Designing of highly effective complementary and mismatch siRNAs for silencing a gene

PLoS One. 2011;6(8):e23443. doi: 10.1371/journal.pone.0023443. Epub 2011 Aug 10.


In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene.Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm ( for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Base Pair Mismatch / genetics*
  • Base Sequence
  • Gene Silencing*
  • Internet
  • Models, Genetic
  • Molecular Sequence Data
  • Nucleotides / genetics
  • Position-Specific Scoring Matrices
  • RNA, Complementary / genetics*
  • RNA, Small Interfering / genetics*
  • Support Vector Machine


  • Nucleotides
  • RNA, Complementary
  • RNA, Small Interfering