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Gene. 2015 May 15;562(2):152-8. doi: 10.1016/j.gene.2015.02.067. Epub 2015 Feb 25.

Soft computing model for optimized siRNA design by identifying off target possibilities using artificial neural network model.

Author information

1
Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Kerala, India. Electronic address: reena.rajesh@rit.ac.in.
2
Department of Computer Science and Engineering, Rajiv Gandhi Institute of Technology, Kerala, India.
3
Department of Computer Science and Engineering, Cochin University of Science & Technology, Kerala, India.

Abstract

The ability of small interfering RNA (siRNA) to do posttranscriptional gene regulation by knocking down targeted genes is an important research topic in functional genomics, biomedical research and in cancer therapeutics. Many tools had been developed to design exogenous siRNA with high experimental inhibition. Even though considerable amount of work has been done in designing exogenous siRNA, design of effective siRNA sequences is still a challenging work because the target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. In some cases, siRNAs may tolerate mismatches with the target mRNA, but knockdown of genes other than the intended target could make serious consequences. Hence to design siRNAs, two important concepts must be considered: the ability in knocking down target genes and the off target possibility on any nontarget genes. So before doing gene silencing by siRNAs, it is essential to analyze their off target effects in addition to their inhibition efficacy against a particular target. Only a few methods have been developed by considering both efficacy and off target possibility of siRNA against a gene. In this paper we present a new design of neural network model with whole stacking energy (ΔG) that enables to identify the efficacy and off target effect of siRNAs against target genes. The tool lists all siRNAs against a particular target with their inhibition efficacy and number of matches or sequence similarity with other genes in the database. We could achieve an excellent performance of Pearson Correlation Coefficient (R=0. 74) and Area Under Curve (AUC=0.906) when the threshold of whole stacking energy is ≥-34.6 kcal/mol. To the best of the author's knowledge, this is one of the best score while considering the "combined efficacy and off target possibility" of siRNA for silencing a gene. The proposed model shall be useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in the area of bioinformatics. The software is developed as a desktop application and available at http://opsid.in/opsid/.

KEYWORDS:

Area Under Curve; Artificial neural network; Messenger RNA; Off target; Small interfering RNA

PMID:
25725126
DOI:
10.1016/j.gene.2015.02.067
[Indexed for MEDLINE]

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