Vir-Mir db: prediction of viral microRNA candidate hairpins

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Abstract
MicroRNAs have been found in various organisms and play essential roles in gene expression regulation of many critical cellular processes. Large-scale computational prediction of miRNAs has been conducted for many organisms using known genomic sequences; however, there has been no such effort for the thousands of known viral genomes. Some viruses utilize existing host cellular pathways for their own benefit. Furthermore, viruses are capable of encoding miRNAs and using them to repress host genes. Thus, identifying potential miRNAs in all viral genomes would be valuable to virologists who study virus–host interactions. Based on our previously reported hairpin secondary structure and feature selection filters, we have examined the 2266 available viral genome sequences for putative miRNA hairpins and identified 33 691 hairpin candidates in 1491 genomes. Evaluation of the system performance indicated that our discovery pipeline exhibited 84.4% sensitivity. We established an interface for users to query the predicted viral miRNA hairpins based on taxonomic classification, and a host target gene prediction service based on the RNAhybrid program and the 3′-UTR gene sequences of human, mouse, rat, zebrafish, rice and Arabidopsis. The viral miRNA prediction database (Vir-Mir) can be accessed via http://alk.ibms.sinica.edu.tw .