Dataset Information

In silico prediction of cellular gene targets of herpesvirus encoded microRNAs.

ABSTRACT: Herpesviruses have evolved to encode multiple microRNAs [viral miRNAs (v-miRs)], a unique feature of this family of double stranded DNA (dsDNA) viruses. However, functional role of these v-miRs in host-pathogen interaction remains poorly studied. In this data, we examined the impact of oral disease associated v-miRs viz., miR-H1 [encoded by herpes simplex virus 1 (HSV1)] and miR-K12-3 [encoded by Kaposi sarcoma-associated herpesvirus (KSHV)] by identifying putative targets of viral miRNAs. We used our published microarray data (GSE107005) to identify the transcripts downregulated by the v-miRs. The 3' untranslated region (UTR) of these genes were extracted using BioMart tool on Ensembl and subjected to RNA:RNA interaction employing RNA Hybrid. We obtained hundreds of potential and novel miR-H1 and miR-K12-3 binding sites on the 3'UTR of the genes downregulated by these v-miRs. The information can provide likely regulatory mechanisms of the candidate v-miRs through which they can exert biological impact during herpesvirus infection and pathogenesis.

SUBMITTER: Naqvi AR

PROVIDER: S-EPMC5993014 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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<i>In silico</i> prediction of cellular gene targets of herpesvirus encoded microRNAs.

Naqvi Afsar R AR Seal Alexandra A Shango Jennifer J Shukla Deepak D Nares Salvador S

Data in brief 20180519

Herpesviruses have evolved to encode multiple microRNAs [viral miRNAs (v-miRs)], a unique feature of this family of double stranded DNA (dsDNA) viruses. However, functional role of these v-miRs in host-pathogen interaction remains poorly studied. In this data, we examined the impact of oral disease associated v-miRs <i>viz</i>., miR-H1 [encoded by herpes simplex virus 1 (HSV1)] and miR-K12-3 [encoded by Kaposi sarcoma-associated herpesvirus (KSHV)] by identifying putative targets of viral miRNAs ...[more]

PMID: 29892642

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Project description:BackgroundTarget identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market.MethodsTo test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets.ResultsWe observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature.ConclusionsOur in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

Dataset Information

In silico prediction of cellular gene targets of herpesvirus encoded microRNAs.

Publications

<i>In silico</i> prediction of cellular gene targets of herpesvirus encoded microRNAs.

Similar Datasets

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