Biotechnology Information (NCBI), National Institutes of Health, USA. It would be beneﬁcial to have a user- friendly web interface to query the predicted viral miRNA hairpins based on their familial taxonomic classiﬁcation. In addition, a host target gene prediction service is established based on the RNAhybrid program and the parsed 30-UTR gene sequences of human, mouse, rat, zebraﬁsh, rice and Arabidopsis species to search for potential target genes of user’s interest. DATA GENERATION Predicting hairpins from virus genome scaffold sequences using Srnaloop During miRNA maturation, the full-length pre-miRNA transcript needs to form a hairpin (stem-loop) structure. The secondary structure is folded via intramolecular base pairing and has been the most signiﬁcant criterion for computational identiﬁcation of miRNAs (13–16). The Srnaloop program, developed by Grad et al. (14), has been used to identify putative hairpin secondary structures from viral genomic sequences. All the viral genome sequences were obtained on 4 May 2006 from NCBI, National Institutes of Health, USA. The NCBI viral1. genomic.fna ﬁle comprises the genomic sequences of 2266 viruses, and the ﬁle size is 41 MB. The optimized parameters for Srnaloop were as reported previously (13), except for the parameters ‘–l 90’ and ‘–t 17’. These parameters, modiﬁed based on known viral pre-miRNAs, are speciﬁc for identifying hairpins that are up to 90 bases long and have a score of at least 17. Sequence and structural features filter To reduce the number of falsely predicted miRNAs based on hairpin structure alone, we have included several additional selection ﬁlters, namely GC content, minimum free energy of the core of hairpin structure (core mfe), minimum free energy of the whole hairpin (hairpin mfe) and the ratio of core mfe to hairpin mfe (ch_ratio) as deﬁned in a previous publication (13). To distinguish authentic miRNA candidates, we also employed addi- tional classiﬁcation criteria speciﬁc to known miRNAs. We ﬁrst investigated the features of known viral miRNAs as well as their precursors and then determined the reference range values of the selected features. The reference range values are listed in Table 1. Therefore, a candidate hairpin with sequence and structural features within the optimized reference range values was con- sidered to be a positive miRNA hairpin. Application of open reading frame (ORF) feature filter When predicting miRNAs in organisms other than viruses, a ‘conservation pattern’ is usually used to ﬁnd evolutionarily conserved miRNA candidates (13–16). However, this feature is not applicable for most viral miRNA prediction here. Cullen found only a few miRNAs located in protein-coding transcripts (17), which is used as a criterion to predict eukaryotic miRNAs (13,14). Therefore, we screened our candidate Nucleic Acids Research, 2008, Vol. 36, Database issue D185 Table 1. Distribution range of sequence and structure features from known viral miRNAs Distribution Reference value GC content core mfe hairpin mfe ch ratio 34–65 38–65 37.5 to 37.5 to 14.5 19.4 22 to 50.9 to 59.2 28.2 51–100 51–98 From the known viral pre-miRNA information, we evaluated the distributions of quantiﬁable sequence and structure features, namely GC content, core mfe, hairpin mfe and the ratio of core mfe to hairpin mfe. Because extreme values existed, we adopted the reference value to be used in the sequence and structure feature ﬁlter. hairpins to check if they overlap the ORFs belonging to the same virus. The sequences of the ORFs were extracted from viral1.protein.gbﬀ ﬁle downloaded from NCBI on 4 May 2006. Because viruses encode functional genes redundantly in a highly compact genomic region, we cannot exclude the possibility that viral miRNAs may overlap other viral protein-coding genes. This concern is more signiﬁcant for small genome RNA viruses. Therefore, we still considered the hairpins overlapping ORFs to be positive miRNA hairpins, but we marked the NP accession numbers of the overlapped ORF protein in the dataset for reference. DATABASE STATISTICS Candidate hairpins predicted by Srnaloop and additional feature filters We identiﬁed roughly 514 874 candidate hairpins from all viral genomic sequences by means of Srnaloop from 2266 viral genomes. They were then divided into 5P and 3P stem arms as putative miRNA candidates as done previously (13), because an miRNA could be located in either the 5’ arm or 3’ arm of the stem-loop precursors. Following the screen with the sequence and structure feature ﬁlter, 33 691 candidates survived. Among them, 5306 candidate hairpins do not overlap any ORF of the correspondent virus, implying they have higher likelihood to be authentic, but not absolutely as described earlier. All the hairpin information can be accessed from the web interface. We compared the sequences of these putative viral candidate miRNAs with the sequences of known miRNAs in miRBase (release 9.0). The positive match criterion required at least an 18-nt matched length with >90% identity. With this high stringency of sequence similarity, only seven candidates with unique IDs of 821661, 1341202, 1343051, 1408482, 1430283, 3325175 and 3663618 showed positive matches. For example, candidate 821661, identiﬁed from Cercopithecine monkey herpes virus 15, is similar to ebv-miR-BART19; candidates 1341202, 1343051 and 3325175, identiﬁed from chimpanzee cytomegalovirus, are similar to hcmv-miR-UL112, hcmv-miR-UL148D and hcmv-miR- UL112, respectively. It is possible that more matches could be found if the stringency of sequence similarity was lowered.