Methods for selecting effective siRNA sequences by using statistical and clustering techniques

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 487; p. 1
• Main Author: Takasaki, Shigeru
• Format: Journal Article
• Language: English
• Published: United States 2009
• Subjects: Animals; Cluster Analysis; Computational Biology - methods; Gene Silencing; Humans; RNA, Small Interfering - genetics; RNA, Small Interfering - isolation & purification
• DOI: 10.1007/978-1-60327-547-7_1
• ISSN: 1064-3745

Short interfering RNAs (siRNAs) have been widely used for studying gene functions in mammalian cells but vary markedly in their gene-silencing efficacy. Although many design rules/guidelines for effective siRNAs based on various criteria have been reported recently, there are only a few consistencies among them. This makes it difficult to select effective siRNA sequences targeting mammalian genes. This chapter first reviews the reported siRNA design guidelines and clarifies the problems concerning the current guidelines. It then describes the recently reported new scoring methods for selecting effective siRNA sequences by using statistics and clustering techniques such as the self-organizing map (SOM) and the radial basis function (RBF) network. In the proposed three methods, individual scores are defined as a gene degradation measure based on position-specific statistical significances. The effectiveness of the methods was confirmed by evaluating effective and ineffective siRNAs for recently reported genes and comparison with other reported scoring methods. The sizes (values) of these scores are closely correlated with the degree of gene degradation, and the scores can easily be used for selecting high-potential siRNA candidates. The evaluation results indicate that the proposed new methods are useful for selecting siRNA sequences targeting mammalian mRNA sequences.