PAGE 7 OF 8 Nucleic Acids Research, 2005, Vol. 33, No. 15 e134 Table 4. Prediction performance of various scoring schemes, PLS and BWS, based on top 3 scoring windows Virus BoHV1 BoHV4 BoHV5 CeHV1 CeHV2 CeHV7 EBV EHV1 EHV4 GaHV1 HCMV HHV6 HHV6b HHV7 HSV1 HSV2 RCMV SHV1 VZV Known ORIs/Names 111 080–111 300 126918–127 138 97 143–98 850 113 206–113 418 129 595–129 807 61 592–61 789 61 795–61 992 132 795–132 796 132 998–132 999 149 425–149 426 149 628–149 629 61 445–61 542 129 452–129 623 144 386–144 557 109 627–109 646 118 613–118 632 7315–9312 52 589–53 581 126 187–126 338 73 900–73 919 119 462–119 481 138 568–138 587 24 738–25 005 93 201–94 646 67 617–67 993 68 740–69 581 66 685–67 298 62 475 131 999 146 235 62 930 132 760 148 981 75 666–78 970 63 848–63 908 114 393–115 009 129 593–130 209 110 087–110 350 119 547–119 810 (oriS) (oriS) (oriLyt) (oriLyt) (oriLyt) (oriL1) (oriL2) (oriS1) (oriS2) (oriS2) (oriS1) (oriL) (oriS) (oriS) (oriP) (oriLyt) (oriL) (oriS) (oriS) (oriL) (oriLyt) (oriLyt) (oriLyt) (oriL) (oriS) (oriS) (oriL) (oriS) (oriS) (oriLyt) (oriL) (oriS) (oriS) PCS 1.75 mu 1.61 mu — — — — — — — — — — — — — — contains ori contains ori — — — — — contains ori — — — — — — — — — overlaps ori — — — — — PLS 1.63 mu 1.87 mu — — — 0.057 mu 0.18 mu 0.13 mu 0.0016 mu 0.016 mu 0.11 mu 0.07 mu 0.02 mu 0.17 mu — — 0.41 mu 0.067 mu — — — — — 0.055 mu — 0.024 mu — 0.11 mu 1.41 mu 1.42 mu — — — 0.62 mu — — — 0.094 mu 0.22 mu BWS1 1.63 mu 1.87mu — 0.06 mu 0.07 mu 0.057 mu 0.18 mu 0.13 mu 0.0016 mu 0.016 mu 0.11 mu 0.07 mu 0.02 mu 0.17 mu — — 0.41 mu 0.067 mu — — — — — 0.055 mu — — — 0.11 mu 1.41 mu 1.42 mu — — — 0.62 mu — — — 0.094 mu 0.22 mu The table shows the distance between each known origin from the nearest significant palindrome cluster for PCS, or the nearest high scoring window for PLS and BWS1 if the center of the cluster or window is within 2 mu of the origin. For example, one of the top 3 scoring windows under the PLS (and BWS) for RCMV is 0.62 map unit away from the RCMV oriLyt. sequence features should be combined to produce the optimal prediction results is the subject of an ongoing investigation. While it is encouraging to see that close to 80% of replica- tion origins can be predicted using a palindrome-based scoring scheme like BWS, we have also noted that the positive pre- dictive value is rather low whenever the corresponding sens- itivity exceeds 50%. This means that a substantial percentage of the high-scoring windows do not correspond to conﬁrmed replication origins. On closer examination of these high scor- ing windows which are not replication origins, some of them turn out to be regulatory sequences such as transcription factor binding sites. So far, we have not made use of palindromes to predict regulatory sites, but this would be an important area to explore. Our prediction scheme is geared towards herpesviruses and still needs to be tested on other DNA viruses. There are a few other methods proposed for prediction of replication origins for bacterial, archaeal and yeast genomes (20–23). These methods, which are based on DNA asymmetry, ﬂanking sequence similarity, z-curves, might be adapted to work on viral DNA as well. Finally, we note that these endeavors to accurately predict replication origins has motivated several interesting and challenging mathematical problems about random letter sequences and probability distributions of patterns on them. We are now dealing with palindromes only but there will be a stream of similar problems about direct and inverted repeats that calls for efforts from the mathematical scientists. ACKNOWLEDGEMENTS We would like to thank the editor and two anonymous reviewers for helpful comments and suggestions. Kwok Pui Choi was supported by BMRC grant BMRC01/1/21/19/140 and National University of Singapore ARF Research grant R-146-000-068-112; and Ming-Ying Leung by NIH grants 5S06-GM08012-34 and RCMI 2G13-RR008124. Funding to pay the Open Access publication charges for this article was provided by NIH grant 5S06-GM08012-34. Conflict of interest statement. None declared.