Efficient Methods for Selecting siRNA Sequences by Using the Average Silencing Probability and a Hidden Markov Model
Toyo University, 1-1-1 Izumino Itakura-machi, Ora-gun Gunma, 374-0193, Japan
- *Corresponding Author:
- Shigeru Takasaki
Toyo University, 1-1-1 Izumino Itakura-machi
Ora-gun Gunma 374-0193 Japan
E-mail: [email protected]
Received Date: December 27, 2013; Accepted Date: January 12, 2013; Published Date: January 14, 2014
Citation: Takasaki S (2014) Efficient Methods for Selecting siRNA Sequences by Using the Average Silencing Probability and a Hidden Markov Model. J Comput Sci Syst Biol 7:045-053. doi: 10.4172/jcsb.1000137
Copyright: © 2014 Takasaki S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Short interfering RNA (siRNA) has been widely used for studying gene functions in mammalian cells but varies markedly in its 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 in mammalian genes. This paper first clarifies problems of the recently reported siRNA design guidelines and then proposes a new method for selecting effective siRNA sequences from many possible candidates by using the average silencing probability on the basis of large number of known effective siRNAs. It is different from the previous score-based siRNA design techniques and can predict the probability that a candidate siRNA sequence will be effective. The results of evaluating it by applying it to recently reported effective and ineffective siRNA sequences for various genes indicate that it would be useful for many other genes. The evaluation results indicate that the proposed method would be useful for many other genes. It should therefore be useful for selecting siRNA sequences effective for mammalian genes. The paper also describes another method using a Hidden Markov Model (HMM) to select the optimal functional siRNAs.