The net of life: reconstructing the microbial phylogenetic network.

Abstract

It has previously been suggested that the phylogeny of microbial species might be better described as a network containing vertical and horizontal gene transfer (HGT) events. Yet, all phylogenetic reconstructions so far have presented microbial trees rather than networks. Here, we present a first attempt to reconstruct such an evolutionary network, which we term the "net of life". We use available tree reconstruction methods to infer vertical inheritance, and use an ancestral state inference algorithm to map HGT events on the tree. We also describe a weighting scheme used to estimate the number of genes exchanged between pairs of organisms. We demonstrate that vertical inheritance constitutes the bulk of gene transfer on the tree of life. We term the bulk of horizontal gene flow between tree nodes as "vines", and demonstrate that multiple but mostly tiny vines interconnect the tree. Our results strongly suggest that the HGT network is a scale-free graph, a finding with important implications for genome evolution. We propose that genes might propagate extremely rapidly across microbial species through the HGT network, using certain organisms as hubs.

Full text

Page 1

The net of life: Reconstructing the microbial
phylogenetic network
Victor Kunin,1Leon Goldovsky, Nikos Darzentas, and Christos A. Ouzounis2
Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation,
Cambridge CB10 1SD, United Kingdom
It has previously been suggested that the phylogeny of microbial species might be better described as a network
containing vertical and horizontal gene transfer (HGT) events. Yet, all phylogenetic reconstructions so far have
presented microbial trees rather than networks. Here, we present a first attempt to reconstruct such an evolutionary
network, which we term the “net of life.” We use available tree reconstruction methods to infer vertical inheritance,
and use an ancestral state inference algorithm to map HGT events on the tree. We also describe a weighting scheme
used to estimate the number of genes exchanged between pairs of organisms. We demonstrate that vertical
inheritance constitutes the bulk of gene transfer on the tree of life. We term the bulk of horizontal gene flow
between tree nodes as “vines,” and demonstrate that multiple but mostly tiny vines interconnect the tree. Our
results strongly suggest that the HGT network is a scale-free graph, a finding with important implications for
genome evolution. We propose that genes might propagate extremely rapidly across microbial species through the
HGT network, using certain organisms as hubs.
[Supplemental material is available online at www.genome.org.]
Following the legacy of Darwin’s Origin of Species (Darwin 1859),
most current methods for phylogenetic reconstruction depict
evolutionary history of organisms as a tree. Phylogenetic trees
have been derived from compositional signatures (Fox et al.
1980), sequence alignments (Doolittle 1981), or alignments of
artificially concatenated conserved orthologs (Brown et al. 2001;
Rokas et al. 2003). With genome sequencing technology, meth-
ods based on complete genome sequences appeared, including
trees based on gene content (Fitz-Gibbon and House 1999; Snel et
al. 1999; Tekaia et al. 1999; Lin and Gerstein 2000; Korbel et al.
2002;), gene order (Korbel et al. 2002), average ortholog similar-
ity (Clarke et al. 2002), and genome conservation—a novel ge-
nome-based method combining gene content and sequence
similarity (Kunin et al. 2005).
All these tree-like representations of evolution have an in-
herent drawback, dealing solely with vertical inheritance (Bap-
teste et al. 2004). Yet, a well-established consensus between evo-
lutionary biologists is that the genomic history of most microbial
species is mosaic, with a significant amount of horizontal gene
transfer (HGT) present (Boucher et al. 2003). Although the quan-
tification of the evolutionary effect of the HGT is still a subject of
an ongoing debate (Snel et al. 2002; Kunin and Ouzounis 2003a),
its existence is not questioned. The strong influence of HGT led
to a proposal that presentation of microbial phylogeny as a tree
is inaccurate as instances of HGT are not recorded in this presen-
tation (Doolittle 1999; Martin 1999), and a correct representation
should reflect HGT events.
Attempts to deal with this issue include algorithmic solu-
tions for network-like tree reconstruction, mostly addressing re-
combination (but not HGT) as a form of nonvertical inheritance
(Wang et al. 2001; Gusfield et al. 2004), and topological analyses
of tree structure (Piel et al. 2003; Makarenkov and Legendre
2004). Thus, the widely accepted view that the phylogenetic his-
tory of genomes should be represented as a network rather than
a tree has not been realized yet.
Here we present a first attempt to reconstruct the history of
the microbial world, recording both horizontal and vertical gene
transfer. For a scaffold depicting vertical gene transfer we use
established tree reconstruction methods, on which we document
the instances of horizontal transfer that intertwine the tree. We
discuss the major properties of this complex phylogenetic net-
work based on a multitude of genome comparisons; demonstrate
its scale-free, small-world nature; and discuss the patterns of gene
propagation through the network.
Results
Data
To ensure that our results are not affected solely by the orthology
data (see Methods), we used two data sets: OFAM (see Methods)
and groups of orthologs defined by STRING (von Mering et al.
2003). Similarly, to avoid possible bias from a single tree recon-
struction method, we derived genomic trees with three indepen-
dent methods: gene content, average ortholog similarity, and
genome conservation (see Methods) for OFAM data and gene
content for STRING data.
The summary of the evolutionary events reconstructed with
each method is presented in Table 1. It is evident that although
HGT is readily detectable, the bulk of the genes are still trans-
ferred by vertical gene transfer, which is the most prevailing
mode of inheritance (Kunin and Ouzounis 2003a). In analogy,
the net of life is not a grid, where all edges are of a similar
strength, but more like a tree, with robust branching stems con-
nected by thin climbing vines.
1Present address: DOE Joint Genome Institute, Walnut Creek, Cali-
fornia 94598, USA.
2Corresponding author.
E-mail ouzounis@ebi.ac.uk; fax 44-1223-494471.
Article and publication are at http://www.genome.org/cgi/doi/10.1101/
gr.3666505. Article published online before print in June 2005.
Letter
954Genome Research
www.genome.org
15:954–959 ©2005 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/05; www.genome.org

Page 2

HGT vine width distribution
We define the HGT vine width as a summary of all horizontal
transfer events between two nodes on the tree, subsequently fix-
ated within the genome. The distribution of HGT vine widths, or
number of genes transferred between any two nodes on the tree,
is shown in Figure 1. All data sets and trees produce virtually
identical frequency distribution (Fig. 1), following a power law
(Table 2A), with the STRING data shifted by an order of magni-
tude, due to lower coverage of genomes (Table 1).
Connectivity of the network
To investigate the properties of the HGT network, we removed
the underlying (vertical inheritance) tree from the net of life.
Since our inference of HGT vine widths is probabilistic (see Meth-
ods), we had to select a meaningful threshold to depict the in-
ferred events. Thus, to investigate the connectivity of the HGT
network, we experimented with several thresholds, namely, one
(a single HGT), five, and 10. Irrespectively of the tree used and
data set, the HGT network displays small-world behavior, with
the diameter of the network fluctuating between five and six.
When higher thresholds are chosen for the analysis, the
network also demonstrates power law distribution of connectiv-
ity of nodes (Table 2B), once again irrespectively of the data set or
the tree used (Fig. 2). This power-law signal is obscured at the
lowest thresholds, where many nodes appear to have high con-
nectivity. We suggest that this deviation from the power law is a
result of noise inevitable when a probability model is examined
at low thresholds, namely, possibly containing more false-
positive instances. Our usage of thresholds higher than one for
evidence of HGT is indeed reinforced by biological observations
that genes often travel between organisms as groups rather than
singletons (Boucher et al. 2003). We thus conclude that the HGT
network is likely to have a power-law
distribution of connectivity, and thus be
scale-free.
HGT champions
We aimed at investigating the HGT net-
work in search of hubs and the widest
HGT vines. Unlike the global properties
of the network, which are virtually iden-
tical and independent on the data set,
the exact number of predicted gene
transfers between two nodes is highly
dependent on the tree structure. Incor-
rect tree architecture can cause the mis-
taken inference of high amounts of
HGT, particularly when two related or-
ganisms are positioned distantly on a
tree. We thus aimed to exclude tree ar-
chitecture bias from our analysis
and examined results consistent be-
tween different tree architectures.
Also, since the tree architectures are
different, inner nodes (i.e. ancestral
states) are often incomparable, and
thus we limited the analysis to the
leaves (terminal nodes) of the tree,
i.e., the sequenced genomes from
contemporary species.
When examining 165 microbial genomes for the network
hubs, certain species came out on the top of the connectivity list
with a remarkable consistency between the results obtained from
different trees and data sets (Table 3). We found Pirellula sp.,
Bradyrhizobium japonicum, and Erwinia carotovora always at the
top of the list of (terminal) nodes with the largest number of HGT
partners. Interestingly, the original genome report for Pirellula sp.
provides certain hints for HGT events in this species (Glockner et
al. 2003). Furthermore, there is evidence for HGT between B.
japonicum and E. carotovora in the literature (Streit et al. 2004). In
conclusion, these hubs can serve as bacterial “gene banks,” pro-
viding a medium to acquire and redistribute genes in the micro-
bial communities, caused either by specific genetic mechanisms
or by virtue of their close proximity to and interaction with other
species in their environmental niches.
We have also examined HGT vines that are reported to be
wide and consistent across data sets and trees. One of the widest
HGT vines is observed between the Bradyrhizobium genus (or
sometimes the broader Rhizobiales group) of Alpha Proteobacte-
ria and the Beta Proteobacterium Ralstonia solanacearum. Phylo-
genetically distant, both these species are soil bacteria, penetrat-
ing plant roots and forming—symbiotic in case of Bradyrhizobium
(Kiers et al. 2003) and parasitic in case of Ralstonia (Alfano and
Collmer 2004; Genin and Boucher 2004)—relationships with
plants. Both cause tumor-like structures, and possess complex
molecular mechanisms to interact with the host plants (Sawada
et al. 2003). Both bacteria are reported to have acquired large
number of genes horizontally (Kaneko et al. 2002; Salanoubat
et al. 2002). Careful analysis of the genes that are transferred
between the two bacteria can help to understand the mecha-
nisms of pathogen–host interactions in these species, as well in
other cases of HGT detected between species with similar life
styles.
Table 1.
Summary of settings and results from various experimental designs
Orthology
data Tree reconstruction method OrganismsHGT events Gene loss
Vertical
transfers
OFAM
OFAM
OFAM
STRING
Average ortholog similarity
Gene content
Genome conservation
Gene content
165
165
165
98
39,005
36,385
39,589
9968
88,834
89,951
84,630
32,943
640,328
646,791
635,056
288,225
Figure 1.
Distribution of HGT vine widths.
Phylogenetic network reconstruction
Genome Research
www.genome.org
955

Page 3

Discussion
The strongest limitation of the types of the network reconstruc-
tion presented here is the inability of the ancestral state inference
methods to precisely establish the donor organism for a HGT
event. Often a HGT event is inferred across nodes of the tree that
existed at different time periods. In this case, GeneTrace deter-
mines the donor group of organisms rather than a particular do-
nor species, and the prediction should be read as “the donor is a
progeny of the node.” Although this effect might influence the
character of the inferred network, the consistency between the
results of medium- and high-confidence HGT vine width thresh-
olds, as well any input data used in this study, indicates that the
properties of the phylogenetic network reported here are genuine
and realistic. A method to correctly infer HGT donors should
greatly improve reconstruction of the network.
The GeneTrace method applied here uses phylogenetic dis-
tribution as a marker of HGT events. However, HGT more often
occurs between related organisms, followed by homologous re-
combination (Vulic et al. 1997). Rather than introducing new
protein families into a genome, this type of HGT causes ortholo-
gous gene replacement. In this study, we did not address this
mechanism, we focus instead on events that introduce novel
protein families into genomes. We are currently working on in-
corporation of detecting homologous HGT events in the phylo-
genetic network.
Another limitation is our inability to determine the correct
path across organisms when multiple HGT events happened. Al-
though the probabilistic schema described in the Methods sec-
tion was designed to reduce the impact of this phenomenon,
identification of the exact order and direction of HGT events
would drastically improve reconstruction of the network.
The hubs of the HGT network presented here might partially
result from the phylogenetic coverage of the sequenced species.
When the coverage is low, multiple HGT events accumulate on
long branches, and an artificial “hub” might appear. Thus, the
reconstruction and understanding of the net of life will improve
with better phylogenetic representation of sequenced organisms.
The currently acceptable representation of phylogenetic
data is in the form of a tree-like structure in a two-dimensional
space, often referred to as a “dendrogram” (meaning tree-graph
in Greek). This presentation has the limitation of an inherent
inability to depict HGT events. We propose to represent the phy-
logenetic data in the form of a three-dimensional tree, where
beyond a tree drawn in the conventional two-dimensional space,
HGT vines require a third dimension. When convergence of gene
content is particularly high, participating nodes can be drawn
closer in the third dimension. An example of such drawing is
shown in Figure 3, with real data from this study. The full tree is
available in VRML format, including all species identifiers (Jans-
sen et al. 2003), as Supplemental material.
Our results suggest that the connectivity of microbial HGT
network has a power-law behavior; i.e., the connectivity distri-
bution appears as a decreasing straight line on a log-log scale (Fig.
2). A network in which connectivity of nodes distributes as a
power-law has also scale-free and small-world properties. Scale-
free networks display identical properties when any random sub-
set of the complete network is sampled, suggesting that our con-
clusions should not be strongly affected by an ever-increasing
number of genomes.
In a small-world network, the average shortest path between
any two of its nodes (termed “network diameter”) involves tra-
versing only relatively few nodes. This has a profound ecological
meaning and strong implications for genome evolution. In the
context of the HGT network, a small-world structure means that
a substantially beneficial gene appearing in any organism can
swing across species barriers and reach any other organism via a
very small number of HGT events. In fact, this prediction of our
hypothesis has an independent verification from the “experi-
ment” of antibiotics-resistance genes that are known to spread
extremely rapidly across species (Jacoby 1996), or the preferential
involvement of specific functional classes (Nakamura et al.
2004). Although most of the reported instances of drug resistance
involve pathogenic bacteria, based on the scale-free model, we
predict that the initial donor and final acceptor organisms might
have nothing in common in terms of phylogenetic origin, eco-
logical niche, or geographical distribution, and communicate in-
directly through the “hubs” in the network of life.
Methods
In order to reconstruct the phylogenetic network of microbial
species, we required a data set of orthologs across all currently
sequenced species. We used BLASTP (Altschul et al. 1997) to find
best bidirectional hits across 165 microbial genomes in COGENT
database release 184 (Janssen et al. 2003). To eliminate paralogy,
Figure 2.
Connectivity of the HGT network.
Table 2.
HGT vine widths (Fig. 1) and (B) the connectivity of the network
(Fig. 2), according to the four methods used
Parameters for the power-law distribution (b, k) for (A)
Method
y = a*xk; where a = exp(b) = eb
bKR2
A
Average ortholog similarity
Gene content
Genome conservation
STRING
11.8
11.8
11.7
9.9
?2.88
?2.93
?2.84
?2.68
0.95
0.96
0.94
0.95
B
Average ortholog similarity
Gene content
Genome conservation
STRING
6.7
7.4
5.8
6.0
?1.93
?2.25
?1.54
?2.55
0.68
0.77
0.72
0.83
Goodness-of-fit is expressed as the coefficient of determination (R2) de-
fined as R2= 1 ? SSE/SSM, where SSE is the sum of squared errors, and
SSM is the sum of squares around the mean.
Kunin et al.
956 Genome Research
www.genome.org

Page 4

we used only bidirectional best hits across genomes. We then
clustered these hits by using Markov clustering algorithm (MCL)
(Enright et al. 2002). The exhaustive nature of this schema en-
sures that all genes that had at least one bidirectional best hit in
another organism are represented (L. Goldovsky, P. Jenssen,
D. Ahrén, B. Audit, I. Cases, N. Darzentas, A.J. Enright, N. López-
Bigas, J.M. Peregrin-Alvarez, M. Smith, et al., in prep.). We call
the resulting protein families used for the analysis described
herein as the “OFAM” data set. This data set is accessible at
http://cgg.ebi.ac.uk/services/ortho-fam/.
To ensure that the results are not an artifact of the orthology
definition, we used orthology information for 110 species from
the STRING database (von Mering et
al. 2003), from which we cross-linked
106 species to COGENT, resulting in
98 prokaryotic species, after exclud-
ing Eukaryotes. STRING adopts the
definition of orthologs as groups of
homologs built from at least one trip-
let of best-matching pairs of se-
quences, also known as clusters of or-
thologous genes (COGs) (Tatusov et
al. 1997).
To reconstruct the microbial
phylogenetic network, we required a
phylogenetic tree. There are many
methods for the reconstruction of
phylogenetic trees (see Introduction); however, none guarantees
100% accuracy. To avoid biases generated by any single tree, we
used three methods of genome-based phylogenetic reconstruc-
tion, i.e., gene content (Korbel et al. 2002), average gene similar-
ity (Clarke et al. 2002), and genome conservation (Kunin et al.
2005). The first method derives phylogenetic distances from con-
servation of gene content, the second uses only sequence simi-
larity between genomes, and the third combines the two mea-
sures to achieve maximum precision and contrast (Kunin et al.
2005). While being based on complete genomes, all these meth-
ods produce phylogenies that are remarkably similar to the clas-
sical 16S rRNA trees. All methods are implemented as it appears
Table 3.
connectivity ranking in the three trees considered
The list of species representing the major hubs in the HGT network and their
Organism
Average
ortholog
similarity
Gene
content
Genome
conservationSTRING
Pirellula sp.
Bradyrhizobium japonicum
Erwinia carotovora
Clostridium acetobutylicum
Chromobacterium violaceum
2
3
5
4
6
1
3
2
4
1
2
4
Absent
4
Absent
5
Absent
10
9 10
(HGT vine width threshold is set to 10; see Methods). Inner nodes of the tree are ignored during the
ranking. Absent signifies absence of the organism in the input data.
Figure 3.
Methods). The root is represented as a yellow sphere. Bacteria are shown as nodes on cyan branches; Archaea, as nodes on green branches. Red lines
correspond to the vines representing HGT. The radius of the nodes is proportional to the estimated gene content size (in terms of number of gene
families). Also, the widths of both the vertical inheritance branches and the horizontal inheritance vines correspond to the numbers of gene families
transferred by either mechanism. For visualization purposes, only values for HGT vine width >30 are shown. Certain key species and taxa are labeled;
for full names, please refer to Supplemental material.
Three-dimensional representation of the net of life. The tree backbone was generated by using the average gene similarity approach (see
Phylogenetic network reconstruction
Genome Research
www.genome.org
957

Page 5

on the Genome Phylogeny Server (http://cgg.ebi.ac.uk/cgi-bin/
gps/GPS.pl) and described elsewhere (Kunin et al. 2005). Only
results consistent across different trees and with consistently
high jacknife scores (Kunin and Ouzounis 2003b) are considered
robust. For STRING data, we used a gene content tree constructed
according to (Korbel et al. 2002).
Just as there are many methods to reconstruct phylogenetic
trees, there are several available methods to identify HGT events.
We could not use methods that are based on identification of
biased GC content or codon usage, as these can only identify
recently acquired genes and are not designed to reconstruct early
events. We thus used GeneTrace—a method that identifies HGT
from the phylogenetic distribution of protein families on the tree
of life (Kunin and Ouzounis 2003b). GeneTrace assumes that
presence of a gene family in multiple members of a clade reveals
its ancestral nature, absence of a gene in some members of a clade
indicates gene loss, and patchy presence of the gene family in
distantly related clades implies HGT. This method was shown to
have at least 90% accuracy on simulated data (Kunin and Ouzou-
nis 2003b) and at least 81% accuracy on biological data (Kunin
and Ouzounis 2003a), being capable of reconstructing HGT
events on most levels on the tree of life.
A limitation of the GeneTrace approach to reconstructing
HGT events is its inability to distinguish between the donor and
the acceptor genomes (Kunin and Ouzounis 2003b). Thus, a gene
that was extensively transferred horizontally creates links be-
tween all lineages that possess the gene, regardless whether they
were involved in the particular transfer or not. We thus adopted
a schema for normalization of the number of transferred genes,
to avoid multiple counts of a single HGT event, as below.
Consider a situation when a protein family appears twice in
distant sections of a tree. In this case, at least one HGT event may
be necessary to explain the phylogenetic distribution of the fam-
ily. Consider now a protein family that has three dispersed roots
within a tree. Then, at least two horizontal transfer events are
necessary to explain the distribution. However, simple linking of
all nodes creates three possible edges for horizontal transfer. As-
suming equal probability for all possible scenarios, we then as-
sign the value of 2/3 as a probability for each possible event to be
depicted correctly (and 1/3 for an incorrect detection). Thus,
while the minimal number of edges required to connect all nodes
(n) by HGT is n ? 1, the number of all possible connections is
n(n ? 1)/2. This gives us the probability that each of the edges
describes a valid HGT event as (n ? 1)/(n(n ? 1)/2), or 2/n. Thus,
to each node that connects independent origins of a protein
family, previously labeled by GeneTrace as arising from HGT, we
assign a probability of 2/n.
To describe the inferred sum of all HGT events between two
nodes within an evolutionary net, we sum up all probabilities of
transfer for each gene family transferred between the two nodes
and term the resulting edge as “vine” and the weight of the edge
as “vine width.”
Acknowledgments
We thank members of the Computational Genomics Group for
useful discussions. CAO acknowledges support from the UK
Medical Research Council and IBM Research.
References
Alfano, J.R. and Collmer, A. 2004. Type III secretion system effector
proteins: Double agents in bacterial disease and plant defense. Annu.
Rev. Phytopathol. 42: 385–414.
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller,
W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new
generation of protein database search programs. Nucleic Acids Res.
25: 3389–3402.
Bapteste, E., Boucher, Y., Leigh, J., and Doolittle, W.F. 2004.
Phylogenetic reconstruction and lateral gene transfer. Trends
Microbiol. 12: 406–411.
Boucher, Y., Douady, C.J., Papke, R.T., Walsh, D.A., Boudreau, M.E.,
Nesbo, C.L., Case, R.J., and Doolittle, W.F. 2003. Lateral gene
transfer and the origins of prokaryotic groups. Annu. Rev. Genet.
37: 283–328.
Brown, J.R., Douady, C.J., Italia, M.J., Marshall, W.E., and Stanhope,
M.J. 2001. Universal trees based on large combined protein sequence
data sets. Nat. Genet. 28: 281–285.
Clarke, G.D., Beiko, R.G., Ragan, M.A., and Charlebois, R.L. 2002.
Inferring genome trees by using a filter to eliminate phylogenetically
discordant sequences and a distance matrix based on mean
normalized BLASTP scores. J. Bacteriol. 184: 2072–2080.
Darwin, C. 1859. On the origin of species by means of natural selection. J.
Murray, London.
Doolittle, R.F. 1981. Similar amino acid sequences: Chance or common
ancestry? Science 214: 149–159.
———. 1999. Phylogenetic classification and the universal tree. Science
284: 2124–2129.
Enright, A.J., Van Dongen, S., and Ouzounis, C.A. 2002. An efficient
algorithm for large-scale detection of protein families. Nucleic Acids
Res. 30: 1575–1584.
Fitz-Gibbon, S.T. and House, C.H. 1999. Whole genome-based
phylogenetic analysis of free-living microorganisms. Nucleic Acids
Res. 27: 4218–4222.
Fox, G.E., Stackebrandt, E., Hespell, R.B., Gibson, J., Maniloff, J., Dyer,
T.A., Wolfe, R.S., Balch, W.E., Tanner, R.S., Magrum, L.J., et al. 1980.
The phylogeny of prokaryotes. Science 209: 457–463.
Genin, S. and Boucher, C. 2004. Lessons learned from the genome
analysis of Ralstonia solanacearum. Annu. Rev. Phytopathol.
42: 107–134.
Glockner, F.O., Kube, M., Bauer, M., Teeling, H., Lombardot, T., Ludwig,
W., Gade, D., Beck, A., Borzym, K., Heitmann, K., et al. 2003.
Complete genome sequence of the marine planctomycete Pirellula
sp. strain 1. Proc. Natl. Acad. Sci. 100: 8298–8303.
Gusfield, D., Eddhu, S., and Langley, C. 2004. Optimal, efficient
reconstruction of phylogenetic networks with constrained
recombination. J. Bioinform. Comput. Biol. 2: 173–213.
Jacoby, G.A. 1996. Antimicrobial-resistant pathogens in the 1990s.
Annu. Rev. Med. 47: 169–179.
Janssen, P., Enright, A.J., Audit, B., Cases, I., Goldovsky, L., Harte, N.,
Kunin, V., and Ouzounis, C.A. 2003. COmplete GENome Tracking
(COGENT): A flexible data environment for computational
genomics. Bioinformatics 19: 1451–1452.
Kaneko, T., Nakamura, Y., Sato, S., Minamisawa, K., Uchiumi, T.,
Sasamoto, S., Watanabe, A., Idesawa, K., Iriguchi, M., Kawashima, K.,
et al. 2002. Complete genomic sequence of nitrogen-fixing
symbiotic bacterium Bradyrhizobium japonicum USDA110. DNA Res.
9: 189–197.
Kiers, E.T., Rousseau, R.A., West, S.A., and Denison, R.F. 2003. Host
sanctions and the legume-rhizobium mutualism. Nature 425: 78–
81.
Korbel, J.O., Snel, B., Huynen, M.A., and Bork, P. 2002. SHOT: A web
server for the construction of genome phylogenies. Trends Genet.
18: 158–162.
Kunin, V. and Ouzounis, C.A. 2003a. The balance of driving forces
during genome evolution in prokaryotes. Genome Res.
13: 1589–1594.
———. 2003b. GeneTRACE: Reconstruction of gene content of ancestral
species. Bioinformatics 19: 1412–1416.
Kunin, V., Ahren, D., Goldovsky, L., Janssen, P., and Ouzounis, C.A.
2005. Measuring genome conservation across taxa: Divided strains
and united kingdoms. Nucleic Acids Res. 33: 616–621.
Lin, J. and Gerstein, M. 2000. Whole-genome trees based on the
occurrence of folds and orthologs: Implications for comparing
genomes on different levels. Genome Res. 10: 808–818.
Makarenkov, V. and Legendre, P. 2004. From a phylogenetic tree to a
reticulated network. J. Comput. Biol. 11: 195–212.
Martin, W. 1999. Mosaic bacterial chromosomes: A challenge en route
to a tree of genomes. Bioessays 21: 99–104.
Nakamura, Y., Itoh, T., Matsuda, H., and Gojobori, T. 2004. Biased
biological functions of horizontally transferred genes in prokaryotic
genomes. Nat. Genet. 36: 760–766.
Piel, W.H., Sanderson, M.J., and Donoghue, M.J. 2003. The small-world
dynamics of tree networks and data mining in phyloinformatics.
Bioinformatics 19: 1162–1168.
Kunin et al.
958Genome Research
www.genome.org