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Abstract
Investigations have revealed that silencing unwanted transcripts or off-targeting can induce false positive phenotype during RNA interference (RNAi)-based gene function study. But still the standard computational approaches towards small interfering RNA (siRNA) off-target minimization fall short in terms of addressing this false positive phenotype issue. Some of these off-targets may interfere with the biochemical pathway being investigated. It may also inadvertently target cell’s metabolic pathways with unquantifiable consequences on the processes of user’s interest. Here, we report the development of a siRNA selection tool that, for the first time, implements a functional off-target filtering that aims to minimize false positive phenotypes arising from inadvertent targets that are functionally similar or related to the direct target gene, along with a multi-parametric classifier (support vector machine) for optimized selection of potent siRNAs. The functional off-target filtering minimizes the number of off-target genes which are functionally related to the direct target gene, i.e. involved in a common biological process and may have similar phenotype. A text-mining algorithm is used to find related biological processes associated with the direct target and each off-target transcript by comparison of the biological processes associated with these genes. It also gives the user a choice to select one or more off-targets that may be potentially more harmful, from a predicted off-target gene list to be filtered out. Testing with huge set of biologically validated siRNAs from three different sources showed consistent good performance of our tool in terms of effective siRNA selection. It outperformed four potent siRNA selection algorithms of present day in terms of specificity in the selection of highly efficient siRNAs when compared on a common test set. A genome wide testing with potent siRNAs used in high-content screening confirmed validation of 2767 designed siRNAs in terms of phenotypic output. This tool presently supports siRNA designs for human genes and is freely available at http://gyanxet-beta.com.
Acknowledgements
We hereby acknowledge Department of Science and Technology, Govt. of India for funding this work. We express our gratitude to Prof. Urlike Kutay from Department of Biochemistry, ETH Zurich for providing siRNA screening data from Biogenesis project. We also thank Sanga Mitra and Smarajit Das from Indian Association for the Cultivation of Science for their helpful suggestions about this paper.
