Shibin Qiu and Terran Lane
Department of Computer Science, University of New Mexico, Albuquerque, New Mexico
In recent years, RNA interference (RNAi) has surged into the spotlight of pharmacy, genomics, and system biology. Dozens of RNAi-based drugs are entering FDA trials, and as of this writing, at least three companies are based exclusively on RNAi technology. The massive excitement surrounding RNAi stems from its vast potential for therapeutic and genomic applications. Currently, promising RNAi-based therapies include treatments for HIV, Huntington's disease, macular degeneration, hypercholesterolemia, and even prion diseases such as bovine spongiform encepha-lopathy (BSE; mad cow disease), and potential new therapies are being announced nearly daily.
The fundamental effect of RNAi is posttranscriptional gene silencing via targeted knockdown of mRNA. By careful selection of one or more RNAi initiator molecules, the RNAi cellular machinery can be coerced into identifying mRNA molecules possessing a specific nucleotide sequence and cleaving those transcripts, thus preventing translation into the corresponding protein or other downstream regulatory effects of the gene. Researchers quickly discovered ways to exploit this effect, including knockdown of viral transcripts to treat viral diseases, knockdown of endogenous genes to treat genetic diseases, gene function study using a loss-of-function method, and probing genetic regulatory networks by knocking down key links.
While a number of laboratories and companies are rushing to turn these potentials into practical therapies, a vital step toward realization is to fully characterize the RNAi mechanism and its effects. RNAi-based therapies are threatened by RNAi characteristics, including nonspecificity, off-target effects, poor silencing efficacy,
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synergistic pool effects of siRNA/dsRNA, and uncertainties of RNAi kinetics. These negative effects and risky properties of RNAi must be fully characterized before it can be widely applied, especially to human therapies. Meanwhile, investigations into these issues provide opportunities for researchers in biology, bioinformatics, machine learning, and data mining.
In this chapter we describe the biological mechanisms of RNAi, covering both siRNA and microRNA as interfering initiators, followed by in-depth discussions of a few computational topics, including RNAi specificity, microRNA gene and target prediction, and siRNA silencing efficacy estimation. Finally, we point out some open questions related to RNAi research. At the end of the chapter we provide a glossary of terms and acronyms.
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