Literature DB >> 17438372

High-throughput screening by RNA interference: control of two distinct types of variance.

David J Stone1, Shane Marine, John Majercak, William J Ray, Amy Espeseth, Adam Simon, Marc Ferrer.   

Abstract

The availability of genome-wide RNAi libraries has enabled researchers to rapidly assess the functions of thousands of genes; however the fact that these screens are run in living biological systems add complications above and beyond that normally seen in high-throughput screening (HTS). Specifically, error due to variance in both measurement and biology are large in such screens, leading to the conclusion that the majority of "hits" are expected to be false positives. Here, we outline basic guidelines for screen development that will help the researcher to control these forms of variance. By running a large number of positive and negative control genes, error of measurement can be accurately estimated and false negatives reduced. Likewise, by using a complex readout for the screen, which is not easily mimicked by other biological pathways and phenomena, false positives, can be minimized. By controlling variance in these ways, the researcher can maximize the utility of genome-wide RNAi screening.

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Year:  2007        PMID: 17438372     DOI: 10.4161/cc.6.8.4184

Source DB:  PubMed          Journal:  Cell Cycle        ISSN: 1551-4005            Impact factor:   4.534


  9 in total

Review 1.  Statistical methods for analysis of high-throughput RNA interference screens.

Authors:  Amanda Birmingham; Laura M Selfors; Thorsten Forster; David Wrobel; Caleb J Kennedy; Emma Shanks; Javier Santoyo-Lopez; Dara J Dunican; Aideen Long; Dermot Kelleher; Queta Smith; Roderick L Beijersbergen; Peter Ghazal; Caroline E Shamu
Journal:  Nat Methods       Date:  2009-08       Impact factor: 28.547

Review 2.  Insights to transcriptional networks by using high throughput RNAi strategies.

Authors:  Jaakko Mattila; Oscar Puig
Journal:  Brief Funct Genomics       Date:  2009-12-01       Impact factor: 4.241

Review 3.  High-throughput screening for genes that prevent excess DNA replication in human cells and for molecules that inhibit them.

Authors:  Chrissie Y Lee; Ronald L Johnson; Jennifer Wichterman-Kouznetsova; Rajarshi Guha; Marc Ferrer; Pinar Tuzmen; Scott E Martin; Wenge Zhu; Melvin L DePamphilis
Journal:  Methods       Date:  2012-04-05       Impact factor: 3.608

4.  Optimization and application of median filter corrections to relieve diverse spatial patterns in microtiter plate data.

Authors:  Paul J Bushway; Behrad Azimi; Susanne Heynen-Genel
Journal:  J Biomol Screen       Date:  2011-09-06

5.  RNAi screen for rapid therapeutic target identification in leukemia patients.

Authors:  Jeffrey W Tyner; Michael W Deininger; Marc M Loriaux; Bill H Chang; Jason R Gotlib; Stephanie G Willis; Heidi Erickson; Tibor Kovacsovics; Thomas O'Hare; Michael C Heinrich; Brian J Druker
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-11       Impact factor: 11.205

Review 6.  Genomic screening with RNAi: results and challenges.

Authors:  Stephanie Mohr; Chris Bakal; Norbert Perrimon
Journal:  Annu Rev Biochem       Date:  2010       Impact factor: 23.643

7.  The catalytically inactive tyrosine phosphatase HD-PTP/PTPN23 is a novel regulator of SMN complex localization.

Authors:  Alma Husedzinovic; Beate Neumann; Jürgen Reymann; Stefanie Draeger-Meurer; Ashwin Chari; Holger Erfle; Utz Fischer; Oliver J Gruss
Journal:  Mol Biol Cell       Date:  2014-11-12       Impact factor: 4.138

8.  Sources of Error in Mammalian Genetic Screens.

Authors:  Laura Magill Sack; Teresa Davoli; Qikai Xu; Mamie Z Li; Stephen J Elledge
Journal:  G3 (Bethesda)       Date:  2016-09-08       Impact factor: 3.154

9.  Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation.

Authors:  Simon Dirmeier; Christopher Dächert; Martijn van Hemert; Ali Tas; Natacha S Ogando; Frank van Kuppeveld; Ralf Bartenschlager; Lars Kaderali; Marco Binder; Niko Beerenwinkel
Journal:  PLoS Comput Biol       Date:  2020-02-10       Impact factor: 4.475

  9 in total

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