Statistical methods for analysis of high-throughput RNA interference screens

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule s...

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Veröffentlicht in:	Nature methods Jg. 6; H. 8; S. 569 - 575
Hauptverfasser:	Birmingham, Amanda, Selfors, Laura M, Forster, Thorsten, Wrobel, David, Kennedy, Caleb J, Shanks, Emma, Santoyo-Lopez, Javier, Dunican, Dara J, Long, Aideen, Kelleher, Dermot, Smith, Queta, Beijersbergen, Roderick L, Ghazal, Peter, Shamu, Caroline E
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.08.2009 Nature Publishing Group
Schlagworte:	Animals Bioinformatics Biological Microscopy Biological Techniques Biomedical and Life Sciences Biomedical Engineering/Biotechnology Computer Simulation Genetic research Genetic screening Genetics Life Sciences Methods Models, Statistical Molecular biology Proteomics Research Design - statistics & numerical data review-article Ribonucleic acid RNA RNA Interference RNA, Small Interfering - chemistry RNA, Small Interfering - genetics Small Molecule Libraries Statistical methods United States
ISSN:	1548-7091, 1548-7105, 1548-7105
Online-Zugang:	Volltext
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Abstract	RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.
AbstractList	RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow. RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow. [PUBLICATION ABSTRACT] RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.
Audience	Academic
Author	Dunican, Dara J Birmingham, Amanda Santoyo-Lopez, Javier Ghazal, Peter Wrobel, David Selfors, Laura M Shamu, Caroline E Forster, Thorsten Long, Aideen Smith, Queta Shanks, Emma Kennedy, Caleb J Beijersbergen, Roderick L Kelleher, Dermot
AuthorAffiliation	1 The RNAi Global Initiative, Lafayette, CO, USA 7 Institute of Molecular Medicine, Trinity Centre for Health Sciences, Trinity College, Dublin, Ireland 5 Department of Systems Biology, Harvard Medical School, Boston, MA, USA 8 Division of Molecular Carcinogenesis and NKI Robotics and Screening Center, The Netherlands Cancer Institute, Amsterdam, The Netherlands 2 Thermo Fisher Scientific, Lafayette, CO, USA 4 Division of Pathway Medicine and Centre for Systems Biology, The University of Edinburgh, Edinburgh, UK 3 ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, USA 6 Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee, Scotland
AuthorAffiliation_xml	– name: 3 ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, USA – name: 6 Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee, Scotland – name: 8 Division of Molecular Carcinogenesis and NKI Robotics and Screening Center, The Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 5 Department of Systems Biology, Harvard Medical School, Boston, MA, USA – name: 1 The RNAi Global Initiative, Lafayette, CO, USA – name: 7 Institute of Molecular Medicine, Trinity Centre for Health Sciences, Trinity College, Dublin, Ireland – name: 2 Thermo Fisher Scientific, Lafayette, CO, USA – name: 4 Division of Pathway Medicine and Centre for Systems Biology, The University of Edinburgh, Edinburgh, UK
Author_xml	– sequence: 1 givenname: Amanda surname: Birmingham fullname: Birmingham, Amanda email: amanda.birmingham@thermofisher.com organization: The RNAi Global Initiative, Thermo Fisher Scientific – sequence: 2 givenname: Laura M surname: Selfors fullname: Selfors, Laura M organization: The RNAi Global Initiative, ICCB-Longwood Screening Facility, Harvard Medical School – sequence: 3 givenname: Thorsten surname: Forster fullname: Forster, Thorsten organization: The RNAi Global Initiative, Division of Pathway Medicine and Centre for Systems Biology, The University of Edinburgh – sequence: 4 givenname: David surname: Wrobel fullname: Wrobel, David organization: The RNAi Global Initiative, ICCB-Longwood Screening Facility, Harvard Medical School – sequence: 5 givenname: Caleb J surname: Kennedy fullname: Kennedy, Caleb J organization: The RNAi Global Initiative, Department of Systems Biology, Harvard Medical School – sequence: 6 givenname: Emma surname: Shanks fullname: Shanks, Emma organization: The RNAi Global Initiative, Division of Biological Chemistry and Drug Discovery, Drug Discovery Unit, College of Life Sciences, University of Dundee – sequence: 7 givenname: Javier surname: Santoyo-Lopez fullname: Santoyo-Lopez, Javier organization: The RNAi Global Initiative, Division of Pathway Medicine and Centre for Systems Biology, The University of Edinburgh – sequence: 8 givenname: Dara J surname: Dunican fullname: Dunican, Dara J organization: The RNAi Global Initiative, Institute of Molecular Medicine, Trinity Centre for Health Sciences, Trinity College – sequence: 9 givenname: Aideen surname: Long fullname: Long, Aideen organization: The RNAi Global Initiative, Institute of Molecular Medicine, Trinity Centre for Health Sciences, Trinity College – sequence: 10 givenname: Dermot surname: Kelleher fullname: Kelleher, Dermot organization: The RNAi Global Initiative, Institute of Molecular Medicine, Trinity Centre for Health Sciences, Trinity College – sequence: 11 givenname: Queta surname: Smith fullname: Smith, Queta organization: The RNAi Global Initiative, Thermo Fisher Scientific – sequence: 12 givenname: Roderick L surname: Beijersbergen fullname: Beijersbergen, Roderick L organization: The RNAi Global Initiative, Division of Molecular Carcinogenesis and National Cancer Institute Robotics and Screening Center, The Netherlands Cancer Institute – sequence: 13 givenname: Peter surname: Ghazal fullname: Ghazal, Peter organization: The RNAi Global Initiative, Division of Pathway Medicine and Centre for Systems Biology, The University of Edinburgh – sequence: 14 givenname: Caroline E surname: Shamu fullname: Shamu, Caroline E organization: The RNAi Global Initiative, ICCB-Longwood Screening Facility, Harvard Medical School, Department of Systems Biology, Harvard Medical School
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/19644458$$D View this record in MEDLINE/PubMed
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Snippet	RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi... RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery and, in both of these areas, large-scale high-throughput RNAi...
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SubjectTerms	Animals Bioinformatics Biological Microscopy Biological Techniques Biomedical and Life Sciences Biomedical Engineering/Biotechnology Computer Simulation Genetic research Genetic screening Genetics Life Sciences Methods Models, Statistical Molecular biology Proteomics Research Design - statistics & numerical data review-article Ribonucleic acid RNA RNA Interference RNA, Small Interfering - chemistry RNA, Small Interfering - genetics Small Molecule Libraries Statistical methods
Title	Statistical methods for analysis of high-throughput RNA interference screens
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