BMC Genomics 2009, 10:640 http://www.biomedcentral.com/1471-2164/10/640 30 25 20 re15 o c s - z 10 5 0 Bacteria Archaea 0 0.1 0.2 0.5 0.6 0.7 0.3 0.4 gGCSI aFDriicgffhueareeean2ce in GC skew strength between eubacteria and Difference in GC skew strength between eubacteria and archaea. Plot of gGCSI (X-axis) and z-score (Y-axis) for chromosomes of 710 eubacteria (black crosses) and 53 archaea (red closed circles) with 512 windows. Most of the archaeal chromosomes are located in the lower left corner, where gGCSI < 0.1 and z-score < 5. shaped cumulative GC skew graph (Supplemental Figure S3 [see Additional File 1]). These results indicate that the gGCSI score, together with the statistical significance indi- cated by the z-score, can successfully distinguish differ- ences in replication machinery between archaea and bacteria. The overall difference in the distributions of eubacteria and archaea could be observed using the origi- nal GCSI; however, different calculation in SA and in the geometric mean allows to capture the V-shaped cumula- tive GC graph for Methanococcus aeolicus more correctly with the aforementioned score of 0.107, whereas it was 0.071 with the original GCSI. Moreover, the new index allows the inclusion of small genomes such as that of Myc- oplasma genitalium to the analysis because of fixed window numbers, and the availability of z-score clearly elucidates the significant gGCSI. Note that the gGCSI is a measure of the clarity of V-shape cumulative GC skew. A high gGCSI score suggests strong mutation or selection pressures induced by bi-directional replication machinery starting from a single origin, whereas a low gGCSI score does not necessarily imply the existence of alternative replication machinery such as multiple replication origins. Weak GC skew can also result from long doubling times, as exemplified by low gGCSI scores in Mycoplasma and Cyanobacteria species. It is also worth noting that the gGCSI and z-score are weakly corre- lated (r = 0.578 and ρ = 0.678). Since z-score is calculated from the distribution of gGCSI values calculated for ran- domly shuffled genome sequences for 100 iterations, this value indicates the non-randomness of the observed gGCSI. Therefore, the correlation between the gGCSI score and its z-score indicates that high degree of skewness is not a random property that can happen by chance or due to certain bias in the genome such as extremely high GC content, and that certain mutation or selective pressure was required to shape the pronounced GC skew. Predic- tion of replication origins can be erroneous in species where GC skew is not clear or where multiple origins exist. gGCSI can thus be used as a confidence measure for GC skew-based predictions; according to the above results, chromosomes with gGCSI > 0.1 and z-score > 3 can be considered to have sufficient GC skew strength for accu- rate prediction with this number of windows. Difference in GC skew strength between plasmids with different types of replication machinery We tested the distribution of gGCSI values in 908 bacterial plasmids using 64 windows to match the smaller size of these genomes (Figure 3). Of the 908 plasmids, 697 were putative non-RCR replicons as determined by their lack of e r c2 o s - z 4 3 1 0 non-RCR RCR 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 gGCSI pFDliaigfsfuemrrieedns3ce in GC skew strength between RCR and non-RCR Difference in GC skew strength between RCR and non-RCR plasmids. Plot of gGCSI (X-axis) and z-score (Y- axis) for 211 RCR (red circles) and 697 non-RCR (black crosses) plasmids with 64 windows. Most RCR plasmids have gGCSI > 0.1 and z-score < 2, whereas non-RCR replicons show correlation in z-score and gGCSI. Page 6 of 10 (page number not for citation purposes)