W370 Nucleic Acids Research, 2010, Vol. 38, Web Server issue status of the prediction and will eventually contain the prediction results. Alternatively, the user can bookmark the initial result page in the web browser and return to it at a later time. Lastly, the unique id provided after submitting the secondary structure prediction compute request can be used to access the results using another web form available on the server home page. A typical output from a completed RNA structure pre- diction is shown in Figure 3. The prediction result is pre- sented to the user in three diﬀerent formats: (i) An image of the predicted RNA secondary structure created by VARNA (19); (ii) An extended bracket notation in which nested base pairs are denoted as pairs of nested parentheses and helices corresponding to pseudoknot interactions are denoted as letters; (iii) The ‘CT’ ﬁle format that is also generated by other programs such as mfold (5). This format contains a list of the indices of the bases and their predicted base-pairing partners. RESULTS AND DISCUSSION The performance of the new RNA secondary structure prediction method was evaluated using two diﬀerent data sets. Data set 1 (corresponding to the results shown in Table 1) consists of 26 RNA sequences, whose tertiary structure is available in the Protein Data Bank (PDB). The reference secondary structure was obtained by extracting the base pair information from the PDB coordinate ﬁle using the program RNAview (20). Data set 2 consists of 241 RNA sequences and secondary structures originating from PseudoBase (21,22). In order to quantify the time-complexity of the folding method, we ﬁtted a function of the form a*Nb (with N being the number of residues in the input sequence) to the execution time needed for the cases of the 241 sequence set. We found that the execution time (measured in seconds) of the structure prediction is well described by the function 2.74*10 8*N4.47. The timing evaluation was performed on a computer with 4 GB of RAM and an Intel 64-bit Xeon processor (3.0MHz). We report in Tables 1 and 2 prediction results for these two data sets together with the corresponding results obtained by running the RNA secondary structure predic- tion programs HotKnots 2.0 (8), pknotsRG (7) and UNAFold (23). The average Matthews correlation coeﬃcient (MCC) obtained by comparing the base pairing pattern of the predicted secondary structures with their respective reference secondary structure is for data set 1 and CyloFold 0.83; this can be compared to pknotsRG (0.82), HotKnots 2.0 (0.75) and UNAFold (0.73) (see row of Table 1 named ‘All’). We divided this data set into two subsets according to the fraction of pseudoknot base pairs in the respective structures. The results can be seen in the last two rows of Table 1. The eight PDB structures with <5% pseudoknotted base pairs correspond to an average MCC of 0.87 for CyloFold compared to 0.81 for pknotsRG, 0.82 for HotKnots 2.0 and 0.87 for UNAFold. The 18 structures listed in Table 1 that have Figure 3. Screenshot of a typical prediction result returned by the CyloFold web server. The shown sequence corresponds to the bacterio- phage T2 gene 32 mRNA pseudoknot (PDB 2TPK). a pseudoknot amount >5% correspond to an average MCC of 0.81 for CyloFold, 0.82 for pknotsRG, 0.73 for HotKnots 2.0 and 0.66 for UnaFold. Using the larger data set 2, one obtains an average MCC of 0.752 for CyloFold and 0.748 for pknotsRG (Table 2). In Table 2 one can see the RNA secondary structure predictions obtained by CyloFold correspond to the highest MCC (compared to the programs pknotsRG, HotKnots 2.0 and UNAFold). It also has the highest average base pair prediction sensitivity (0.763). For another measure, the positive predictive value (how often are predicted base pairs part of the