Literature DB >> 17716236

Increasing the robustness and validity of RNAi screens.

Steven A Haney1.   

Abstract

RNAi screening in mammalian cells has become a valuable method to identify and describe genetic relationships in both basic biology and disease mechanisms. Multiple efforts are underway to standardize how RNAi screening data are reported, including establishing experimental criteria for defining a validated hit from a screen, and the extent to which the primary screening data themselves are reported. These discussions have identified several key areas that require consistency, or at least understanding, before RNAi screening data can be used generally. Successfully addressing these targeted areas would broaden the use of RNAi screening data beyond advancing one or a few hits into validation experiments, to enable verification of primary screening data, and to facilitate comparisons between sample groups based on screening profiles. Areas for improving RNAi screening include general guidelines for validating hits from screens, the creation of standardized reporting structures for RNAi screening data, such as Minimum Information About an RNAi Experiment (MIARE), statistical methods for analyzing screening data that explicitly account for differences between screening RNAi reagents versus small molecules, and technical improvements to RNAi screening that improve the analysis of gene knockdowns, including multiparametric approaches, such as high-content screening. This review will discuss how these approaches can improve RNAi screening data at the community level and for an individual researcher trying to manage an RNAi screen.

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Mesh:

Year:  2007        PMID: 17716236     DOI: 10.2217/14622416.8.8.1037

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  9 in total

1.  Bottlenecks caused by software gaps in miRNA and RNAi research.

Authors:  Sean Ekins; Ron Shigeta; Barry A Bunin
Journal:  Pharm Res       Date:  2012-02-24       Impact factor: 4.200

Review 2.  Vigilance and validation: Keys to success in RNAi screening.

Authors:  Frederic D Sigoillot; Randall W King
Journal:  ACS Chem Biol       Date:  2010-12-28       Impact factor: 5.100

Review 3.  Networks and pathways in pigmentation, health, and disease.

Authors:  Laura L Baxter; Stacie K Loftus; William J Pavan
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2009 Nov-Dec

4.  Identification of Host Kinase Genes Required for Influenza Virus Replication and the Regulatory Role of MicroRNAs.

Authors:  Abhijeet Bakre; Lauren E Andersen; Victoria Meliopoulos; Keegan Coleman; Xiuzhen Yan; Paula Brooks; Jackelyn Crabtree; S Mark Tompkins; Ralph A Tripp
Journal:  PLoS One       Date:  2013-06-21       Impact factor: 3.240

5.  Host- and strain-specific regulation of influenza virus polymerase activity by interacting cellular proteins.

Authors:  Eric Bortz; Liset Westera; Jad Maamary; John Steel; Randy A Albrecht; Balaji Manicassamy; Geoffrey Chase; Luis Martínez-Sobrido; Martin Schwemmle; Adolfo García-Sastre
Journal:  mBio       Date:  2011-08-16       Impact factor: 7.867

6.  Single cell cytometry of protein function in RNAi treated cells and in native populations.

Authors:  Peter LaPan; Jing Zhang; Jing Pan; Andrew Hill; Steven A Haney
Journal:  BMC Cell Biol       Date:  2008-08-01       Impact factor: 4.241

7.  Knowledge based identification of essential signaling from genome-scale siRNA experiments.

Authors:  Armand Bankhead; Iliana Sach; Chester Ni; Nolwenn LeMeur; Mark Kruger; Marc Ferrer; Robert Gentleman; Carol Rohl
Journal:  BMC Syst Biol       Date:  2009-08-05

Review 8.  Host gene targets for novel influenza therapies elucidated by high-throughput RNA interference screens.

Authors:  Victoria A Meliopoulos; Lauren E Andersen; Katherine F Birrer; Kaylene J Simpson; John W Lowenthal; Andrew G D Bean; John Stambas; Cameron R Stewart; S Mark Tompkins; Victor W van Beusechem; Iain Fraser; Musa Mhlanga; Samantha Barichievy; Queta Smith; Devin Leake; Jon Karpilow; Amy Buck; Ghil Jona; Ralph A Tripp
Journal:  FASEB J       Date:  2012-01-12       Impact factor: 5.191

9.  Hit selection with false discovery rate control in genome-scale RNAi screens.

Authors:  Xiaohua Douglas Zhang; Pei Fen Kuan; Marc Ferrer; Xiaohua Shu; Yingxue C Liu; Adam T Gates; Priya Kunapuli; Erica M Stec; Min Xu; Shane D Marine; Daniel J Holder; Berta Strulovici; Joseph F Heyse; Amy S Espeseth
Journal:  Nucleic Acids Res       Date:  2008-07-15       Impact factor: 16.971

  9 in total

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