D186 Nucleic Acids Research, 2008, Vol. 36, Database issue Table 2. System performance test and the number of candidate hairpins Known viral miRNAs (species) HCMV KSHV RLCV SV40 EBV Total (Sensitivity) Total number of predicted miRNA candidate hairpins Number of miRNAs 11 13 16 1 23 64 (100%) 2266 virus genomic sequences Number of miRNAs after Srnaloop ﬁlter 11 12 16 1 23 63 (98.4%) 514 874 Number of miRNAs after sequence and structure ﬁlter 8 8 14 1 23 54 (84.4%) 33 691 Number of miRNAs after ORF ﬁlter 6 7 14 0 23 50 (78.1%) 5306 To test the performance of our system, we calculated the number of known viral pre-miRNAs remaining after each ﬁlter process. Originally, there were 64 known pre-miRNAs belonging to ﬁve viruses. The number of hairpins predicted from 2266 virus genomes is also listed. Figure 1. Viral miRNA candidate hairpin query web interface. Users may query the putative miRNAs of a speciﬁc virus by using the hierarchical menu or by using the search function with GenBank identiﬁer, RefSeq accession number or keyword. Discovery pipeline performance To assess the eﬃcacy of our pipeline system, we performed both sensitivity and speciﬁcity tests. Because it is much easier to identify hairpin structures in sequences of known hairpins than from entire genomic sequences, we calcu- lated the number of predicted candidate hairpins after each ﬁlter procedure in our pipeline to determine the recovery rate of reported miRNAs. Originally, the test dataset consisted of 64 known pre-miRNAs belonging to ﬁve viruses from miRBase. As shown in Table 2, 63 known miRNAs remained after the Srnaloop step, 54 after the sequence and structure features step, and 50 after the ORF comparison step of the selection. These results imply that the recovery rate is close to 98% after Srnaloop hairpin prediction, 84% after Srnaloop hairpin prediction and sequence and structure feature ﬁlter and 78% after additional ORF ﬁlter. For the speciﬁcity examination on the pipeline, the negative dataset generation procedure was similar to the one described by Sewer et al. (18). This procedure is based on the fact that the fraction of miRNA-encoding sequences in the genome is very small; therefore, randomly extracted sequences are extremely unlikely to encode miRNAs. We randomly extracted 19 200 (64 300) sequence fragments (90 bp in length) from viral genomic sequences. These 19 200 randomly chosen sequences of 1.8 Mb were applied to our discovery pipeline under the same hairpin identiﬁcation parameters and sequence and structural ﬁlter criteria. As a result, we obtained 533 presumed false positives from three independent experi- ments (175, 176 and 182 predicted candidates, respec- tively), corresponding to an average of 178 false positives. This would give an estimated 1% false positive prediction in a randomly generated 20 000 sequences. With over 40 000 000 nt from virus genomes, we could estimate 4000 false positive predictions (using non-overlapping 90 bp length windows). Therefore, the best speciﬁcity we could achieve is close to 88%, and the actual prediction rate would lower depending on the viral genome structures (DNA versus RNA viruses) and gene organizations. WEB INTERFACE FOR VIR-MIR db Due to the large number of prediction results, we constructed a user-friendly web interface to present the viral candidate miRNAs. As shown in Figures 1 and 2, the