ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy. Issue 9 (1st September 2017)

• Record Type: Journal Article
• Authors: Monga, Isha; Qureshi, Abid; Thakur, Nishant; Gupta, Amit Kumar; Kumar, Manoj
• Is Part Of: G3. Volume 7:Issue 9(2017)
• Page Start: 2931
• Page End: 2943
• Publication Date: 2017-09-01
• Subjects: allele-specific siRNA -- ASPsiDb -- ASPsiPred -- genetic disease database -- prediction algorithm; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.117.044024
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://crdd.osdd.net/servers/aspsirna/ ) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar . ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Eff mut ) and wild-type allele (Eff wild ) with one mismatch by ASPsiPred SVM and ASPsiPred matrix, respectively. In ASPsiPred SVM, 922 unique ASP-siRNAs with experimentally validated quantitative Eff mut were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson's correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Eff mut against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPred matrix was constructed from rule-based studies describing the effect of single siRNA:mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAi-based therapeutics for human genetic diseases.