Chapter 5 miRNAMap – integrated database of microRNA-target interactions
5.4 Improvement
5.6.2 Comparing miRNA target prediction databses to miRTarBase
In order to evaluate the performance of miRNA target prediction tools and our proposed method, we collected 1,524 experimentally validated miRNA-target interactions in human, including 204 non-functional MTIs and 1,320 functional MTIs. The detail list is shown in Appendix I. This data set is based on our manually curated MTIs with strong evidence support in miRTarBase (177). The following formulas of predictive performance are defined:
Precision (Pre) = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 , Sensitivity (Sn) = 𝑇𝑃
𝑇𝑃 + 𝐹𝑁 , Specificity(Sp) = 𝑇𝑁
𝑇𝑁 + 𝐹𝑃 , Accuracy (𝐴𝑐𝑐) = 𝑇𝑃 + 𝑇𝑁
𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁 , Performance (PERF) = 𝑆𝑝 × 𝑆𝑛
In the equation, TN represents true negative, TP true positive, FN false negative and FP false positive. The performance of individual prediction MTI database is displayed in
Table 19. We found that the top three in performance (PERF) are TargetScan
(Conserved), microRNA.org (2010_aug_S_0) and miRTar (overlapping 4 tools) and.Table 19. Predictive performance across miRNA-target interaction databases by using experimentally verified MTIs with strong evidence support in miRTarBase.
Integrated
Databases Dataset Number of
positive data Number of
negative data Precision Sensitivity Specificity Accuracy PERF
TargetScan Conserved 1,189 185 90.8% 57.2% 62.7% 57.9% 0.359
Chapter 6 Conclusion
MicroRNA plays important roles in post-translational gene regulation among various species. Nowadays many miRNA-target interactions are revealed by experimental approach and miRNA target site prediction tools. This thesis spotlights the miRNA-target interactions whether they are validated or predicted. It is composed of miRTarBase, miRTar, miRNAMap and HomoloMTI (Figure 9).
Firstly, we construct a more comprehensive database of miRNA-target interactions, miRTarBase, which were experimentally validated. The biological features of miRNA/target duplex were observed based on largest collection of human miRNA-target interactions currently available. Various web interfaces are designed to facilitate the presentation of miRNA-target interactions. A pipeline combining text-mining and manual review was established to extract MTI information from research articles.
Second of all, miRTar adopts various analyzing scenarios to identify putative miRNA target sites of the gene transcripts and elucidates the biological functions of miRNAs toward their targets in biological pathways. The system has three major features. First, the prediction system considers various analyzing scenarios (1 miRNA:1 gene, 1:N, N:1, N:M, all miRNAs:N genes, and N miRNAs: genes involved in a pathway) to easily identify the regulatory relationships between interesting miRNAs and their targets, in 3’UTR, 5’UTR and coding regions. Second, miRTar can analyze and highlight a group of miRNA-regulated genes that participate in particular KEGG pathways to elucidate the biological roles of miRNAs in biological pathways. Third, miRTar can provide further information for elucidating the miRNA regulation, i.e., miRNA-target interactions, affected by alternative splicing.
Thirdly, miRNAMap integrates miRNA-target interactions databases to elucidate accurate MTIs. We make the focus on the investigation of miRNA-target interaction. To make miRNAMap more comprehensive, we integrate the experimentally verified MTIs, the MTIs predicted by 11 predicted miRNA target databases and more expression
The homologous MTIs profiles could reveal the homologous genes regulated by miRNA with at least one experimental validation in one of the homologous genes and predicted miRNA target interactions. The novel miRNA target interactions could be pioneer studied by HomoloMTI. HomoloMTI aims to provide a comprehensive comparative perspective analysis on the various species of miRNA target interactions to find homologous miRNA target sites.
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