The tree of genomes: An empirical comparison of genome-phylogeny reconstruction methods

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BMC Evolutionary Biology
Open Access
Research article
The tree of genomes: An empirical comparison of
genome-phylogeny reconstruction methods
Angela McCann, James A Cotton and James O McInerney*
Address: Bioinformatics laboratory, Department of Biology, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland
Email: Angela McCann - angela.mccann@nuim.ie; James A Cotton - j.a.cotton@qmul.ac.uk; James O McInerney* - james.o.mcinerney@nuim.ie
* Corresponding author
Abstract
Background: In the past decade or more, the emphasis for reconstructing species phylogenies has
moved from the analysis of a single gene to the analysis of multiple genes and even completed
genomes. The simplest method of scaling up is to use familiar analysis methods on a larger scale
and this is the most popular approach. However, duplications and losses of genes along with
horizontal gene transfer (HGT) can lead to a situation where there is only an indirect relationship
between gene and genome phylogenies. In this study we examine five widely-used approaches and
their variants to see if indeed they are more-or-less saying the same thing. In particular, we focus
on Conditioned Reconstruction as it is a method that is designed to work well even if HGT is
present.
Results: We confirm a previous suggestion that this method has a systematic bias. We show that
no two methods produce the same results and most current methods of inferring genome
phylogenies produce results that are significantly different to other methods.
Conclusion: We conclude that genome phylogenies need to be interpreted differently, depending
on the method used to construct them.
Background
Hundreds of genome sequencing projects have been com-
pleted [1], providing us with an abundant source of data
to reconstruct phylogenetic relationships, but also with
some novel problems in interpreting these data. The evo-
lutionary history of any genome includes elements of
gene duplication, gene loss, lineage sorting and horizon-
tal transfer of genes, all of which have the ability to con-
found phylogeny reconstruction [2-4]. Against this
background, a variety of genome-phylogeny methods
have been developed. These vary in their approach, the
input data they require and the interpretation of the
result. However, to date, no study has been carried out
that asks whether these methods are picking out funda-
mentally different signals or if they are more-or-less find-
ing the same tree.
Current genome-level phylogeny methods can be split
into two categories – sequence-based methods and gene-
content methods. Analyses of sequence evolution pre-
dates gene-content methods simply because data for indi-
vidual genes were available before data for completed
genomes. Ubiquitously distributed ribosomal RNA
(rRNA) genes have usually been used as surrogates for
larger samples of individual genomes. These particular
genes are popular for phylogenetic studies due to their
plentitude, universally conserved structure and apparent
resistance to horizontal gene transfer (HGT) [5]. In con-
Published: 12 November 2008
BMC Evolutionary Biology 2008, 8:312 doi:10.1186/1471-2148-8-312
Received: 7 April 2008
Accepted: 12 November 2008
This article is available from: http://www.biomedcentral.com/1471-2148/8/312
© 2008 McCann et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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trast, some methods are designed to include information
from the evolutionary history of several individual genes.
The supertree approach, for instance, involves the creation
of individual trees from gene families and the amalgama-
tion of these into one final supertree. Another sequence-
based approach has involved the concatenation of align-
ments of several genes [6-8] usually with an effort being
made to remove sequences that have an obvious history of
HGT. Data concatenation should have the effect of mini-
mizing stochastic effects due to small sample size and
amplifying low signals, though gene concatenation is not
without its problems [8,9].
The second group of methods uses variation in gene-con-
tent as the basis for phylogeny reconstruction. These
approaches range from the use of similarity of gene-con-
tent [10-12] to the inclusion of the analysis of gene order
data [13]. Usually a pairwise analysis of genomes in a set
is carried out with a metric being computed that reflects
the similarity between the genomes, this can be done
using a maximum parsimony score, a threshold parsi-
mony score (e.g. [14]) or deriving a phylogenetic distance.
Finally phylogenetic hypotheses are generated based
upon these scores.
If the process of evolution is indeed hierarchical or tree-
like, then with increased sampling, all reasonable or con-
sistent methods should converge on the same tree. Recent
work has found a great deal of congruence between phyl-
ogenetic trees for different gene families in closely related
organisms but a lack of congruence between gene trees
from distant relatives [15,16]. This suggests that the pat-
tern of inheritance of genes may indeed be largely vertical,
or at least tree-like for parts of the reconstructable tree, but
that this pattern is difficult to identify for deep-level rela-
tionships [17]. In other words, parts of life's history may
not be reconstructable because of incorrect identification
of orthologs, hidden paralogy, horizontal gene transfer
events or the inability of methods based upon current
evolutionary models to correctly reconstruct deep-level
phylogenetic relationships. Ideally, a formal probabilistic
model describing all of the many processes involved
would allow us to both study these processes quantita-
tively and reconstruct phylogenetic relationships [18], but
no such unifying models exist, and any such model would
be complex and difficult to fit.
Given that a number of heuristic methods now exist for
the inference of phylogenetic histories from genomic
data, it is reasonable to ask whether these methods are
likely to give fundamentally different answers. In this
report, we have examined the similarity of the results we
obtain when we use a variety of different organismal phy-
logeny reconstruction approaches on a real dataset. In
reality, given the enormous number of genome-phylog-
eny methods, we have not tried to exhaustively explore all
available methods. Instead, we have chosen exemplar
methods that use different kinds of data. If all genome-
phylogeny methods tend to return the same answers, then
it probably does not matter which one is used; however, if
on the other hand, different methods give different
results, the choice of method becomes important.
We have used exploratory statistics in order to examine
the phylogeny of 22 diverse Archaea for which completed
genome sequences are available. In particular, we wished
to explore variation in the phylogenetic hypotheses from
this dataset. Our comparisons involved using five distinct
phylogeny reconstruction methods and their variants, giv-
ing a total of nine methods. Seven of these methods use
large portions of each genome: four variants of the Condi-
tioned Reconstruction (CR) method [11,19], two variants
of the SHOT method [20] and a Supertree approach [21].
We have also examined reconstructions based on the 16S
rRNA molecule and from a concatenated alignment of 31
genes involved in translation (the same genes used by
[7]).
Particular attention is paid to CR in this report [11,19].
This is a method that has not been used extensively but
which forms the basis of the "Ring of Life" hypothesis
[11,19]. CR is based on the analysis of shared gene-con-
tent, with closely related species sharing a large propor-
tion of genes whilst more distantly related species have
fewer genes in common. The method is based on the for-
mation of a matrix consisting of the four possible patterns
of joint presence (P) or absence (A) of genes between any
two genomes (PP, PA, AP, AA). The proportions of the
first three are readily determined, but the shared absences
will pose a problem for any Markov method using pres-
ence-absence data to infer phylogenetic signal. This is due
to the fact that the analysis will only be carried out over all
genes present in either genome one or genome two. The
authors' solution is to introduce a conditioning genome;
i.e. an additional genome that is used solely for reference
purposes. Genes that are present in the conditioning
genome, but absent in the two genomes under considera-
tion provide an estimate of shared absence.
Along with their claim about insensitivity to HGT the
authors also claim that the phylogenetic outcome of CR is
not affected by the choice of conditioning genome
[11,19]. However, there has been little work testing either
of these conjectures, or examining the performance of CR
in comparison to other methods [22]. An analysis was
however preformed to determine if CR could differentiate
between genome fusions or HGT events involved in the
formation of mixed genomes [22], as the authors claimed
it could. Bailey et al (2006) concluded that this was not
possible and that CR can actually induce bias in ortholog

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sampling; they also showed using simulation studies that
different conditioning genomes can result in different
trees being derived for the same dataset. Another study of
CR carried out by Spencer et al (2007), also showed that
altering the conditioning genome in an analysis can affect
bootstrap support for different tree topologies.
A problem that we encounter in this study concerns the
issue of using different analysis methods and using differ-
ent kinds of data. When we consider a single alignment,
then it is usual to perform an initial analysis to find which
model best fits the data [23,24]. Then using this model,
the best phylogenetic tree or set of trees is found using
some optimization procedure [25]. Therefore, the two
variables will be the choice of alignment – whether to use
a single gene [26,27] or concatenate several genes together
[6-8] – and the choice of model. When using gene-content
data, the choices will include the method of encoding
gene-content and the way in which the encoded data is
analysed [10-12]. For an analysis that includes phyloge-
netic supertree construction, the choices will center on the
model that is used to construct phylogenetic trees from
the alignments of orthologs and the method of inferring
the supertree [28]. Clearly, it is difficult to carry out a
study that uses all combinations of methods and also it is
difficult to use an approach that holds all variables con-
stant while only changing one at a time. In this study, we
have chosen to use a representative sample of approaches
and our analysis involved comparing the final sets of phy-
logenetic hypotheses. The overall objective of this work is
not to identify which method yields the correct phylog-
eny, rather it is to ask whether the different methods are,
in general, producing the same phylogenies.
Methods
Ortholog identification
A total of 22 fully sequenced Archaeal genomes were
downloaded from the Cogent database [1]. A previously
described greedy algorithm was used to identify ortholo-
gous families in these genomes [16]. Briefly, a random
query sequence was chosen from the original database of
22 Archaeal genomes and homologous genes were identi-
fied as BLASTP hits [29] with an E-value of 10-8 or less. The
initial query sequence and all hits were then removed
from the original Archaeal database and the process con-
tinued iteratively with a new query until all genes were
assigned to a gene family. A total of 14,673 gene families
were identified. From this initial set, 1,655 paralogous
families were eliminated by removing all gene families
with more than one representative from any genome.
11,864 phylogenetically uninformative gene families
(fewer than four sequences) were also eliminated. Amino-
acid sequences for each gene family were then aligned
using ClustalW v1.83 [30] with all settings at their default
values. Gblocks [31] was then used to remove highly var-
iable positions from the alignment, these are potentially
fast evolving or poorly aligned regions. In Gblocks [31],
the maximum number of contiguous non-conserved posi-
tions allowed was set to 15 and the minimum length of a
block was set to 8 amino acid positions. Following
Gblocks site removal, those alignments that now had
fewer than 150 amino acid positions remaining were
excluded from further analysis. All remaining alignments
were screened for the presence of phylogenetic signal
using the Permutation Tail Probability (PTP) test [32,33].
Only 594 alignments passed the test (p < 0.01) and were
retained. The presence or absence of these remaining sin-
gle-copy gene families were scored in a matrix and pro-
vided the input for the gene-content based phylogenetic
methods.
Supertree
The 594 remaining alignments were analysed using Mul-
tiphyl [23]. This software reconstructs maximum-likeli-
hood (ML) phylogenies for each gene family using the
best-fitting empirical homogeneous model of amino-acid
substitution, according to the Akaike Information Crite-
rion (AIC). Multiphyl [23] distributes the model selection
and tree search calculations across a network of comput-
ers. Tree space was searched using Nearest Neighbor Inter-
change (NNI) branch swapping and local branch length
optimization, until convergence. Following gene tree esti-
mation, a supertree was inferred from these gene trees
using CLANN [21] with the Most Similar Supertree Algo-
rithm (MSSA/DFIT) criterion [15], using the default heu-
ristic search options. Non-parametric bootstrapping was
carried out by sampling-with-replacement 100 pseudore-
plicates of individual gene trees, using the default settings
in CLANN [21], generating a supertree for each of these
replicates and summarizing the results using a majority-
rule consensus method.
Conditioned Reconstruction
A matrix of presence and absence of gene families (see
ortholog identification above) was analysed using a Java
implementation of the CR algorithm [11,19], (software
available on request). Our program implements a number
of variants of the standard CR method. The first approach
uses only a single conditioning genome, as originally pro-
posed by the authors [11,19]. The conditioning genome is
specified by the user and a phylogeny is inferred using
paralinear/logdet distances [34,35]. The first variant of CR
is called averaged (Avg) CR and does not require the a pri-
ori identification of a conditioning genome. In this case,
every genome is used as the conditioning genome. In
other words, when working out the distance between two
genomes, every other genome acts the conditioning
genome. The logdet distances derived using each condi-
tioning genome are summed and the mean of this value
gives the final distance between the two genomes of inter-

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est, this process is repeated for all pairs of genomes in the
analyses. The second variant is an unconditioned distance
approach (see [36]), this involves including a pseudo-
conditioning genome in which every gene family is
present (i.e. comprised entirely of the present state).
The final variant of CR analysed in this report, employed
software created by Spencer et al., (2007). This program is
based on a modified BIONJ algorithm [37], adapted to
produce a supertree. The input to this program is a series
of distance matrices, each derived using a different condi-
tioning genome; in our case 22 different matrices were
used, therefore all genomes in this analysis acted as the
conditioning genome at one point. The modified BIONJ
algorithm operates by firstly choosing a pair of taxa from
each distance matrix that minimizes some criterion (see
[36]). The best such pair across all the distance matrices
are then selected and the subtrees containing these taxa
are aggregated in all distance matrices; finally the distance
matrices are updated. This process is continued iteratively
until every taxon has been aggregated in every matrix and
a supertree is produced. Spencer et al., (2007) provide two
different approaches of the algorithm, both of which were
used in this report. The first method is a vote-counting
method that does not take into account differences in reli-
ability between conditioning genomes. The second is an
inverse-variance weighting scheme that does take into
account differences in reliability between conditioning
genomes.
In total four variants of CR are implemented in this study.
With the exception of the modified BIONJ approach
described above, all distance matrices were converted into
phylogenetic trees using the neighbor-joining algorithm
implemented in PHYLIP [38]. In addition, we constructed
100 bootstrap pseudoreplicates by resampling the origi-
nal presence and absence matrix (see ortholog identifica-
tion above).
SHOT method
Two distance matrices were derived from the matrix of all
orthologs (see ortholog identification) based on two vari-
ants of the SHOT method [20], by applying the formulae
below:
npp is the number of gene families in common between the
two genomes of interest and a and b are the number of
gene families in each of the two genomes individually.
Following derivation of a distance matrix, phylogenetic
hypotheses were derived using the neighbor-joining
method as described above. Bootstrap resampling was
employed in order to examine variation in estimates from
these approaches.
Concatenated alignment
A concatenated alignment was built using the 31 genes
used by Cicarelli et al., (2006; see table S2). These genes
are largely involved in translation and have been
described as having "indisputable orthology" in 191 spe-
cies. The complete data matrix was obtained from the
iTOL website [39] and all non-archaeal species were
removed. Four genomes used in our study were absent
from the Ciccarelli et al (2006) data set. The genes from
these genomes were retrieved and aligned to the iTOL
genes as a profile alignment in ClustalW v1.83 [30]. Phy-
logenetic hypotheses based on this alignment were then
generated using Multiphyl [23] using the homogeneous
(unpartitioned) model selection, tree reconstruction and
bootstrap resampling capabilities of MultiPhyl.
Ribosomal RNA Tree
16S rRNA sequences were obtained from the Ribosomal
Database Project (RDP, [40]) or, when particular
sequences were not available in the RDP, they were
retrieved from GenBank (see table S1). All downloaded
16S rRNA genes were compared to the corresponding
genes in our downloaded genomes to ensure the correct
genes had been retrieved. The RDP alignment was used as
a profile to align GenBank sequences using ClustalW
v1.83 [30]. According to the AIC implemented in Model-
test [24], the best-fitting model of nucleotide substitution
was the General Time Reversible (GTR) substitution
model, with rates at variable sites sampled from a gamma
distribution. Phylogeny reconstruction was carried out
using the default TBR heuristic search in PAUP 4b10 [41].
Bootstrap resampling was also carried out using PAUP
4b10.
Comparing trees and matrices
Pairwise Robinson-Foulds (RF) distances (symmetric-dif-
ference distances) [42] between trees were calculated
using PAUP 4b10 [41]. Phylogenetic trees were visualized
using TreeView [43] and TreeMap 2.0β [44] (see Addi-
tional file 1). Comparisons between distance matrices
produced using the gene-content methods were per-
formed by calculating a sum-of-squares distance. The
matrices were transformed so that undefined values from
the CR procedure were replaced with the largest value in
the matrix. The resulting distance matrices could then be
visualised using Principle Components Analysis (PCA) in
the R statistical programming language (R Development
Core Team, 2006).
dn
a
ab
b
2
pp
1
22
= −
+
log()
(1)
d
npp
a b
2= −log(min( , ))
(2)

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Results & discussion
Our objective was to explore a variety of exemplar analysis
methods from each of the different categories of analysis
type in order to ascertain whether variation in the result-
ing trees is trivial or extensive, random or accompanied by
systematic bias. In the first instance, we used exploratory
statistics to examine variation in the distance matrices
produced by those methods that produced distance matri-
ces. We focused on the CR approach and specifically, the
effects that are seen with variation in the choice of condi-
tioning genome.
Variation within Conditioned Reconstruction approaches
In the analysis of the CR approach it became obvious that
the distance matrix that was recovered was very dependent
on the conditioning genome that was used in the analysis.
We inferred CR distance matrices using all possible (a
total of 22) combinations of conditioning genomes.
Another distance matrix was derived by taking a pair of
genomes and calculating the distance between them using
every other genome as a conditioning genome and then
averaging these distances (Avg CR). Another distance
matrix was produced using a synthetic conditioning
genome where every gene family was present (uncondi-
tioned approach). Two final matrices were produced
using the SHOT formulae in equations 1 and 2 above. We
used PCA in order to visualize the most important sources
of variation across the CR distance matrices as well as the
two matrices from the SHOT methods. Figure 1a shows
the most important axes following PCA of the distance
matrices. Each point on the diagram represents the loca-
tion of a distance matrix, with the relative closeness of
points to one another being indicative of the relative sim-
ilarities of the distance matrices. Beside each point is the
name of the conditioning genome that was used in order
to produce the matrix. In the case of the SHOT methods
or the various CR alternatives, these are labeled appropri-
ately. In this plot the points are proportional in size to the
size of the conditioning genome and the colours of the
shaded points are darker for Crenarchaeotes and lighter
for Euryarchaeotes.
The two axes in this plot account for 74% of the total
amount of variation in the PCA. The first axis (the
abscissa) accounts for 52% of the variation and the sec-
ond axis (the ordinate) accounts for 22% of the total var-
iation. No other axis accounted for more than 6% of the
variation, therefore these two axes are by far the most
important correlates with variation in the distance matri-
ces.
Firstly, an analysis of this plot shows that the same dis-
tance matrix is not produced every time and that the cal-
culated distances heavily depend on the conditioning
genome or the data treatment that is used. The most
important trend in these data matrices (axis 1) is corre-
lated with choice of conditioning genome. When condi-
tioning genomes are used that are closely related, then the
resulting distance matrices will tend to be closely related.
For example, the distance matrices produced using the
four Thermococci (see table S3 for classification) as the
conditioning genomes are clustered together. A similar
within-group clustering is observed when, say, the Ther-
moplasmatales, the Crenarchaeota or the Methanogens
act as the conditioning genome. When Archaeoglobus fulg-
idus was used as the conditioning genome the resulting
distance matrix also clustered with the Methanogens. Arc.
fulgidus is a sulphur metabolising archaeon with similar
biochemistry to the methanogens [45] so therefore, this
placement is also perhaps not surprising. Therefore, tak-
ing an overall look at the results of using different condi-
tioning genomes, we can see that phylogenetic position is
the most important factor in inducing differences in the
distance matrices.
The outliers on axis 1 in this plot are the matrices where
the four Thermococci were used as conditioning genomes.
These outliers account for much of the variation in axis 1.
There are two things that can be said about these distance
matrices. Firstly, three of these four matrices contained the
highest proportion of undefined values in our analyses.
Undefined values occur when attempting to perform an
operation on invalid operands, e.g. getting the logarithm
of a negative number. When one Thermococcus is chosen
as the conditioning genome the distances between the
other Thermococci and the rest of the genomes contain a
high proportion of undefined values. This point is backed
up by the claim [36] that a conditioning genome far from
the taxon of interest is optimal. This may be the reason
that these are outliers. Another possible reason is that
these are close relatives so perhaps they are outliers
because quite simply these four conditioning genomes
have produced matrices that are similar to one another
but very different to the rest of the conditioning genomes
and the fact that they have large numbers of undefined
values is purely incidental.
The second most important axis (axis 2) clearly defines
the split between the Crenarchaeota and Euryarchaeota.
So, even the second most important source of variation in
the data is also related to phylogenetic affiliations of the
conditioning genomes.
In order to explore whether these matrices are signifi-
cantly different to one another we used bootstrap resam-
pling of the data. For each dataset, we produced 100
bootstrap samples and 100 corresponding distance matri-
ces. We expected to find one of two situations. Either the
variation within 100 bootstrap replicates is so great that
there is no correlation between matrices produced using a

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1a displays a PCA of the variation between the distance matrices
Figure 1
1a displays a PCA of the variation between the distance matrices. Each point is named after either the phylogenetic
method used to create the matrix or in the case of CR the conditioning genome used. The size of the points is proportional to
the size of the conditioning genome used. Figure 1b depicts PCA analysis of the 100 bootstrap replicates from each of the
gene-content methods and the 22 variants of CR using one genome. Finally 1c displays a matrix produced using Methanosarcina
acetivorans as the conditioning genome. The columns representing Methanococcus jannaschii and Methanosarcina mazei in the dis-
tance matrixhave been plotted. The dots on the plot represent the distance between the labeled genome and Mco. jannaschii
(plotted on the x axis) and Methanosarcina mazei (plotted on the y-axis).
0.0
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Unconditioned
50
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specific conditioning genome, or that the bootstrap repli-
cates for a particular method would cluster together.
From figure 1b we can see that bootstrap samples tend to
result in distance matrices that are generally similar to the
matrices produced from the original data. Three clusters
are observed in the bootstrapped data, one grouping com-
prises the Crenarchaeota, another consists of the Metha-
nogens and the third contains the Thermococci. This
means that the distance matrices derived from different
treatments of the data are generally different but are clus-
tered based on phylogenetic position. We interpret this to
be significant support for a systematic bias as previously
observed in two other studies [22,36].
Next, we analysed the phylogenetic trees that can be con-
structed from each of the different approaches. In this sec-
tion, we present an exemplar analysis of what we see when
we use one conditioning genome, when we combine
information from different distance matrices to build a
supertree [36] and also we present the results of compari-
sons of the different phylogenetic methods.
Effect the Conditioning Genome has on its Closest
Relatives
One recurring feature of phylograms that are constructed
from matrices produced using the CR approach is the
presence of a few long branches in the analysis (see sup-
plementary material). A closer inspection of this phenom-
enon showed that the tree tips with unusual branch
lengths corresponded usually with those taxa that are
known to be close relatives of the conditioning genome.
This is a systematic bias in CR [22,36]. When a phylogram
was constructed using Pyrococcus abyssi as the conditioning
genome (supplementary information), the three remain-
ing Thermococci had elongated branch lengths in particu-
lar P. horikoshii. P. horikoshii is considered to be the closest
relative to P. abyssi in the taxon sampling we have used. It
has been reported that the two genomes share 1,122 kb in
common and a similar chromosomal organisation, with
an average amino acid identity of 77% [46]. The branch
leading to P. horikoshii was 1.17 whilst the average branch
length across the tree was 0.33 (see supplementary infor-
mation, table S4 for branch lengths using other condition-
ing genomes).
Figure 1c is a plot of the distances from a matrix produced
using Methanosarcina acetivorans as the conditioning
genome. The dots on the plot represent the distance
between the labeled genome and Methanocococcus jannas-
chii (plotted on the x-axis) and Methanosarcina mazei
(plotted on the y-axis). Considering that both these
genomes are methanogens, it would be expected that as
the distance from a particular genome to Mco. jannaschii
increases, an increase would also be observed in the dis-
tance from that genome to Msa. mazei. This scenario is evi-
dent in the supplementary material where Mco. jannaschii
(x-axis) is plotted against Methanococcus maripaludis (y-
axis). However there appears to be no such correlation in
figure 1c. What would also be expected is that well recog-
nized phylogenetic groups would cluster together on this
plot. However this is also not observed, for example the
Crenarchaeota do not form a cluster. The reason why this
plot behaves differently is that the genome that has been
plotted on the y-axis is Msa. mazei. Msa. mazei is the clos-
est relative to the conditioning genome (Msa. acetivorans)
in this analysis. It was inferred from a phylogram of this
tree that the terminal branch leading to Msa. mazei had a
length of 2.02 whilst the average branch length across the
tree was only 0.31. It is also interesting to note that M.
mazei features as an outlier on this plot, not with the other
methanogens and it also has the second largest distance to
Mco. jannaschii.
Empirical example of choosing one Conditioning Genome
Figure 2a depicts a phylogenetic tree constructed using the
neighbor-joining algorithm [47] with Mco. jannaschii cho-
sen as the conditioning genome. The inferred neighbor-
joining tree did not recover the Crenarchaeota as a clade
(see table S3 for members of the Crenarchaeota). Four of
the Crenarchaeota are found in a clade, however instead
of featuring along with the other Crenarchaeota, Aero-
pyrum pernix appears in a group with the Themococci and
Nanoarchaeum equitans (with bootstrap support of 62%),
(see supplementary information for more examples of
this scenario). It is interesting to note from figure 1a that
Mco. jannaschii is the most outlying point on one end of
axis 2.
Comparing the Supertree (constructed using the modified
BIONJ algorithm) to the Avg Conditioned Reconstruction
tree
The trees derived using the modified BIONJ algorithm
[36] were compared to the Avg CR tree and it was evident
that they were quite similar. Both the inverse-variance
weighting and the vote-counting tree differed from the CR
tree by an RF distance of 6 [42]. The BIONJ supertree algo-
rithm outperforms the Avg CR method as it has been
stated the latter may not be tree-additive [36]. However
the principles upon which the two methods are based are
not entirely different; the supertree approach amalga-
mates information from the distance matrices produced
using each of the 22 Archaea as the conditioning genome.
In an approach that is not dissimilar, Avg CR produces
only one distance matrix with its values being representa-
tive of the distances between two given taxa using the
other twenty genomes as the conditioning genome, aver-
aged by the number of genomes.

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(a) Phylogenetic tree constructed using the CR algorithm with only one conditioning genome chosen, in this case Mco. jannas-chii
Figure 2
(a) Phylogenetic tree constructed using the CR algorithm with only one conditioning genome chosen, in this
case Mco. jannaschii. The tree is rooted on four members of the Crenarchaeota (b) Phylogenetic tree constructed using Avg
CR i.e. every genome in the analysis acts as the conditioning genome.
Pyrobaculum aerophilum
Sulfolobus solfataricus
Sulfolobus tokodaii
Sulfolobus acidocaldarius
Aeropyrum pernix
Nanoarchaeum equitans
Thermococcus kodakaraensis
Pyrococcus furiosus
Pyrococcus horikoshii
Pyrococcus abyssi
Picrophilus torridus
Thermoplasma acidophilum
Thermoplasma volcanium
Haloarcula marismortui
Halobacterium
Methanosarcina mazei
Methanosarcina acetivorans
Methanopyrus kandleri
Methanococcus maripaludis
Methanobacterium thermoautotrophicum
Archaeoglobus fulgidus
55
100
72
62
87
100
50
97
100
88
99
100
91
78
42
99
96
77
55
Aeropyrum pernix
Pyrobaculum aerophilum
Sulfolobus solfataricus
Sulfolobus tokodaii
Sulfolobus acidocaldarius
Thermococcus kodakaraensis
Pyrococcus furiosus
Pyrococcus horikoshii
Pyrococcus abyssi
Nanoarchaeum equitans
Picrophilus torridus
Thermoplasma acidophilum
Thermoplasma volcanium
Haloarcula marismortui
Halobacterium
Methanosarcina mazei
Methanosarcina acetivorans
Methanobacterium thermoautotrophicum
Methanopyrus kandleri
Methanococcus maripaludis
Methanococcus jannaschii
Archaeoglobus fulgidus
100
62
100
38
100
79
100
31
68
68
100
97
100
89
96
100
84
96
83
98
A
B

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Comparing different genome-phylogeny methods
In order to explore whether similar kinds of approaches
such as gene-content methods or sequence-based meth-
ods gave similar or dissimilar results we undertook an
analysis of RF [42] distances between trees produced by all
the different methods (figure 3). Our specific comparison
involved taking each of the 100 bootstrap replicates pro-
duced by a particular method and comparing every tree
replicate with each of the sets of 100 bootstrap trees that
had been constructed for all of the methods. For each
method comparison (i.e. 16S rRNA Vs Shot1) 10,000
individual comparisons were performed (100 × 100),
resulting in 280,000 comparisons being performed in
total across all the methods. Also plotted in this figure,
represented by an asterisk is the RF [42] distance derived
between the individual original trees derived using each of
the methods. CR trees produced using only one condi-
tioning genome were excluded from this analysis because
these trees contain only 21 taxa with the conditioning
genome being excluded from the phylogenetic analysis.
Also excluded were the supertrees created using the mod-
ified BIONJ approach [36] as these were only included in
the study to perform a comparison with the Avg CR tree.
The results are presented in figure 3, where, for clarity, we
have coloured the sequence-based versus sequence-based
comparisons black, the gene-content versus gene-content
analyses grey and the sequence-based versus gene-content
comparisons are left open.
One single overriding trend emerges from this analysis.
When we compare like with like (say, comparing gene-
content with gene-content) the trees are more similar than
when we compare trees derived using methods from dif-
ferent categories of phylogenetic approach (say, when
comparing gene-content with sequence-based methods).
The extreme outliers are the comparison of each of the
bootstrap replicates from the concatenated dataset against
the same set of 100 bootstrap replicates. The extreme out-
lier on the other side is found in the comparison of the
100 16S rRNA bootstrapped gene trees with 100 bootstrap
replicates created using the SHOT 1 method.
Figure 3 illustrates that the average number of steps (e.g.
merging or splitting nodes) involved in transforming the
bootstrapped 16S rRNA tree replicates into the concate-
nated alignment replicates is 13 (i.e. the RF distance). To
transform the 16S rRNA tree replicates into the supertree
replicates requires an average of 14 elementary operations
and to convert the 16S rRNA replicates into the SHOT var-
iant 1 replicates is 18. Clearly these methods produce sig-
nificantly different tree topologies.
A visual comparison of the 16S rRNA versus the Condi-
tioned Reconstruction tree is provided using the TreeMap
2.0β software [44] in the supplementary material
(TreeMap folder). Perhaps at a first glance the trees do not
seem significantly different but if examined in greater
detail using either distance metrics or indeed by eye it is
evident that fundamental differences arise. Take for exam-
ple the topology of the Thermococci, the placement of
Nanoarchaeum equitans or the position of Methanopyrus
kandleri to name but a few.
What the trees say
Having different principles upon which the methods are
based is inevitably going to lead to differences, for exam-
ple within the Archaeal phylogeny a methanogen that
appears in conflicting positions in the trees created using
the different methods is Methanopyrus kandleri. It is known
that whole-genome trees based on either gene-content or
on the conservation of gene order commonly group M.
kandleri with the Methanococcales and the Methanobacte-
riales, because M. kandleri is known to share groups of
genes associated with methanogenesis and to have a sim-
ilar operon organisation to Mco. jannaschii and M. thermo-
autotrophicum [48]. It is therefore not surprising that in
each of the four whole-genome trees such a positioning
was observed and backed up with high bootstrap support.
Methods that are not based on gene-content do not place
such a strong emphasis on shared genes such as those
involved in methanogenesis, perhaps for this reason
instead of grouping with the other methanogens in the
supertree M. kandleri is found in a clade with Arc. fulgidus.
Conclusion
Conclusions about the similarities of phylogenetic trees is
often carried out using visual assessment, without using
any explicit measure of tree-to-tree similarity. As an exam-
ple, Choi and Kim [49] claimed that phylogenetic trees
reconstructed by most methods are "practically the same
as that constructed from SSU rRNA sequences". In this
study, we were not trying to evaluate the method of phyl-
ogeny reconstruction or to infer the "Tree of Life", if one
exists [50]. Rather, we wished to explore variation in the
resulting tree topology when a number of exemplar phyl-
ogenetic approaches were examined.
Even though the authors of CR claim their method is
robust to HGT, the Avg CR method performs very simi-
larly to the other gene-content methods. We have found
that CR using one conditioning genome is misled by
genome size and the taxonomy of the conditioning
genome. This can have a profound affect on a number of
aspects of the analysis. First of all, the choice of condition-
ing genome affects the inferred distances between two
other genomes. Depending on which conditioning
genome is used, two organisms can appear to be close rel-
atives or distant relatives. In addition, there is a systematic

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Robinson Foulds distances between each of the 100 bootstrap replicates for a method against the sets of 100 bootstrap repli-cates for all the methods
Figure 3
Robinson Foulds distances between each of the 100 bootstrap replicates for a method against the sets of 100
bootstrap replicates for all the methods. The sequenced-based versus sequenced-based methods are coloured black, the
gene-content versus gene-content methods grey and the gene-content versus sequence-based are left blank. Each asterisk rep-
resents the Robinson Foulds distance between the unpermuted datasets.

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bias in the inferred distance between two genomes if these
two genomes are close relatives of the conditioning
genome.
The case of the effect of the conditioning genome on the
inferred distance can be shown in the following hypothet-
ical situation. Consider two similar genomes of interest,
say, two species of the same genus. They have very similar
genome content. Now consider two conditioning
genomes, one of whom is also in the same genus and one
that is in a different phylum. In both cases, the condition-
ing genome has 100 genes. The presence-absence matrix
of genes in the genomes of interest that is recorded when
using the conditioning genome that is in the same genus
is as follows (left-to-right: presence, absence; top-to-bot-
tom: presence, absence):
The matrix for the same two genomes when using a con-
ditioning genome that is a distant relative could be as fol-
lows:
The LogDet distance estimated from the first matrix is
0.357 whereas the distance estimated from the second
matrix is 0.041. This explains why we see phylogenetic
trees with long branches leading to those taxa that are
close relatives of the conditioning genome. This is a sys-
tematic error in the CR method [22,36]. This kind of error
has been noted previously and there have been sugges-
tions for how to avoid this error [36]. However, given the
pervasiveness of the problem, it is not obvious to us that
simply being judicious about the choice of conditioning
genome will solve the problem.
Of concern also is the comparison of the 100 boot-
strapped phylogenetic trees derived from the concate-
nated data against the same set of tree 100 trees. This is the
single longest alignment in our analyses and it is also the
dataset where there was the closest resemblance between
the bootstrap samples. The real meaning of bootstrapping
has been debated, but probably the most accurate assess-
ment of what it does is that it evaluates whether there has
been enough sampling of the original data [9]. The data in
figure 3 seem to indicate that in general during bootstrap-
ping a similar phylogenetic tree was returned. Clearly,
however, from the other analyses, there are conflicts in the
genome data that are not adequately accounted for by the
concatenated data. We would go further to suggest that
the concatenated alignment is in effect hiding the varia-
tion in the genome-scale data. Indeed, it has been sug-
gested that phylogenetic trees derived using this approach
represent the "tree of one percent" of a genome [51]. It
might not be surprising therefore that there is considera-
ble agreement among bootstrap samples from a large
number of alignment columns that have been pre-selected
from a specific group of informational genes. This does
not mean that we can infer from such a dataset that there
is a robust phylogeny. We can infer that using a dataset of
informational genes, where obvious horizontal transfers
have been removed, bootstrapping really is a measure of
whether we will benefit from sampling more alignment
columns and using them in the same kind of analysis.
The main point raised by our analysis is that while differ-
ent clustering methods have been developed that use dif-
ferent data, these methods produce different inferences
and these inferences are not essentially the same as the
inferences from SSU rRNA (see [49], where the opposite
claim is made). The gene-content methods produce
results that are similar to one another. We suggest that
these phylogenies are biased towards metabolism-based
classifications (i.e. the clustering of organisms based on
similarity of metabolism) rather than on the basis of
recentness of common ancestry, although definite ele-
ments of both come through in the same analysis. Phylo-
genetic trees derived from the SSU rRNA gene or from the
concatenated alignment are really trees of small amounts
of any genome and these alignment positions are likely to
be in good agreement with one another, being generally
inherited together. The evolutionary history of the
remainder of the genomes is however neglected. The
supertree approach seeks to amalgamate the information
from many input trees and therefore is a sequence-based
method that is more democratic in terms of getting its
information. However, the data analysed here represent a
small fraction of the total amount of the evolutionary
information that is available and until liberal supertree
methods can accommodate complete genome informa-
tion, they too, will only infer histories from partial data.
In short, none of the phylogenetic trees presented here are
without merit, however, interpretation of these trees
should always be accompanied by caveats that describe
specifically what the trees represent. It is still not clear that
in any of these instances the end product is a true species
phylogeny. What is clear is that these methods do not
agree with one another. We feel it is unwise to speculate
on which method, model, approach or encoding of a
genome is likely to be the best – this may be a task for tree
interpretation in any case – however in the future it is pos-
sible that appropriate simulation studies will provide
insights into what each method really recovers.
Authors' contributions
AMc performed all the analysis of the methods and wrote
the computer code required drafted the manuscript. JC
96
0
2
2
⎡
⎣⎢
⎤
⎦⎥
49
1
1
49
⎡
⎣⎢
⎤
⎦⎥

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performed the PCA analysis and provided help with the R
software package and editing the manuscript. JMc con-
ceived the idea for this project, oversaw this study and in
particular edited the manuscript. All authors have read
and approved the final manuscript.
Additional material
Acknowledgements
This work was funded by a Science Foundation Ireland grant EEB0026 and
an Irish Research Council for Science, Engineering and Technology grant.
We wish to acknowledge support from the Irish Centre for High-End com-
puting. We would also like to thank the four anonymous referees whose
comments have improved this manuscript greatly.
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