Turning quantity into quality: novel quality assurance strategies for data produced by high-throughput genomics technologies |
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| Institution: | 1. Department of Human Genetics, Emory University, Atlanta, GA, USA;;2. EGL Genetics, Tucker, GA, USA;;3. Department of Pathology, Harvard Medical School/Massachusetts General Hospital, Boston, MA, USA;;4. Veritas Genetics, Danvers, MA, USA;;5. School of Medicine, University of California, San Francisco, CA, USA;;6. Division of Clinical Genetics, Children’s Mercy Hospital, Kansas City, MO, USA;;7. Department of Pediatrics, University of Missouri–Kansas City School of Medicine, Kansas City, MO, USA;;8. Quest Diagnostics Nichols Institute, San Juan Capistrano, CA, USA;;9. Division of Genomic Diagnostics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA;;10. Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA;;11. Department of Pathology, University of Utah, Salt Lake City, UT, USA;;12. ARUP Laboratories, University of Utah, Salt Lake City, UT, USA.;1. Department of Pathology, Duke University, Durham, NC, USA;2. Department of Human Genetics, Emory University, Atlanta, GA, USA;3. HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA;4. Division of Genetics and Genomics, Department of Pediatrics, University of California, Irvine, CA, USA;5. Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA;6. Department of Human Genetics, University of Chicago, Chicago, IL, USA;7. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA;8. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA;9. Broad Institute of MIT and Harvard, Cambridge, MA, USA;10. Department of Pediatrics, Duke University, Durham, NC, USA.;11. Division of Pathology & Laboratory Medicine, Children’s National Health System, Washington, DC, USA;12. Departments of Pathology and Pediatrics, George Washington University, Washington, DC, USA.;13. American College of Medical Genetics and Genomics, Bethesda, MD, USA.;1. Institute for Information Law (IViR), University of Amsterdam, Amsterdam, the Netherlands;2. Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, the Netherlands;1. Chemistry and Physics Department, University of Almeria, Agrifood Campus of International Excellence (ceiA3), 04120 Almería, Spain;2. Department of Zoology, University of Córdoba, Campus of Rabanales, 14071, Córdoba, Spain |
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| Abstract: | The pharmaceutical industry is facing the challenge of managing the exponential increase in volume, diversity and complexity of data generated by high-throughput technologies such as genome sequencing, gene-expression profiling, protein-expression profiling, metabolic profiling and high-throughput screening. These novel ‘genomics’ technologies are expected to reshape the approach of life science companies to research. Unfortunately, in many cases genomics technologies have been used uncritically, and some preliminary results have been disappointing. The lack of standardized data validation and quality assurance processes is recognized as one of the major hurdles for successfully implementing genomics technologies. This is particularly important for industrialized drug discovery processes, because more and more key conclusions and far-reaching decisions in the pharmaceutical industry are based on data that is generated automatically. Therefore, automated, specialized quality-control systems that can spot erroneous data that might obscure important biological effects are needed urgently. In this article, special emphasis is placed on DNA microarray technologies, a key genomics technology that suffers from severe problems with data quality. A generic, automatable data-quality-assurance workflow is discussed that will ultimately improve the quality of the drug candidates and, at the same time, reduce overall drug-development costs. |
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