BACKGROUND
the knowledge of the chemical basis for nucleic acids goes back more than a hundred years, to the work of miescher  <cit> .
by the early 1950's, it was known that the relative frequency of the four dna bases  was different for different organisms  <cit> , and in general the number of a's was equal to the number of t's, and the number of g's was the same as the number of c's; this is known as 'chargaff's first parity rule'  <cit> .
further, for nearly all genomes studied, the parity rule appears to extend to each strand of the chromosome, when averaged over long distances  <cit> , although in bacterial chromosomes, there is a clear bias of g's towards the replication leading strand, and for some genomes  the a's are also biased towards the leading strand  <cit> .
for a circular chromosome with the replication origin and terminus on exactly opposite sides, this bias of g's towards the replication leading strand will average out to near zero, when one only looks at the dna sequence in the genbank file, and the sequence will appear to conform to chargaff's second rule.
from the genbank database at ncbi http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi it can be seen that gc content in prokaryotes ranges from  <dig> % in carsonella ruddii strain pv to  <dig> % in anaeromyxobacter dehalogenans strain 2cp-c. within a given genome, the gc content along the chromosome can vary, although since most bacterial genomes have a high coding density, usually the variation is less than that found in eukaryotes  <cit> .
the average genomic gc content is an important property in microbial genomes and has been associated with properties such as genome size  <cit> , oxygen, and nitrogen exposure  <cit>  and specific habitats  <cit> .
for instance, intracellular bacteria have, on average, smaller genomes and are mostly at rich, while soil bacteria tend to have larger genomes and higher %gc  <cit> .
higher at content in intracellular bacteria may be attributed to a loss of repair genes; this loss will eventually lead to an increase in mutation rates from cytosine to thymine  <cit> .
genes not expressed will eventually lead to reduced genome sizes  <cit> .
higher gc content in soil bacteria may be due to the increased availability of nitrogen  <cit> .
however, increased nitrogen in the soil does not explain why gc rich bacteria often have larger genomes.
the base composition in gc rich genomes might reflect stronger selective forces than at rich genomes  <cit> .
this may indicate that gc rich microbes live in more complex environments than intracellular bacteria  <cit> .
the reasons for stronger selective forces and gc richness is not known, but may be connected to the fact that considerably more energy is required to de-stack gc rich dna sequences than at rich dna sequences  <cit> .
although gc content has been found to vary only slightly within prokaryotic genomes some regions differ more than others.
a large region flanking the replication origin, for instance, is more gc rich than the average genomic gc content  <cit>  whereas the region around replication terminus is more at rich  <cit> .
surface proteins and rna genes often have gc content that differs from the average genomic gc content  <cit> , and protein coding regions have been found to be, on average, approximately 5% more gc rich than non-coding regions  <cit> .
in addition to being more gc rich, coding regions have been found to be more homogeneous in terms of base composition than non-coding regions  <cit> .
the gc heterogeneity in coding regions has, however, been found to be associated with mean genomic at content in non-coding regions  <cit> .
in other words, gc content variability tends to increase with higher mean genomic at content in non-coding regions.
horizontally transferred dna may have a different fraction of gc than the host genome as a result of different evolutionary pressures  <cit> .
since horizontally transferred dna is often linked to pathogenesis in microbes  <cit> , detection of such regions is of great importance.
the gc content of foreign dna will, however, become progressively more similar to the host genome in a process known as amelioration  <cit>  making such regions more difficult to detect as time progress  <cit> .
the conformation of base compositional patterns from foreign dna to host dna may be related to the finding that a particular subunit of the dna polymerase iii, the pol iii α subunit, appears to be driving genomic gc content in prokaryotes  <cit> .
there is a considerable amount of research and documentation related to mean genomic gc content in prokaryotes demonstrating that this property is the result of many factors interacting in a highly complex manner  <cit> .
on the other hand, analysis of genomic gc content variability within microbial chromosomes, has received much less attention.
a more recent overview of methodology used to analyze gc content variation within genomes can be found in bernaola-galvan et.
al.,  <cit> , and a study of how intra-genomic gc content variation affects codon usage is described by daubin et.
al.
<cit> .
in the present work, we introduce the gcvar measure to examine gc content variability within prokaryotic genomes.
the gcvar metric gives a measure of how gc content varies within a given genome with respect to the mean genomic gc content.
a low gcvar thus points to little gc content variation, or gc content homogeneity, within the genome, while a high gcvar designates varying gc content, or gc content heterogeneity.
to the best of our knowledge, no study has examined the interplay between environmental factors and gc content homogeneity in prokaryotes.
in the present study the aim was therefore to examine whether gc content homogeneity in prokaryotes, measured here using the gcvar measure, could be related to specific factors in the environment such as temperature and oxygen, as well as the broader properties implicated in phylogeny and gc content.
to do this, regression analyses were performed using gcvar as the response variable.
the response variable was fitted to the following variables: oxygen requirement , phylum, genomic gc content, genome size, growth temperature , pathogenicity  and habitat .
the dataset consisted of  <dig> genomes  with similar strains and species removed from the analysis to reduce phylogenetic bias.
RESULTS
gc distribution within genomes
the histograms in figure  <dig> shows the statistical distributions of gc content differences, di = gci - gc , methods section), within four at-rich and four gc-rich genomes.
the statistical distributions shown in figure  <dig> are based on the differences, or residuals, between the gc content of a  <dig> bp non-overlapping sliding window and mean genomic gc content for each of the  <dig> genomes.
figure  <dig> therefore shows the statistical distributions of how gc contents differences are distributed within each of the described genomes.
with the exception of carsonella rudii, one of the smallest bacterial genomes currently sequenced , all empirical distributions follow the bell shaped gaussian curve.
this indicates that gc difference within prokaryotic genomes appears to be a sum of many independent processes, giving a gaussian like distribution according to the central limit theorem .
thus, it seems likely that for most prokaryotic genomes intra-genomic gc content variation appears to follow a random, white-noise like pattern, devoid of any complex and long-range interacting factors.
the gcvar regression model
we define gcvar as a measure of the intra-genomic gc variation in a genome.
a linear regression model was fitted to data for  <dig> prokaryote chromosomes with gcvar as the response and with gc content, size, phylum, oxygen requirement, growth temperature, pathogenicity and habitat as covariates  in the methods section).
the results of the gcvar regression model can be observed in table  <dig>  and in figure  <dig> we show the 95% confidence intervals for the significant effects.
the variables: size, growth temperature, pathogenicity and habitat had no significant influence on gcvar, and were therefore discarded from further analyses.
the variable gc is continuous while phylum and oxygen are categorical variables.
note that for the phylum variable we have used the sum-to-zero parameterization, i.e.
all estimated effects are deviations from the mean phylum effect.
for the oxygen requirement variable however, we used a relative parameterization where the category "aerobic" is the reference, i.e.
the estimated effect is the deviation from the aerobic effect.
in addition, the number of chromosomes, average %gc, and average genomes size in mbp, are included for each phylogenetic group.
gcvar in phyla
environmental factors and phylogenetic bias
to examine how gcvar was affected by phylogenetic bias a regression model similar to the one described above was fitted, i.e.
gcvar was the response variable, with mean genomic gc content, oxygen requirement, habitat, optimal growth temperature, and genome size as predictors.
in addition, an interaction term between gc content and phylum was added to account for more similar gc content within phyla  in the methods section).
using this regression model we found that oxygen requirement was the only significant factor .
gcvar was significantly higher in the genomes of anaerobic microbes  as compared to the genomes of aerobic microbes, meaning that the genomes of anaerobic microbes tend to have a more heterogeneous distribution of gc content than genomes of aerobic microbes .
facultative microbes were found to have gcvar values in the region between aerobic and anaerobic microbes .
the associations between mean genomic gc content, gcvar and oxygen requirement
the regression models described above indicates that aerobic microbes have genomes with more homogeneous gc content than those of organisms with facultative and anaerobic oxygen requirement .
it has been shown that gc rich genomes tend to be more homogeneous in terms of base composition than at rich genomes  <cit> .
aerobic microbes have been associated with gc rich genomes  <cit> .
this result is supported by our linear regression model only when we ignore phylogenetic bias is .
however, adding phyla as a predictor  in the methods section) fails to demonstrate such an association .
we found that mean genomic gc content was associated with gcvar, but there was no linear relationship between mean genomic gc content and gcvar , although this does not exclude a non-linear relationship.
gcvar and dna uptake
there are many indications that mean genomic gc content is as much affected by the environment as by phyla  <cit> .
it is also well known that chromosomally integrated foreign dna may differ in base composition as compared to host dna.
the difference in base composition between foreign and host dna is assumed to be the result of exposure to different selective pressures.
it is thought that such genetic regions may be acquired from horizontal transfer or other means of dna uptake  <cit> .
since pathogenesis is often associated with horizontally transferred dna, i.e.
pathogenicity islands,  <cit> , establishing a link between any genomic property and horizontal transfer is of considerably interest.
however, no significant association  was found between the dichotomous pathogenicity factor and gcvar using the regression model that included all covariates discussed above  in the methods section).
base composition and oxygen requirement
the introduction of atmospheric oxygen is presumed to have had profound effects on environment and life  <cit> .
increase in atmospheric oxygen is believed to have influenced cellular compartmentalization and thus to have been instrumental in the evolution of eukaryotes  <cit> .
prokaryotes were also affected by the introduction of oxygen  <cit>  in that while some remained anaerobic others adapted to an aerobic metabolism  <cit> .
the precise effect of increase in atmospheric oxygen on prokaryotic genomes is debated  <cit> .
a negative correlation has been found between proteomic oxygen content and genomic gc content  <cit> .
although it has been suggested that genomic gc content is also affected by an aerobic lifestyle  <cit> , the effects on prokaryote genome composition has remained unclear  <cit> .
indeed, our own results presented above do not support any connection between genomic gc content and aerobiosis.
our results did, however, find a significant association 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 genomes of anaerobes.
this is supported by the recent report that metabolic networks of aerobic bacteria are more complex than those of anaerobic bacteria  <cit> .
from figure  <dig> it can be seen that gcvar appears to be progressively decreasing in facultative and aerobic prokaryotes, respectively.
CONCLUSIONS
in summary, we found that gcvar was associated with oxygen requirement.
it is possible that gcvar is associated with gc content, but from figure  <dig> 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 describing 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 findings suggest that gcvar is linked with oxygen requirement, while mean genomic gc content is not.

 the knowledge of the chemical basis for nucleic acids goes back more than a hundred years, to the work of miescher. by the early 1950's, it was known that the relative frequency of the four dna bases was different for different organisms, and in general the number of a's was equal to the number of t's, and the number of g's was the same as the number of c's; this is known as'chargaff's first parity rule'. further, for nearly all genomes studied, the parity rule appears to extend to each strand of the chromosome, when averaged over long distances, although in bacterial chromosomes, there is a clear bias of g's towards the replication leading strand, and for some genomes the a's are also biased towards the leading strand. for a circular chromosome with the replication origin and terminus on exactly opposite sides, this bias of g's towards the replication leading strand will average out to near zero, when one only looks at the dna sequence in the genbank file, and the sequence will appear to conform to chargaff's second rule. from the genbank database at ncbi http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi it can be seen that gc content in prokaryotes ranges from % in carsonella ruddii strain pv to % in anaeromyxobacter dehalogenans strain 2cp-c. within a given genome, the gc content along the chromosome can vary, although since most bacterial genomes have a high coding density, usually the variation is less than that found in eukaryotes. the average genomic gc content is an important property in microbial genomes and has been associated with properties such as genome size, oxygen, and nitrogen exposure and specific habitats. for instance, intracellular bacteria have, on average, smaller genomes and are mostly at rich, while soil bacteria tend to have larger genomes and higher %gc. higher at content in intracellular bacteria may be attributed to a loss of repair genes; this loss will eventually lead to an increase in mutation rates from cytosine to thymine. genes not expressed will eventually lead to reduced genome sizes. higher gc content in soil bacteria may be due to the increased availability of nitrogen. however, increased nitrogen in the soil does not explain why gc rich bacteria often have larger genomes. the base composition in gc rich genomes might reflect stronger selective forces than at rich genomes. this may indicate that gc rich microbes live in more complex environments than intracellular bacteria. the reasons for stronger selective forces and gc richness is not known, but may be connected to the fact that considerably more energy is required to de-stack gc rich dna sequences than at rich dna sequences. although gc content has been found to vary only slightly within prokaryotic genomes some regions differ more than others. a large region flanking the replication origin, for instance, is more gc rich than the average genomic gc content whereas the region around replication terminus is more at rich. surface proteins and rna genes often have gc content that differs from the average genomic gc content, and protein coding regions have been found to be, on average, approximately 5% more gc rich than non-coding regions. in addition to being more gc rich, coding regions have been found to be more homogeneous in terms of base composition than non-coding regions. the gc heterogeneity in coding regions has, however, been found to be associated with mean genomic at content in non-coding regions. in other words, gc content variability tends to increase with higher mean genomic at content in non-coding regions. horizontally transferred dna may have a different fraction of gc than the host genome as a result of different evolutionary pressures. since horizontally transferred dna is often linked to pathogenesis in microbes, detection of such regions is of great importance. the gc content of foreign dna will, however, become progressively more similar to the host genome in a process known as amelioration making such regions more difficult to detect as time progress. the conformation of base compositional patterns from foreign dna to host dna may be related to the finding that a particular subunit of the dna polymerase iii, the pol iii α subunit, appears to be driving genomic gc content in prokaryotes. there is a considerable amount of research and documentation related to mean genomic gc content in prokaryotes demonstrating that this property is the result of many factors interacting in a highly complex manner. on the other hand, analysis of genomic gc content variability within microbial chromosomes, has received much less attention. a more recent overview of methodology used to analyze gc content variation within genomes can be found in bernaola-galvan et. al.,, and a study of how intra-genomic gc content variation affects codon usage is described by daubin et. al.. in the present work, we introduce the gcvar measure to examine gc content variability within prokaryotic genomes. the gcvar metric gives a measure of how gc content varies within a given genome with respect to the mean genomic gc content. a low gcvar thus points to little gc content variation, or gc content homogeneity, within the genome, while a high gcvar designates varying gc content, or gc content heterogeneity. to the best of our knowledge, no study has examined the interplay between environmental factors and gc content homogeneity in prokaryotes. in the present study the aim was therefore to examine whether gc content homogeneity in prokaryotes, measured here using the gcvar measure, could be related to specific factors in the environment such as temperature and oxygen, as well as the broader properties implicated in phylogeny and gc content. to do this, regression analyses were performed using gcvar as the response variable. the response variable was fitted to the following variables: oxygen requirement, phylum, genomic gc content, genome size, growth temperature, pathogenicity and habitat. the dataset consisted of genomes with similar strains and species removed from the analysis to reduce phylogenetic bias.  gc distribution within genomes the histograms in figure shows the statistical distributions of gc content differences, di = gci - gc, methods section), within four at-rich and four gc-rich genomes. the statistical distributions shown in figure are based on the differences, or residuals, between the gc content of a bp non-overlapping sliding window and mean genomic gc content for each of the genomes. figure therefore shows the statistical distributions of how gc contents differences are distributed within each of the described genomes. with the exception of carsonella rudii, one of the smallest bacterial genomes currently sequenced, all empirical distributions follow the bell shaped gaussian curve. this indicates that gc difference within prokaryotic genomes appears to be a sum of many independent processes, giving a gaussian like distribution according to the central limit theorem. thus, it seems likely that for most prokaryotic genomes intra-genomic gc content variation appears to follow a random, white-noise like pattern, devoid of any complex and long-range interacting factors. the gcvar regression model we define gcvar as a measure of the intra-genomic gc variation in a genome. a linear regression model was fitted to data for prokaryote chromosomes with gcvar as the response and with gc content, size, phylum, oxygen requirement, growth temperature, pathogenicity and habitat as covariates in the methods section). the results of the gcvar regression model can be observed in table and in figure we show the 95% confidence intervals for the significant effects. the variables: size, growth temperature, pathogenicity and habitat had no significant influence on gcvar, and were therefore discarded from further analyses. the variable gc is continuous while phylum and oxygen are categorical variables. note that for the phylum variable we have used the sum-to-zero parameterization, i.e. all estimated effects are deviations from the mean phylum effect. for the oxygen requirement variable however, we used a relative parameterization where the category "aerobic" is the reference, i.e. the estimated effect is the deviation from the aerobic effect. in addition, the number of chromosomes, average %gc, and average genomes size in mbp, are included for each phylogenetic group. gcvar in phyla environmental factors and phylogenetic bias to examine how gcvar was affected by phylogenetic bias a regression model similar to the one described above was fitted, i.e. gcvar was the response variable, with mean genomic gc content, oxygen requirement, habitat, optimal growth temperature, and genome size as predictors. in addition, an interaction term between gc content and phylum was added to account for more similar gc content within phyla in the methods section). using this regression model we found that oxygen requirement was the only significant factor. gcvar was significantly higher in the genomes of anaerobic microbes as compared to the genomes of aerobic microbes, meaning that the genomes of anaerobic microbes tend to have a more heterogeneous distribution of gc content than genomes of aerobic microbes. facultative microbes were found to have gcvar values in the region between aerobic and anaerobic microbes. the associations between mean genomic gc content, gcvar and oxygen requirement the regression models described above indicates that aerobic microbes have genomes with more homogeneous gc content than those of organisms with facultative and anaerobic oxygen requirement. it has been shown that gc rich genomes tend to be more homogeneous in terms of base composition than at rich genomes. aerobic microbes have been associated with gc rich genomes. this result is supported by our linear regression model only when we ignore phylogenetic bias is. however, adding phyla as a predictor in the methods section) fails to demonstrate such an association. we found that mean genomic gc content was associated with gcvar, but there was no linear relationship between mean genomic gc content and gcvar, although this does not exclude a non-linear relationship. gcvar and dna uptake there are many indications that mean genomic gc content is as much affected by the environment as by phyla. it is also well known that chromosomally integrated foreign dna may differ in base composition as compared to host dna. the difference in base composition between foreign and host dna is assumed to be the result of exposure to different selective pressures. it is thought that such genetic regions may be acquired from horizontal transfer or other means of dna uptake. since pathogenesis is often associated with horizontally transferred dna, i.e. pathogenicity islands,, establishing a link between any genomic property and horizontal transfer is of considerably interest. however, no significant association was found between the dichotomous pathogenicity factor and gcvar using the regression model that included all covariates discussed above in the methods section). base composition and oxygen requirement the introduction of atmospheric oxygen is presumed to have had profound effects on environment and life. increase in atmospheric oxygen is believed to have influenced cellular compartmentalization and thus to have been instrumental in the evolution of eukaryotes. prokaryotes were also affected by the introduction of oxygen in that while some remained anaerobic others adapted to an aerobic metabolism. the precise effect of increase in atmospheric oxygen on prokaryotic genomes is debated. a negative correlation has been found between proteomic oxygen content and genomic gc content. although it has been suggested that genomic gc content is also affected by an aerobic lifestyle, the effects on prokaryote genome composition has remained unclear. indeed, our own results presented above do not support any connection between genomic gc content and aerobiosis. our results did, however, find a significant association 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 genomes of anaerobes. this is supported by the recent report that metabolic networks of aerobic bacteria are more complex than those of anaerobic bacteria. from figure it can be seen that gcvar appears to be progressively decreasing in facultative and aerobic prokaryotes, respectively.  in summary, we found that gcvar was associated with oxygen requirement. it is possible that gcvar is associated with gc content, but from figure 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 describing 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 findings suggest that gcvar is linked with oxygen requirement, while mean genomic gc content is not. 
we have examined how the gc content varies within microbial genomes to assess whether this property can be associated with certain biological functions related to the organism's environment and phylogeny. a significant association between gcvar and mean gc content was also found but appears to be non-linear and varies greatly among phyla. bacterial genomes possess varying gc content and cytosines per total of the four bases within the genome) but within a given genome, gc content can vary locally along the chromosome, with some regions significantly more or less gc rich than on average. we utilize a new quantity gcvar, the intra-genomic gc content variability with respect to the average gc content of the total genome. although an association has previously been found between mean genomic gc content and oxygen requirement, our analysis suggests that no such association exits when phylogenetic bias is accounted for. our findings show that gcvar is linked with oxygen requirement, while mean genomic gc content is not. we therefore suggest that gcvar should be used as a complement to mean gc content. gcvar was significantly higher among anaerobes than both aerobic and facultative microbes. the regression analyses indicated that gcvar was significantly associated with domain, phylum, and oxygen requirement.
 bacterial genomes possess varying gc content and cytosines per total of the four bases within the genome) but within a given genome, gc content can vary locally along the chromosome, with some regions significantly more or less gc rich than on average. we have examined how the gc content varies within microbial genomes to assess whether this property can be associated with certain biological functions related to the organism's environment and phylogeny. we utilize a new quantity gcvar, the intra-genomic gc content variability with respect to the average gc content of the total genome. a low gcvar indicates intra-genomic gc homogeneity and high gcvar heterogeneity.  the regression analyses indicated that gcvar was significantly associated with domain, phylum, and oxygen requirement. gcvar was significantly higher among anaerobes than both aerobic and facultative microbes. although an association has previously been found between mean genomic gc content and oxygen requirement, our analysis suggests that no such association exits when phylogenetic bias is accounted for. a significant association between gcvar and mean gc content was also found but appears to be non-linear and varies greatly among phyla.  our findings show that gcvar is linked with oxygen requirement, while mean genomic gc content is not. we therefore suggest that gcvar should be used as a complement to mean gc content. 