 Molecular Diagnostics Department, National Jewish Health , Denver Colorado
| | - Jerry Sheehan
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 48 California Institute for Telecommunications and Information Technology, University of California-San Diego , La Jolla, California
| | - Larry Smarr
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 48 California Institute for Telecommunications and Information Technology, University of California-San Diego , La Jolla, California
| | - Charles V Smith
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 49 Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
| | - Todd Smith
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 50 Digital World Biology , Seattle, Washington
| | - Michael Snyder
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 13 Department of Genetics, Stanford University , Stanford, California
- 51 Stanford Center for Genomics and Personalized Medicine, Stanford University , Stanford, California
| | - Srikanth Rapole
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 52 Proteomics Laboratory, National Centre for Cell Science, University of Pune , Pune, India
| | - Sanjeeva Srivastava
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 53 Proteomics Laboratory, Indian Institute of Technology Bombay , Mumbai, India
| | - Larissa Stanberry
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 Predictive Analytics , Seattle Children's, Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Elizabeth Stewart
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Stefano Toppo
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 54 Department of Molecular Medicine, University of Padova , Padova, Italy
| | - Peter Uetz
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 55 Center for the Study of Biological Complexity (CSBC), Virginia Commonwealth University , Richmond, Virginia
| | - Kenneth Verheggen
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Department of Medical Protein Research, Vlaams Instituut voor Biotechnologie , Ghent, Belgium
- 7 Department of Biochemistry, Ghent University, Ghent , Belgium
| | - Brynn H Voy
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 56 Department of Animal Science, University of Tennessee Institute of Agriculture , Knoxville, Tennessee
| | - Louise Warnich
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 57 Department of Genetics, Faculty of AgriSciences, University of Stellenbosch , Stellenbosch, South Africa
| | - Steven W Wilhelm
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 58 Department of Microbiology, University of Tennessee-Knoxville , Knoxville, Tennessee
| | - Gregory Yandl
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 3 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
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Nacak M, Isir AB, Balci SO, Pehlivan S, Benlier N, Aynacioglu S. Analysis of Dopamine D2 Receptor (DRD2) Gene Polymorphisms in Cannabinoid Addicts*. J Forensic Sci 2012; 57:1621-4. [DOI: 10.1111/j.1556-4029.2012.02169.x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Ozkur M, Erbagci I, Gungor K, Nacak M, Aynacioglu S, Bekir NA. Angiotensin-Converting Enzyme Insertion-Deletion Polymorphism in Primary Open-Angle Glaucoma. Ophthalmologica 2004; 218:415-8. [PMID: 15564761 DOI: 10.1159/000080946] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2003] [Accepted: 01/22/2004] [Indexed: 11/19/2022]
Abstract
PURPOSE To investigate the hypothesis that primary open-angle glaucoma (POAG) is associated with a common insertion-deletion (I/D) polymorphism in the angiotensin-converting enzyme (ACE) gene. METHODS ACE I/D polymorphism was investigated in a control group of healthy subjects (n = 101) and in a group of patients diagnosed with POAG (n = 104). Polymerase chain reaction detection of I/D polymorphism was used to determine the presence of the two ACE alleles in the groups. RESULTS Neither the I/D genotype distributions nor the allele frequencies differed significantly between POAG and control subjects (DD genotype 34.6 vs. 39.6%; ID genotype 53.9 vs. 40.6%; II genotype 11.5 vs. 19.8%, p = 0.1; D allele 61.5 vs. 60%; I allele 38.5 vs. 40%, p = 0.8). CONCLUSION We could not identify a possible association of the I/D polymorphism in the ACE gene with POAG, however further studies with larger patient numbers in different populations are required to clarify the role of ACE gene in susceptibility to POAG.
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Affiliation(s)
- Mehtap Ozkur
- Department of Pharmacology, Faculty of Medicine, University of Gaziantep, Gaziantep, Turkey.
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Herken H, Erdal ME, Erdal N, Aynacioglu S. T102C polymorphisms at the 5-HT2A receptor gene in Turkish schizophrenia patients: a possible association with prognosis. Neuropsychobiology 2003; 47:27-30. [PMID: 12606842 DOI: 10.1159/000068872] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND Serotonergic system abnormalities have been implicated in the pathogenesis of schizophrenia. The 5-HT2A receptor gene polymorphism has long been implicated to play a role in the pathogenesis of schizophrenia. AIM In this study, we assessed the relationship of schizophrenia and its subgroups with 5-HT2A receptor gene polymorphism, and attempted to evaluate a possible correlation between the severity and prognosis of the illness and 5-HT2A receptor gene polymorphism. METHOD Our study comprised 141 unrelated subjects who strictly met DSM-IV criteria for schizophrenia, and 79 healthy unrelated controls, all of Turkish origin. A clinical evaluation of all patients was accomplished applying the Brief Psychiatric Rating Scale (BPRS) test. The analysis of 5-HT2A receptor gene polymorphism was performed using the polymerase chain reaction technique. RESULTS Regarding 5-HT2A receptor gene polymorphisms, no statistically significant difference was found between schizophrenic patients and control subjects (p > 0.05). There was no significant difference between the average of BPRS points of the patients and 5-HT2A receptor gene polymorphisms (p > 0.05). Although there was no correlation between the duration of illness and polymorphism (p > 0.05), the frequency of hospitalization was found to be higher in the patients with T/C and T/T genotypes compared with the patients with C/C genotype (p < 0.05). CONCLUSION Our findings indicate that the T102C polymorphisms of the 5-HT2A receptor gene does not play a substantial role in schizophrenia nor help evaluate susceptibility to schizophrenia. Since the 5-HT2A receptor gene polymorphism is associated with the frequency of hospitalization of the patients, it may be an indicator of prognosis in schizophrenia or help differentiate the patients who are somewhat refractory to antipsychotic treatment.
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Affiliation(s)
- Hasan Herken
- Department of Psychiatry, Medical Faculty of Gaziantep University, Kolejtepe Hastanesi, 27200, TR-Gaziantep, Turkey.
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Araz M, Okan V, Celen Z, Aynacioglu S. Angiotensin converting enzyme gene polymorphism and glomerular filtration rate changes in type 2 diabetic patients. Int J Clin Pract 2002; 56:416-8. [PMID: 12166538] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/26/2023] Open
Abstract
It has been suggested that an insertion/deletion (I/D) polymorphism in intron 16 of the angiotensin-converting enzyme (ACE) gene may be associated with diabetic nephropathy The aim of this study was to investigate whether an association exists between ACE I/D polymorphism and glomerular filtration rate (GFR) in type 2 diabetes mellitus. A total of 128 type 2 diabetic patients were included in the study with the following ACE genotype distribution: DD 40, ID 58,11 30. I/D polymorphism was determined by polymerase chain reaction (PCR). Mean GFR was not statistically different according to ACE genotype (DD: 89.9 +/- 28.1 ml/min, ID: 99.5 +/- 25.1 ml/min, II: 96.6 +/- 19.6 ml/min). There was no significant difference in genotype distribution in normo-, micro- and macroalbuminuric patients (DD:ID:II [%], normo- 35:46:19, micro-28:55:17, macro- 31:55:14). ACE I/D polymorphism does not seem to be associated with GFR in type 2 diabetic patients.
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Affiliation(s)
- M Araz
- Department of Internal Medicine, Medical Faculty, Gaziantep University, Turkey
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Araz M, Aynacioglu S, Aktaran S, Alasehirli B, Okan V. Association between polymorphism of the angiotensin I converting enzyme gene and hypertension in Turkish type II diabetic patients. Acta Medica (Hradec Kralove) 2001; 44:29-32. [PMID: 11367888] [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: 04/16/2023]
Abstract
It has been suggested that an insertion/deletion (I/D) polymorphism in intron 16 of the angiotensin converting enzyme (ACE) gene may be associated with essential hypertension. The aim of this study was to examine the association between ACE I/D polymorphism with blood pressure level and hypertension status in Turkish type 2' diabetic subjects. Hundred and seven hypertensive (78 female, 29 male) and 132 normotensive type 2 diabetic subjects (73 female, 59 male) and 138 sex and age matched control subjects (87 female, 51 male) without diabetes and hypertension were included into the study. The I/D polymorphism was determined by polymerase chain reaction (PCR). There were no statistically difference in genotypic and allelic frequencies of the ACE I/D polymorphism between the hypertensive and normotensive diabetic patients and control subjects. Also no significant differences was detected in systolic and diastolic blood pressure among three different genotypes. ACE I/D polymorphism does not seem to play an important role in the development of hypertension in Turkish type 2 diabetic subjects, but prospective studies may show an association between ACE gene polymorphism and the development of hypertension in diabetic subjects.
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Affiliation(s)
- M Araz
- Department of Internal Medicine, Gaziantep University, Medical Faculty, Gaziantep, Turkey.
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Uzbay IT, Usanmaz SE, Tapanyigit EE, Aynacioglu S, Akarsu ES. Dopaminergic and serotonergic alterations in the rat brain during ethanol withdrawal: association with behavioral signs. Drug Alcohol Depend 1998; 53:39-47. [PMID: 10933339 DOI: 10.1016/s0376-8716(98)00102-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Changes in dopaminergic and serotonergic levels and metabolites in cerebral cortex, corpus striatum and hippocampus were investigated during the first 6-h of withdrawal in ethanol-dependent Wistar rats. Ethanol was given by a liquid diet for 21 days. The concentration of ethanol was 7.2% (v/v) for the last 15 days of the exposure. After 2, 4 and 6 h of ethanol withdrawal, and after audiogenic stimulus (100 dB for 60 s) at 6 h of ethanol withdrawal, various brain regions were assayed for levels of dopamine (DA), DOPAC, HVA, serotonin (5-HT) and 5-HIAA. Behavioral signs of ethanol withdrawal and blood ethanol levels were also evaluated in other parallel groups of ethanol-dependent rats. Significant decreases in 5-HT levels and significant increases in HVA levels in striatum were found during the first 6 h of ethanol withdrawal and after the audiogenic seizures. In hippocampus, 5-HIAA levels were significantly reduced after 2 h of ethanol withdrawal and after the audiogenic seizures. 5-HIAA levels significantly increased after 2 h of ethanol withdrawal in cerebral cortex. Significant increases in both DA and 5-HT levels were also found in cerebral cortex after the audiogenic seizures. The results suggest that the levels of DA, 5-HT and their metabolites are altered by ethanol withdrawal. Furthermore, this may suggest that DA and 5-HT may be involved in the first 6 h of ethanol withdrawal syndrome in rats.
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Affiliation(s)
- I T Uzbay
- Department of Pharmacology, Faculty of Medicine, Gülhane Military Medical Academy, Ankara, Turkey.
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