BMC Genomics 2009, 10:640 http://www.biomedcentral.com/1471-2164/10/640 tional File 1]). In all analyses, skew index with non-GC skews distributed in much narrower range, and separation of different replication machineries was best demon- strated with gGCSI. Correlation between the skew indices of the plasmids and their host chromosomes was also highest with gGCSI, with gGCSI (r = 0.791), ATSI (r = 0.491), gKetoSI (r = 0.569), and gPurineSI (r = 0.528). Implementation and availability The algorithm described in this work is implemented as gcsi function in the 1.8.6 or above versions of G-language Genome Analysis Environment (G-language GAE) pack- age [45-47], which includes the ability to calculate gATSI, gKetoSI, and gPurineSI along with gGCSI. G-language GAE is freely available with open source code licensed under GNU General Public License, Therefore, researchers can readily utilize gGCSI in their analyses through the Perl Application Programming Interface, or through web serv- ices provided by the G-language Project [48]. Conclusions Generalised GC skew index (gGCSI) is a quantitative measure of GC skew strength in genomes of any length that enables comparative study of replication-related mutation or selection pressures in bacterial chromosomes and plasmids. The gGCSI can be used to suggest the type of replication machinery used, i.e., bi-directional replica- tion from a single origin and replication from multiple origins in eubacteria and archaea, as well as RCR in plas- mids. The correlation of the degree of GC skew between bacterial plasmids and their host chromosomes suggests that these replicons within the same cells have replicated using the same replication machinery. gGCSI can be a use- ful measure for the study of replication-related features in bacterial genomes, and the index also provides confidence measures for GC skew-based predictions of replication origins. Methods Software and genome sequences Genome analyses were conducted using the G-language Genome Analysis Environment version 1.8.6 [45-47], and gGCSI is implemented and released with this software package. The 846 complete chromosome sequences of eubacteria (710 strains, note that several strains contain multiple chromosomes) and archaea (53 strains) and 713 plasmid genomes were obtained from the NCBI FTP repository [49]. The 713 plasmids were further filtered to remove RCR replicons by excluding plasmids containing the RCR initiator protein Rep (COG5655: plasmid rolling circle replication initiator protein and truncated deriva- tives), leaving 697 genomes. A similarity search using BLASTP [50] with the 34 Rep sequences included in these genomes resulted in same number of filtered genomes. The 211 RCR plasmid genomes were downloaded through the links provided in the Database of Plasmid Replicons (DPR) [42]. For comparison of the strength in replication-related mutation or selection pressures between host chromosomes and plasmids, 302 chromo- somes of host bacteria that harbour 606 plasmids were used. Calculation of the GCSI The GCSI was calculated as the weighted arithmetic mean of SR and dist, as follows: GCSI = (k1SR + k2dist) / 2 (1) where k1 = 1/6000 and k2 = 1/600 were obtained from regression analysis of all available complete bacterial chromosomes. SR is the signal to noise ratio (S/N) of the 1-Hz power spectrum obtained from the fast Fourier transform (FFT) of the GC skew graph. FFT transforms a given signal to reveal the frequency components making up the input signal, which is computationally optimised by using powers of two for the window numbers. GC skew can be thought of as a discrete signal along the continuous axis of genomic position and FFT F(k) of a signal of length N, f(n), where n = 0, 1, ..., N -1, at frequency k, is calculated as follows: N −1 F(k) = ∑ f (n)e −i2p kn/N n=0 where −1 . The power spectrum PS(k) of F(k) was fur- ther defined as PS(k) = F(k) 2 , k = 0,1, 2,..., N − 1 (3) at each frequency k. In this power spectrum, GC skew shows the greatest contributing component at 1-Hz fre- quency, corresponding to the two replichores having opposite polarity (discrete sine wave) [21]. S/N of the 1 Hz frequency, i.e., SR, is calculated as follows: (2) (4) ⎛ N −1 ⎞ SR = PS(1) / ⎜ ∑ PS(k) ⎟ / (N − 2) ⎜ ⎟ ⎝ 2 ⎠ dist is calculated as the absolute difference between the maximum and minimum values of cumulative GC skew graph. Calculation of the gGCSI The gGCSI is calculated as the weighted geometric mean of SA and dist(norm), as follows: gGCSI = k1SA × k2dist(norm) (5) Page 8 of 10 (page number not for citation purposes)