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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Vydáno v:Nature methods Ročník 6; číslo 8; s. 569 - 575
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Nature Publishing Group US 01.08.2009
Nature Publishing Group
Témata:
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
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Popis
Shrnutí: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.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/nmeth.1351