Selecting effective siRNA target sequences by using Bayes' theorem

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 few consistencies among them. This makes it difficult to select effective siRNA sequences in mammalian genes. Another shortcoming of most previously reported methods is that they cannot estimate the probability that a candidate sequence will silence the target gene. The analytical prediction method proposed in the present study uses Bayes' theorem to select effective siRNA target sequences from many possible candidate sequences. It is quite 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. It should therefore be useful for selecting siRNA sequences effective for mammalian genes.