prior to the genome era , compositional signatures ( 1 ) or sequence alignments ( 2 ) were used to delineate the phylogenetic patterns across organisms .
the availability of entire genome sequences has sparked a further development of methods for phylogenetic reconstructions on a genome - wide scale . following this long tradition in molecular evolution ,
similar methods were expanded to encompass complete genome information , based on either compositional patterns ( 3,4 ) or concatenated alignments of orthologs ( 5,6 ) .
more recently , methods that exploit the entire gene complement of completely sequenced genomes have been developed .
these include phylogenies based on patterns of conservation of gene order ( 7 ) , gene fusion events ( 8) , gene content ( 7,913 ) , protein folds ( 12 ) , and average ortholog similarity ( 14 ) .
only a few of these approaches were successful in resolving difficult cases , such as correctly grouping pathogens having highly reduced genomes with their free - living relatives and clustering proteobacteria into a monophyletic group ( 7,9,14 ) .
in addition , most of the above mentioned methods are ( i ) not scalable to hundreds of species ( e.g. concatenated alignments ) , ( ii ) unable to place correctly species with reduced genomes ( 13 ) and ( iii ) strongly affected by the number and phylogenetic proximity of species ( e.g. gene order ) ( 15 ) .
herein , we define a new composite measure termed  genome conservation , which expresses both the conservation of sequence and gene content between two genomes . this value is derived from the sum of alignment scores between all proteins for every pair of organisms .
larger genomes tend to share more genes , irrespective of their phylogenetic distance ( 9 ) .
thus , a higher conservation score can result from a higher number of shared genes , rather than from phylogenetic proximity . to counterbalance this effect
the genome conservation method is naturally adjusted for missing genes and for gene lengths . if a gene is absent in one of the compared genomes , its contribution to the similarity is zero . however ,
if this absence is a direct result of reductive evolution and difference in genome sizes , absence of this gene would be calibrated by the normalization scheme described below .
the gene length is taken into account in the summation of the blast scores : longer genes generate greater alignment scores and thus , would be more important contributors than shorter genes .
thus , the final similarity is based on both gene content and average sequence similarity of genes , with the adjustment for gene length .
to obtain a similarity measure between any pair of genomes , we have compared all proteins using blastp ( 16 ) , with an e - value cut - off of e-10 , and used the  bit - score as the measure of similarities between two sequences .
the bit - score has the advantages of being independent of the searched database size .
moreover , it does not calibrate for protein length , thus , longer proteins have a greater impact than shorter ones .
to eliminate noise created by paralogy in the cases when multiple hits were observed , only the best hit was used ( i.e. the most significant sequence similarity ) .
the total number of sequence similarities thus obtained exceeds 25 million pairs for 153 genomes .
we will denote the sum of all best hits between genomes a and b as (a , b ) . the usage of best blastp hits , when compared with orthologs , abolishes the issue of ortholog identification , which is still an unresolved problem for datasets of this size .
however , it results in non - reciprocal genomic similarities , that is (a , b )  (b , a ) . to calculate the conservation between genome a and genome b , we used the minimum of the two values , i.e. min((a , b ) , (b , a ) ) . to normalize for differences in genome sizes , we calculated the genome conservation distance measure d in two ways ( d1 and d2 ) : d1 = 1-s / min((a , a ) , (b , b ) ) , or d2 = ln(s/(2 * (a , a ) * (b , b)/(((a , a ) ) + ( (b , b ) ) ) ) ) .
d1 ( 9 ) and d2 ( 7 ) correspond to strategies for the transformation and normalization of self - similarity and adjustment for genome sizes , proposed previously .
the tree discussed in the text was produced using d2 , for consistency with gene content trees , reported to be optimal with this normalization ( 7 ) .
gene content trees were generated as described elsewhere ( 7 ) , using identical data as for the genome conservation tree .
average ortholog similarity was computed as the pairwise score divided by the smallest number of hits between the genomes , divided by 1000 as a scaling factor , or (a , b)/(min(n(a , b ) , n(b , a ) ) * 1000 ) , where n(a , b ) is the number of hits between genomes a and b. these values were also normalized between 0 and 100 , for comparison with other measures ( figure 2 ) .
the values of pairwise distances were used to construct a distance matrix ; trees were calculated using quicktree ( 17 ) .
bootstrap values were generated by resampling the pool of alignment scores between pairs of genomes for 1000 times .
this procedure was not applied to the gene content tree , as it requires a jack knife approach rather than genuine bootstrapping ( 7 ) . in order to evaluate the conservation within taxonomic units
, we computed an average of the conservation values , while eliminating taxonomic over - representation . within each species , we averaged the pairwise conservation values of its strains . for higher taxonomic ranks ( e.g. genus , family ) , the conservation between each pair of its sub - ranking taxa was averaged , thereby avoiding bias of unequal taxonomic sampling of sequenced species .
ideally , a species distance metric should enable the reliable inference of phylogeny across variable evolutionary time scales and taxonomic ranges . to assess the performance of genome conservation
, we used standard neighbor - joining procedures ( 17,18 ) and produced a phylogenetic tree for 153 species with known genomes ( 19 ) . the obtained tree ( supplement 1 ) clusters all the major clades consistently with current taxonomic knowledge ( 20 ) and is similar to the gene content - based tree ( 7,9 ) , with a number of important exceptions , described below .
overall , compared to gene content phylogeny ( 7 ) , a previously proposed and widely accepted method for whole - genome - based phylogenetic reconstruction , genome conservation produces significantly improved results ( supplement 2 ) . in the genome conservation tree , the alpha- , gamma- , delta- and epsilon proteobacteria form highly supported clades and consistently form a monophyletic proteobacterial clade ( figure 1a ) .
beta proteobacteria form a clade inside gamma proteobacteria , and there are two inconsistencies with the accepted taxonomy . first , aquifex aeolicus is placed within proteobacteria .
second , the pseudomonas clade and nitrosomonas europaea seem to be flipped with respect to each other . both are coupled to tree nodes with low bootstrap values ( 686 and 585 out of 1000 , respectively ) . in comparison , the gene content method fails to produce a plausible phylogenetic reconstruction of proteobacteria ( figure 1b ) , or other taxa ( supplement 2 ) .
furthermore , the average ortholog similarity approach generates a plausible scenario for evolution of proteobacteria in the shallow branches of the tree , but fails to group both delta and epsilon subdivisions with other proteobacteria , defining them as separate deeply branching groups ( data not shown ) .
the recognition of abundance of horizontal gene transfer ( hgt ) among archaea and bacteria led to the questioning whether a reliable bacterial phylogeny can possibly be reconstructed ( 22 ) .
yet , the overall agreement of whole - genome based methods , such as genome conservation , average pairwise sequence similarity , gene content and 16s rrna phylogenies clearly demonstrates the existence of a consistent bacterial phylogeny ( 23 ) . in comparative genomics , it is crucial to accurately measure the evolutionary distance between organisms .
levels of conservation of 16s rrna sequence may not be sufficient to estimate the evolutionary distance and guarantee species identity , especially in the case of recently divergent organisms ( 24 ) .
presently , species distances are often estimated in millions of years of divergence ( 2527 ) . however , the time of divergence estimated from the 
molecular clock is extremely imprecise ( 28 ) and only organisms with fossil records can be dated with some accuracy ( 29 ) .
another popular form of estimating evolutionary distance is measuring the number of neutral substitutions per site .
this technique is appropriate for higher eukaryotes or closely related bacteria ; however , saturation in mutations hinders reliable estimations for highly divergent bacterial species ( 30 ) .
we propose the use of pairwise genome conservation metric as a stable whole - genome based evolutionary measurement to assess conservation between organisms .
a pictorial representation of the genome conservation matrix across all presently sequenced organisms readily demonstrates the ability of this species distance metric to define evolutionary relationships at variable taxonomic ranges , from strain variants up to the three domains of life ( figure 2a ; the complete set of values is available as supplement 3 ) .
we have compared the similarity matrices derived from the three principal genome - based phylogeny methods , namely genome conservation , gene content and average ortholog similarity . all matrices evidently contain a strong phylogenetic signal , represented both by the diagonal ( self - hits ) and various groupings of related taxa ( figure 2 ) .
all matrices are also able to clearly separate the three domains of life and delineate closely related groups .
these similarity matrices are transformed to distance matrices for the construction of phylogenetic trees ( see materials and methods ) , which produce different results .
massive gene loss in some intracellular parasites , such as buchnera and wolbachia , creates an effect where these species share their entire gene content with multiple , distantly related lineages .
the similarity estimated from gene content , normalized by the size of the minimal genome , fails to accurately estimate species distances ( figure 2b ) .
the genome conservation approach also suffers from the same effect , however on a significantly lower level ( figure 2a ) .
finally , average ortholog similarity ( figure 2c ) is independent of genome size , and thus resistant to the problem of drastically reduced genome sizes .
however , it is evident that genome conservation allows the detection of phylogenetic groupings at variable taxonomy ranges , from stains up to entire domains of life ( figure 2a ) .
despite the fact that some of these patterns are also present in the gene content and average ortholog similarity matrices ( figure 2b and c , respectively ) , their resolution is less pronounced , reflected by a blurred distribution of color - encoded similarity across taxa .
having demonstrated that the genome conservation metric reflects meaningful evolutionary relationships , we subsequently explored its ability to resolve long - standing arguments in defining the concept of bacterial taxa ( 31 ) . using genome conservation as a measure of evolutionary divergence
, we investigated how the levels of taxonomical classification in bacteria correspond to evolutionary distances ( figure 3 ) .
overall , there is a clear decrease in genome conservation at the higher taxonomic ranks .
however , the definition of some taxonomic units is not precise , and the ranges of genome conservation for various ranks often overlap ( figure 3 ) .
the other two measures , namely , gene content and average ortholog similarity , also exhibit a gradual reduction across increasing taxonomic distances , yet within a narrower value range , which renders them less effective in detecting taxonomic ranks ( data not shown ) .
it is worth noting that in all cases , the ranks of genus and family are the least well - defined , according to any genomic similarity measure . in particular , we found that the broadest distribution of genome conservation scores is observed within the genus rank .
for example , mycobacterium tuberculosis and m.bovis are 96% similar whereas mycoplasma gallisepticum , m.pneumoniae and m.penetrans are only 16% similar .
another example of questionable classification involves prochlorococcus marinus strains mit9313 and med4 , which present a challenging case for rrna - based taxonomy .
these two strains are 97% similar in their 16s rrna sequence ( 32 ) , while representing distinct genetic populations , with a 2-fold difference in genome size and content ( 33 ) , as well as specific phenotypic properties .
genome conservation between these strains is only 49% , which is more typical for distances between genera within a family , rather than strains within a species .
the genome conservation measurement of classification provides a possibility of a precise and quantitative definition of each taxonomic unit and a guide for future taxonomic classification . in summary ,
the genome conservation method uses a genomic perspective of gene content and couples it with sequence divergence at the whole - genome level . despite the limitation that an entire genome is required in order to place a species in its taxonomic context ,
this approach can delineate poorly resolved taxa and potentially be coupled with local rrna - based phylogenies . however , with the new approaches to whole - genome sequencing in environmental genomics ( 34 ) , the genome conservation approach can provide an unambiguous and consistent classification system for the newly discovered species .
the proposed species distance metric provides a clear measure based on sequence divergence for use in comparative genomics and taxonomy .
part of the complete tree of life containing the proteobacteria generated by genome conservation ( a ) and gene content ( b ) methods .
classes are color - coded , and the spirochaetum leptospira interrogans and deeply branching aquifex aeolicus are shown in black .
trees were generated using d2 normalization as described in materials and methods ; the complete tree is available in supplement 1 .
similarity matrices across all completely sequenced organisms , derived from genome conservation ( a ) , gene content ( b ) and average ortholog similarity ( c ) .
genome conservation and gene content were computed using d1 normalization ( see materials and methods ) .
species are ordered consistently across the different matrices , sorted according to their position on the genome conservation tree ( supplement 1 ) , and major clades are indicated in ( a ) .
the conservation levels in percentages are color - coded , and the values for individual pairwise scores for genome conservation are available ( supplement 3 ) .
it is evident that there are three fields of values , seen as lighter blue sub - matrices representing eukarya , archaea and bacteria , from top left to bottom right in ( a ) .
highly similar groups are evident , for instance escherichia coli strains ( red or yellow ) and enterobacteria ( green ) , both within -proteobacteria . for
see text for discussion , genome conservation computed using d1 normalization ( see materials and methods ) .
 species evolutionary relationships have traditionally been defined by sequence similarities of phylogenetic marker molecules , recently followed by whole - genome phylogenies based on gene order , average ortholog similarity or gene content . here 
 , we introduce genome conservation  a novel metric of evolutionary distances between species that simultaneously takes into account , both gene content and sequence similarity at the whole - genome level . 
 genome conservation represents a robust distance measure , as demonstrated by accurate phylogenetic reconstructions . 
 the genome conservation matrix for all presently sequenced organisms exhibits a remarkable ability to define evolutionary relationships across all taxonomic ranges . 
 an assessment of taxonomic ranks with genome conservation shows that certain ranks are inadequately described and raises the possibility for a more precise and quantitative taxonomy in the future . 
 all phylogenetic reconstructions are available at the genome phylogeny server : < > . 
INTRODUCTION
MATERIALS AND METHODS
RESULTS AND DISCUSSION
SUPPLEMENTARY MATERIAL
Supplementary Material
Figures and Tables