chromosome were counted and divided by chromosome size, giving the mean chromosomal GC content GC. A similar counting was performed for all 100 bp non- overlapping windows along the chromosome, giving the mean GC content GCi for window i. The difference, Di, between the mean GC content of window i, and the mean chromosomal GC content, can therefore be writ- ten as: Di = GCi − GC The quantity GCVAR is then defined as the log-trans- formed average of the absolute value of the difference between the mean GC content of each non-overlapping sliding window i and mean chromosomal GC content: Bohlin et al. BMC Genomics 2010, 11:464 http://www.biomedcentral.com/1471-2164/11/464 been instrumental in the evolution of eukaryotes [36]. Prokaryotes were also affected by the introduction of oxygen [35] in that while some remained anaerobic others adapted to an aerobic metabolism [37]. The precise effect of increase in atmospheric oxygen on prokaryotic genomes is debated [37,38]. A negative correlation has been found between proteomic oxygen content and genomic GC content [37]. Although it has been suggested that genomic GC content is also affected by an aerobic lifestyle [8], the effects on prokaryote gen- ome composition has remained unclear [37,38]. Indeed, our own results presented above do not support any connection between genomic GC content and aerobio- sis. Our results did, however, find a significant associa- tion between GCVAR and oxygen requirement. This greater GC content homogeneity found in aerobes implies that the genomes of these organisms have been subjected to stronger selective pressures than the gen- omes of anaerobes. This is supported by the recent report that metabolic networks of aerobic bacteria are more complex than those of anaerobic bacteria [35]. From Figure 2 it can be seen that GCVAR appears to be progressively decreasing in facultative and aerobic pro- karyotes, respectively. Conclusion In summary, we found that GCVAR was associated with oxygen requirement. It is possible that GCVAR is asso- ciated with GC content, but from Figure 3 it appears to be a highly non-linear relationship. Other factors such as genome size, habitat and growth temperature were not found significant in the GCVAR model. GCVAR was however found to be higher in archaea than bacteria. By adding an interaction term to model the closer similarity between the genomes in the same phylogenetic group, we found that oxygen requirement was not significantly associated with mean genomic GC content in microbes. The different results obtained for the models describ- ing GCVAR and mean genomic GC content imply that these properties are governed by different influences, or are interrelated in a non-linear manner. Thus, our find- ings suggest that GCVAR is linked with oxygen require- ment, while mean genomic GC content is not. Methods All genomes and related information were gathered from the NCBI web site http://www.ncbi.nlm.nih.gov/ genomes/lproks.cgi. The statistical package R[39] was used for statistical analyses and graphical representations. The GCVAR measure To calculate GC variation within a prokaryotic genome, the number of guanine and cytosine nucleotides in a GCVAR = log( 1 N N ∑ | Di |) i=1 N is the maximum number of non-overlapping 100 bp sliding windows that can fit into the chromosome that is being analyzed. The log-transformation makes GCVARs empirical distribution more Gaussian-like, for convenience in subsequent linear regression model fit- ting and statistical inference. Since the optimal sized sliding window varies from genome to genome [18], dif- ferent window lengths were tested. The sliding window width of 100 bp was chosen to make the test as sensitive as possible. The other sliding window lengths tested contained 500, 1000, and 2000 bp. The 100 bp sliding window was found to be large enough to carry genome specific information without discarding weak genomic signals as noise. Since the aim of this study was to examine GC content difference within genomes, non- overlapping sliding windows were used to avoid bias and interactions from neighboring genetic regions. Linear models Linear regression analysis was used to examine influ- ences affecting GCVAR. In our first analysis we made a regression of GCVAR onto GC and Size (genome size in Mb), also including the categorical variables phylum (22 phylogenetic groups), required oxygen (aerobic, faculta- tive, anaerobic), growth temperature (psychrophilic, mesophilic and thermophilic), pathogenicity (pathogenic, non-pathogenic) and habitat (aquatic, host-associated, multiple, specialized and terrestrial) as predictors or explanatory variables. The model can be written as: E(GCVARyotah) =  +  y +  o +  t + +a +  h +  ⋅ GC +  ⋅ Size (3) where μ is the overall intercept, ay, y = 1, ...,22, are the effects of phylum, δo, o = 1,2,3, are the effects of Page 6 of 8 (1) (2)