PAGE 3 OF 8 Table 1. The list of herpesviruses to be analyzed Virus Alcelaphine herpesvirus 1 Ateline herpesvirus 3 Bovine herpesvirus 1 Bovine herpesvirus 4 Bovine herpesvirus 5 Callitrichine herpesvirus 3 Cercopithecine herpesvirus 1 Cercopithecine herpesvirus 15 Cercopithecine herpesvirus 17 Cercopithecine herpesvirus 2 Cercopithecine herpesvirus 8 Cercopithecine herpesvirus 9 Equid herpesvirus 1 Equid herpesvirus 2 Equid herpesvirus 4 Gallid herpesvirus 1 Gallid herpesvirus 2 Gallid herpesvirus 3 Human herpesvirus 1 Human herpesvirus 2 Human herpesvirus 3 Human herpesvirus 4 Human herpesvirus 5 strain AD169 Human herpesvirus 5 strain Merlin Human herpesvirus 6 Human herpesvirus 6B Human herpesvirus 7 Human herpesvirus 8 Ictalurid herpesvirus 1 Meleagrid herpesvirus 1 Murid herpesvirus 1 Murid herpesvirus 2 Murid herpesvirus 4 Ostreid herpesvirus 1 Pongine herpesvirus 4 Psittacid herpesvirus 1 Saimiriine herpesvirus 2 Suid herpesvirus 1 Tupaiid herpesvirus 1 Abbreviation AlHV1 AtHV3 BoHV1 BoHV4 BoHV5 CalHV3 CeHV1 CeHV15 MMRV CeHV2 CeHV8 CeHV7 EHV1 EHV2 EHV4 GaHV1 GaHV2 GaHV3 HSV1 HSV2 VZV EBV HCMV HCMV-M HHV6 HHV6B HHV7 HHV8 IcHV1 MeHV1 MCMV RCMV MUHV4 OsHV1 CCMV PSHV1 SaHV2 SHV1 THV Nucleic Acids Research, 2005, Vol. 33, No. 15 e134 Accession NC_002531 NC_001987 NC_001847 NC_002665 NC_005261 NC_004367 NC_004812 NC_006146 NC_003401 NC_006560 NC_006150 NC_002686 NC_001491 NC_001650 NC_001844 NC_006623 NC_002229 NC_002577 NC_001806 NC_001798 NC_001348 NC_001345 NC_001347 NC_006273 NC_001664 NC_000898 NC_001716 NC_003409 NC_001493 NC_002641 NC_004065 NC_002512 NC_001826 NC_005881 NC_003521 NC_005264 NC_001350 NC_006151 NC_002794 Length 130 608 108 409 135 301 108 873 138 390 149 696 156 789 171 096 133 719 150 715 221 454 124 138 150 224 184 427 145 597 148 687 174 077 164 270 152 261 154 746 124 884 172 281 230 287 235 645 159 321 162 114 153 080 137 508 134 226 159 160 230 278 230 138 119 450 207 439 241 087 163 025 112 930 143 461 195 859 Base composition (A, C, G, T) (0.27, 0.24, 0.22, 0.26) (0.32, 0.19, 0.17, 0.31) (0.14, 0.36, 0.37, 0.14) (0.30, 0.21, 0.20, 0.29) (0.12, 0.37, 0.38, 0.13) (0.26, 0.25, 0.25, 0.25) (0.13, 0.37, 0.38, 0.13) (0.18, 0.31, 0.31, 0.20) (0.24, 0.27, 0.26, 0.23) (0.12, 0.38, 0.38, 0.12) (0.26, 0.25, 0.24, 0.25) (0.29, 0.21, 0.20, 0.30) (0.22, 0.29, 0.28, 0.22) (0.22, 0.29, 0.28, 0.21) (0.25, 0.25, 0.25, 0.25) (0.26, 0.24, 0.24, 0.26) (0.28, 0.22, 0.22, 0.28) (0.23, 0.27, 0.27, 0.23) (0.16, 0.34, 0.34, 0.16) (0.15, 0.35, 0.35, 0.15) (0.27, 0.23, 0.23, 0.27) (0.20, 0.30, 0.29, 0.20) (0.22, 0.28, 0.29, 0.21) (0.21, 0.29, 0.29, 0.21) (0.29, 0.22, 0.21, 0.29) (0.29, 0.22, 0.21, 0.29) (0.32, 0.20, 0.17, 0.31) (0.24, 0.27, 0.26, 0.23) (0.21, 0.28, 0.28, 0.22) (0.26, 0.24, 0.24, 0.26) (0.20, 0.29, 0.30, 0.21) (0.19, 0.30, 0.31, 0.20) (0.27, 0.24, 0.23, 0.26) (0.31, 0.19, 0.19, 0.30) (0.19, 0.31, 0.31, 0.19) (0.19, 0.31, 0.30, 0.20) (0.33, 0.18, 0.16, 0.32) (0.13, 0.37, 0.37, 0.13) (0.17, 0.33, 0.34, 0.17) which is equal to (nA log pA + nC log pC + nG log pG + nT log pT). Consider two palindromes: CACGTACGTG and TTTTTAAAAA in a very CG-rich genome, say, with relative base frequencies pA ¼ pT ¼ 0.1 and pC ¼ pG ¼ 0.4. The latter palindrome is much less likely to occur than the former, and accordingly should receive a higher score to reﬂect its rarity compared with the former. Indeed, the calculated scores of the two palindromes turn out to be 14.7 for the former and 23.0 for the latter. Step (iii): Computing the window score The score of a window in the genome is simply the total of the scores of all the palindromes occurring in this window. A palindrome is considered in the window if its left-center is. By trying out a variety of window lengths with the method, we have found that it is best to choose the window length w at 0.5% of the genome length, rounded down to the nearest hundred bases for convenience. Also, we let consecutive win- dows overlap by half their lengths. That is, the ﬁrst window spans the ﬁrst through the wth bases, the second from the ( w 2 þ 1) to ( 32w)th bases and so on. Because of the way the sliding windows are constructed, the length of the last window is usually shorter than w. Step (iv): Selecting regions with significant palindrome clusters For the PCS, regions that harbor statistically signiﬁcant clus- ters of palindromes are identiﬁed using the scan statistics cri- terion as described in Leung et al. (14). As the criteria for statistical signiﬁcance for PLS and BWS have not yet been established, we use a non-parametric approach where a ﬁxed number of top scoring windows are chosen as the predicted locations of replication origins. It is well known that herpes- viruses have multiple replication origins. However, there does not appear to be any obvious rule to determine the number of top scoring windows that one should take. Based on sensitivity and positive predictive value consideration (deﬁned below), we ﬁnd that using the top 3–5 ranked windows for prediction works well for the herpesviruses. RESULTS AND DISCUSSION Scan statistics method versus the new scoring schemes To compare and contrast the two new scoring schemes with the scan statistics method, now called PCS, the sliding window plots for HCMV and HSV1 using PCS, PLS and BWS0 score schemes are displayed in Figure 2. In each plot, the scores of