BMC Genomics 2009, 10:640 http://www.biomedcentral.com/1471-2164/10/640 the RCR initiator Rep protein [25], and 211 were RCR plasmids obtained from the Database of Plasmid Repli- cons [42]. The 697 non-RCR plasmids showed a similar score distribution to those of bacterial genomes shown in Figure 2, with a correlation between the gGCSI and z-score (r = 0.420 and ρ = 0.355). The RCR plasmids were distrib- uted differently from the non-RCR plasmids (median of 0.134), mostly having high gGCSI values (median of 0.357) that were correlated with their z-scores (r = 0.195 and ρ = 0.182). As stated earlier, because RCR is based on strand displacement, one strand of the duplex DNA always serves as the template for replication, presumably resulting in continuous G/C bias along the entire genome without any shift point. This leads to high dist values con- current with low SA values. Whereas the resulting geomet- ric mean of these values (i.e., the gGCSI) becomes relatively high because one of the two values is high, the z-score remains low, because randomising the sequence will yield similar levels of SA and dist values and, subse- quently, a similar gGCSI. This characteristic distribution is observable in Figure 3, where the RCR plasmids are mostly distributed below the non-RCR plasmids, with insignificant z-scores (p > 0.01 for z < 2.33) and relatively high but narrowly distributed gGCSI scores. Correlation of GC skew strength between plasmids and their hosts In order to observe the effect of replication-related muta- tion or selection pressures on different replicons within the same cell, we analysed the correlation of gGCSI values between plasmids and chromosomes from the same bac- terial strains. Plasmids are transferable replicons that are capable of autonomous replication. Although the size, nucleotide composition, and available copy number of plasmids depend on growth conditions and hosts, plas- mids maintain a finite copy number per cell under specific growth conditions in a specific host. Copy number con- trol of plasmids is regulated through self-encoded nega- tive regulation mechanisms using antisense RNA or through repeated genomic sequence elements called iter- ons in order to retain sufficient partitioning upon host cell division and also to avoid overshooting so that the plas- mid can stably co-exist within the host cell without meta- bolic overload [43]. Therefore, plasmid replication is generally in harmony with host cell growth and thus with replication of the host chromosome, suggesting the exist- ence of similar selection pressure in this pair of genomic elements. Using 302 host chromosomes and the 606 plas- mids harboured by these strains, we have plotted the plas- mid-host pairs according to their respective gGCSI values calculated with 64 windows (Figure 4). Because many plasmids and host chromosomes showed low gGCSI scores < 0.2, a log-log plot clarified this correlation (r = 0.791 and ρ = 0.706). We also verified the consistency of results when using different numbers of windows (data hFCoiogsrutrsreelat4ion of GC skew strength between plasmids and Correlation of GC skew strength between plasmids and hosts. Log-log plot of gGCSI of plasmids against that of their corresponding host chromosomes. not shown). Our results indicate that plasmids tend to have GC skew strength similar to that of their known host chromosomes. Previous work has shown similarity in dinucleotide com- position between plasmids and host chromosomes [30,31]. This similarity is assumed to be caused by host- specific mutation biases of replication machineries, but the exact mechanisms remain unknown. Our finding that plasmids tend to be similar in GC skew strength to their host chromosomes strongly supports the assumption that host-specific properties of replication machineries homogenise the nucleotide composition of replicons in the cell. Application of gGCSI to other genomic compositional skews This manuscript has thus far only considered the GC skew; however, other genomic compositional skews can be alternatively calculated using A+T, keto (G+T), or purine (A+G) bases, as AT skew (T-A)/(T+A), Keto skew (A+C-G-T)/(A+T+G+C), and Purine skew (C+T-A-G)/ (A+T+G+C), respectively [44]. By utilizing these skew val- ues as input instead of GC skew, we can likewise obtain gATSI, gKetoSI, and gPurineSI. In order to assess the appli- cability of these indices in comparison to the gGCSI, we have reproduced the Figures 2 to 4 using these indices (Supplemental Figures S4a-c, S5a-c, and S6a-c [see Addi- Page 7 of 10 (page number not for citation purposes)