High-throughput RNA interference (HT-RNAi) is a powerful research tool for parallel, 'genome-wide', targeted knockdown of specific gene products. Such perturbation of gene product expression allows for the systematic query of gene function. The phenotypic results can be monitored by assaying for specific alterations in molecular and cellular endpoints, such as promoter activation, cell proliferation and survival. RNAi profiling may also be coupled with drug screening to identify molecular correlates of drug response. As with other genomic-scale data, methods of data analysis are required to handle the unique aspects of data normalization and statistical processing. In addition, novel techniques or knowledge-mining strategies are required to extract useful biological information from HT-RNAi data. Knowledge-mining strategies involve the novel application of bioinformatic tools and expert curation to provide biological context to genomic-scale data such as that generated from HT-RNAi data. Pathway-based tools, whether text-mining based or manually curated, serve an essential role in knowledge mining. These tools can be applied during all steps of HT-RNAi screen experiments including pre-screen knowledge gathering, assay development and hit confirmation and validation. Most importantly, pathway tools allow the interrogation of HT-RNAi data to identify and prioritize pathway-based biological information as a result of specific loss of gene function.
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http://dx.doi.org/10.1007/978-1-60761-175-2_15 | DOI Listing |
Methods Mol Biol
December 2017
Constellation Pharmaceuticals, Cambridge, MA, USA.
High-throughput RNA interference (HT-RNAi) screening is an effective technology to help identify important genes and pathways involved in a biological process. Analysis of high-throughput RNAi screening data is a critical part of this technology, and many analysis methods have been described. Here, we summarize the workflow and types of analyses commonly used in high-throughput RNAi screening.
View Article and Find Full Text PDFWorld J Virol
May 2013
Sandeep Amberkar, Narsis A Kiani, Lars Kaderali, Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany.
Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread.
View Article and Find Full Text PDFAssay Drug Dev Technol
June 2010
Pharmaceutical Genomics Division, The Translational Genomics Research Institute, Scottsdale, Arizona, USA.
Niemann-Pick disease type C (NPC) is an inherited lipid storage disorder characterized by a defect in intracellular trafficking of exogenous cholesterol and glycosphingolipids. A goal for therapeutic treatment of NPC is to decrease/normalize cholesterol accumulation. We developed a functional genomics-based assay, combining high-throughput RNA interference (HT-RNAi) screening with high-content fluorescence imaging to identify specific genes in NPC cells that will result in more normal cholesterol levels in the diseased cells.
View Article and Find Full Text PDFMethods Mol Biol
October 2009
Pharmaceutical Genomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
High-throughput RNA interference (HT-RNAi) is a powerful research tool for parallel, 'genome-wide', targeted knockdown of specific gene products. Such perturbation of gene product expression allows for the systematic query of gene function. The phenotypic results can be monitored by assaying for specific alterations in molecular and cellular endpoints, such as promoter activation, cell proliferation and survival.
View Article and Find Full Text PDFGenome Res
October 2004
Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 02115, USA.
The recently completed Caenorhabditis elegans genome sequence allows application of high-throughput (HT) approaches for phenotypic analyses using RNA interference (RNAi). As large phenotypic data sets become available, "phenoclustering" strategies can be used to begin understanding the complex molecular networks involved in development and other biological processes. The current HT-RNAi resources represent a great asset for phenotypic profiling but are limited by lack of flexibility.
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