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Abby et al. BMC Bioinformatics 2010, 11:324
http://www.biomedcentral.com/1471-2105/11/324

Open Access

METHODOLOGY ARTICLE

Detecting lateral gene transfers by statistical
reconciliation of phylogenetic forests
Methodology article

Sophie S Abby, Eric Tannier, Manolo Gouy and Vincent Daubin*

Abstract
Background: To understand the evolutionary role of Lateral Gene Transfer (LGT), accurate methods are needed to
identify transferred genes and infer their timing of acquisition. Phylogenetic methods are particularly promising for this
purpose, but the reconciliation of a gene tree with a reference (species) tree is computationally hard. In addition, the
application of these methods to real data raises the problem of sorting out real and artifactual phylogenetic conflict.
Results: We present Prunier, a new method for phylogenetic detection of LGT based on the search for a maximum
statistical agreement forest (MSAF) between a gene tree and a reference tree. The program is flexible as it can use any
definition of "agreement" among trees. We evaluate the performance of Prunier and two other programs (EEEP and
RIATA-HGT) for their ability to detect transferred genes in realistic simulations where gene trees are reconstructed from
sequences. Prunier proposes a single scenario that compares to the other methods in terms of sensitivity, but shows
higher specificity. We show that LGT scenarios carry a strong signal about the position of the root of the species tree
and could be used to identify the direction of evolutionary time on the species tree. We use Prunier on a biological
dataset of 23 universal proteins and discuss their suitability for inferring the tree of life.
Conclusions: The ability of Prunier to take into account branch support in the process of reconciliation allows a gain in
complexity, in comparison to EEEP, and in accuracy in comparison to RIATA-HGT. Prunier's greedy algorithm proposes a
single scenario of LGT for a gene family, but its quality always compares to the best solutions provided by the other
algorithms. When the root position is uncertain in the species tree, Prunier is able to infer a scenario per root at a
limited additional computational cost and can easily run on large datasets.
Prunier is implemented in C++, using the Bio++ library and the phylogeny program Treefinder. It is available at: http://
pbil.univ-lyon1.fr/software/prunier
Background
The systematic reconstruction of molecular phylogenies
based on the diversity of genes found in complete
genomes reveals an unforeseen degree of incongruence
among gene trees. Different reasons, either biological or
methodological, explain this diversity of patterns. First,
evolutionary mechanisms such as gene duplication and
loss, lateral gene transfer (LGT) and incomplete lineage
sorting generate gene histories that deviate from that of
species [1]. In unicellular organisms, and particularly
Bacteria and Archaea, most of the real phylogenetic conflict is likely the result of LGT [2,3]. On the other hand,
* Correspondence: daubin@biomserv.univ-lyon1.fr
1

Université de Lyon; Université Lyon 1; CNRS; INRIA; UMR 5558, Laboratoire de
Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, F-69622
Villeurbanne, France

Full list of author information is available at the end of the article

the reconstruction of gene histories based on sequence
alignments is not trivial, and many artifacts are known
which produce aberrant phylogenies due to stochastic
effects or inadequate models of sequence evolution [4-7].
A challenge in the understanding of the patterns and processes of genome evolution is therefore to sort out these
different sources of conflict.
The question of identifying LGTs based on phylogenies
typically applies to the following data: first, a gene phylogeny, characterized by an unrooted tree topology, with
branch lengths and statistical support for internal
branches; and second, some knowledge of the evolutionary relationships among the organisms represented in
this tree, ideally a rooted species phylogeny. Events of
LGT can then be invoked to explain the topological discrepancies between the two trees. Among the various
approaches that have been proposed to resolve the prob-

© 2010 Abby 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.

Abby et al. BMC Bioinformatics 2010, 11:324
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lem of tree reconciliation, the MAF (maximum agreement forest), and the closely related SPR (subtree pruning
and regrafting) arguably represent the most appropriate
models for the replacement of genes by LGT. The MAF
problem consists in finding the smallest number of edges
to cut in both trees in order to obtain two identical "forests" of rooted subtrees [8,9]. The SPR problem also corresponds to minimizing the number of subtrees to cut in
a tree, but adds extra complexity by searching for the
optimal place to regraft them. In the case of a rooted species tree, both approaches are equivalent to minimizing
the number of LGTs that occurred in the gene family.
These problems are known to be computationally difficult, but several algorithms have been proposed, notably
to efficiently address the SPR problem. For instance,
Than and Nakhleh have proposed a decomposition
approach, implemented in the RIATA-HGT program
[10,11], which identifies regions of the tree where the
conflict can be resolved independently, and thus significantly reduces the complexity of the SPR reconciliation in
many cases. EEEP also implements such a decomposition
[12], and adds the possibility to restrict the type of SPR
moves to those that immediately reduce the discordance
among trees.
Obviously, an incorrectly reconstructed gene tree will
lead to the inference of erroneous LGTs. It is therefore
essential to take into account all information at hand on
the reliability of the observed topological conflict, such as
the length and support of all branches. Methods of LGT
detection based on topological comparisons sometimes
propose ways to incorporate the statistical information of
the gene tree [11,13,12]. RIATA-HGT [11] first performs
a purely topological reconciliation that proposes a collection of LGT scenarios. Each transfer is associated with a
value which depends on the statistical support of the conflict it resolves in the gene tree, and the user can choose
to ignore LGTs under a given threshold. EEEP [12] uses a
different approach in which internal branches of the gene
tree having a statistical support below a given threshold
are collapsed a priori, before the trees are reconciled.
The two approaches described above have been implemented and tested on simulated datasets [11,12]. The
main concern in both simulation setups was to evaluate
time performance and the ability of the methods to
recover the number of simulated LGTs in a gene tree.
However, biologists are usually interested not only in the
number of LGTs but more importantly in identifying the
actual events of gene transfer, i.e. the exact set of species
that are "misplaced" in the gene tree. Both approaches
generally propose a number of evolutionary scenarios,
but their accuracy has not been evaluated so far.
Here, we use simulations to explore the ability of different methods to detect events of transfers based on gene

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trees reconstructed from sequences. We introduce a
greedy algorithm called "Prunier" [14], which uses information on topology, statistical support and branch
lengths to quickly identify a maximum statistical agreement forest (MSAF) that corresponds to a most parsimonious scenario of transfer. Prunier uses a customizable
statistical agreement function. Two implementations of
this function were tested: a fast one based on branch support, and a more advanced one which uses the expected
likelihood weights (ELW) test [15]. We show that working
on reconstructed trees strongly affects the ability of different methods to identify transfer events. Although all
methods can roughly estimate the number of LGTs that
occurred in a gene history, the accuracy of proposed scenarios varies largely. In comparison with other methods,
Prunier has lower false positive rate, which makes it a
more accurate approach to detect LGTs in almost all simulated situations, especially for complicated gene histories. When tested on biological data of 23 universal
proteins with hypothetical reference trees, Prunier
revealed high rates of LGT, in particular in genes that are
known to be prone to transfer. However, the degree of
conflict in these genes raises concern on the approach
used to reconstruct universal trees.

Results
Prunier Algorithm
Objectives

The phylogenetic detection of lateral gene transfers relies
on differences between a gene tree TG, with branch
lengths and support values, and a reference species tree
TS on the same set of species S. Our method takes both
trees as input. For clarity, we will suppose that the species
tree is rooted, though the method offers the possibility of
using an unrooted reference tree as input, with a reasonable increase in complexity (see Material and Methods).
The input gene tree is always unrooted, and a byproduct
of the procedure is to output a restrained set of root locations on the gene tree. Here we consider that topological
differences between TS and TG can result either from LGT
or from stochastic effects in the process of gene tree
reconstruction. Therefore, an agreement function is
needed to decide whether observed topological differences among trees are significant or not. Examples of
such functions are maximum-likelihood tests comparing
trees given a sequence alignment: KH (Kishino-Hasegawa
[16], SH (Shimodaira-Hasegawa [17], AU (approximately
unbiased [18] or ELW (expected likelihood weights [15]
tests. Because these tests all take unrooted trees as input,
we used additional criteria to be able to handle the disagreement for the position of the root when necessary
(see Material and Methods). We also propose a faster
alternative which simply considers the statistical support

Abby et al. BMC Bioinformatics 2010, 11:324
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of internal branches. Two (sub-)trees are said to disagree
if they fail the agreement function.
If a species tree and a gene tree disagree (based on the
agreement function), the objective is to decompose them
into a statistically agreeing forest. For a subset Si of the set
of species S, we note T(Si) the subtree of T containing
exactly Si where internal nodes of degree 2 are contracted.
We aim at partitioning the set of species Sinto a minimum number of subsets S1 ,...,Sk such that for each subset
Si, the two subtrees TS(Si) and TG(Si) agree, and all TS(Si)
as well as all TG(Si) are disjoint. We call this partition a
maximal statistical agreement forest (MSAF), in reference to the maximal agreement forest [8,9], which is the
particular case where the agreement function gives a positive answer only if the two compared (sub-)trees are
homomorphic (have the same topology).
Among S1 ,...,Sk , only one subset contains the root of
the species tree. This is the non-transferred "backbone" of
the tree as the root is its most ancient node and cannot
acquire a gene from one of its descendants. The other
TS(Si) of the forest are interpreted as LGTs that occurred
in the last common ancestor of Si,. Their number (k-1) is
the minimum number of transfer events that have to be
invoked to explain the significant differences between the
two trees.
Greedy Algorithm

We use a greedy procedure to approach the maximum
statistical agreement forest of two trees (see Fig. 1). Conflicting edges of the gene tree are those inducing bipartitions that are not in the species tree. A transfer event is
characterized by a subtree TG(Si) that is in agreement
with the corresponding TS(Si) but whose position is conflicting. In other words, an LGT results in a non conflicting edge whose removal decreases the conflict among
trees. A non conflicting edge e cuts TS in two subtrees,
one containing the root, the other defining a common
clade Si. If TG(Si) and Ts(Si) agree, the "conflict score" of e
is a function (detailed in Material and Methods) of the
number and support of the conflicting edges that are
eliminated if e is removed from the tree. We consider the
non conflicting edge with the highest conflict score as
defining the most likely transfer. The procedure (Fig. 1)
can be briefly described as follows:
• While the gene tree and species tree disagree:
▪ Remove the edge defining a statistically agreeing
common clade with maximal conflict score
Often, several edges have the same conflict score. In
this case, a different criterion is used to choose what edge
to cut among top rating edges. We first use the alignment
of the gene family to estimate branch lengths on both the
gene and reference trees using Treefinder [19]. Then, for
each candidate edge, we compute the difference between

Page 3 of 13

its lengths in both trees and cut the one with the highest
difference. We hence remove the branch that is most
affected when constrained to its reference position. In
practice, branch lengths are estimated only once. This
step appears to be necessary in most gene families and it
accounts for most of the computing time in the "fast"
implementation of Prunier.
Because only non conflicting edges are removed, the
procedure always produces two subtrees, one of which is
in agreement by construction, and the other (which contains the root) can be used recursively as an input of the
function. Eventually, a statistical agreement forest is
reached and each component of this forest which does
not contain the root of TG is interpreted as originating
from a transfer event. A scenario of transfer can readily
be constructed from the comparison of TG with the forest.
The algorithm and its implementation are fully detailed
in the Material and Methods section. In particular, the
definition of the agreement function, conflict score and
the position of the root are discussed.
Accuracy on simulated LGTs

We used the procedure described in [20] (see Material
and Methods for details) to generate 330 gene trees and
corresponding sequence alignments with increasing
number (from 0 to 10) of LGTs. Gene trees were simulated with subtree pruning and regrafting (SPR) operations from a 40-taxa rooted reference tree. Gene
sequences were simulated along gene trees with variable
rates of evolution among branches and sites (See Material and Methods for details). Maximum-likelihood (ML)
trees were reconstructed using the resulting alignments.
We used a different model of substitution than that used
for sequence simulations to increase the chance of topological conflict resulting from reconstruction artifacts.
All results of the simulation procedure are available as
supplemental
information
(additional
file
1,
"simulated_dataset_prunier.zip").
We compared the performances of RIATA-HGT [11]
(available in the PhyloNet package [21], EEEP [12] and
Prunier based on this simulated dataset.
Accuracy of detected transfer events

Rather than focusing on the accuracy of the number of
detected LGTs as in previous simulation studies
[10,12,11], we concentrated on the comparison of
inferred scenarios with the true (simulated) one. Among
the proposed LGT events, we counted the number of true
and false positives (respectively TP and FP) for each
method. In subsequent comparisons, all three methods
used the same threshold for branch support: for EEEP,
branches under this threshold are collapsed a priori; for
RIATA-HGT, among all inferred transfers only those

Abby et al. BMC Bioinformatics 2010, 11:324
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Figure 1 Prunier algorithm: example of a Prunier run. Example of reconciliation of an unrooted gene tree TG with a rooted species tree TS by
searching for the maximum statistical agreement forest (MSAF). In the gene tree, blue branches are those common to the two trees whereas red
branches indicate conflicting edges, i.e., those found in TG but not in TS. Support values are shown for conflicting edges. In this example, two rounds
are needed to reconcile the two trees. The agreement function "Agree" corresponds here to the "fast" version of Prunier: a gene (sub-)tree is considered in statistical agreement with the species (sub-)tree if no conflicting edge above 80 exists. At each round, clades corresponding to common edges
(blue branches) are ranked by decreasing scores. This score reflects the conflict (combination of the support values) removed when the edge is cut
(symbolized by "Rm{clade}"). The highest scoring subtree candidate to transfer is removed if it is in agreement with the species tree. The output of
Prunier is a statistical agreement forest (SAF), composed of a reconciled backbone subtree (non-transferred sequences: {ABFGHIK}) and as many reconciled subtrees (transferred sequences) as lateral gene transfers (LGT). In this example, two LGT events are inferred: {J} and {CDE} have been transferred.

crossing a branch above this threshold were retained; and
for Prunier, two trees were in agreement if they had no
conflicting edges above this threshold. Fig. 2 shows the
number of TP (fig. 2A) and FP (fig. 2B) as a function of
the true number of LGT events, with a threshold of 0.90

(see additional files 2 and 3 for results with a 0.60 threshold:
"TP_HGT_multi-scenar_BP60.pdf"
and
"FP_HGT_multi-scenar_BP60.pdf" ). For clarity, the
results of another agreement function based on the ELW
test [15] (Prunier-slow) which gave results very close to

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scenario proposed (see Material and Methods for why
not all transfers are detectable). For this reference, the
estimated number of transfers is quite accurate but the
number of FP increases with the number of transfers,
remaining under 2 (fig. 2B). The three methods have different behaviors with increasing number of transfers.
RIATA-HGT performs relatively well at identifying true
transfer events (fig. 2A), but consistently infers false positives, even when no transfers have been simulated (fig.
2B). Surprisingly, this number of FP does not increase significantly with the number of transfers. EEEP is very good
at detecting zero or one transfer, but then increasingly
fails at producing any transfer scenario, with more than
50% of failure for the 10-transfer category. Prunier is
comparable to RIATA-HGT for the detection of TP (fig.
2A), but has lower rates of FP (fig. 2B). The rate of FP
increases with the number of simulated transfers to reach
the same level as RIATA-HGT with 10 transfers.

Prunier-fast are not presented. While Prunier gives a single scenario, both RIATA-HGT and EEEP generally propose several solutions that are equivalent in terms of
number of events. We chose to show the performance of
these programs by recording both the worst (with minimum TP and maximum FP) and best (i.e. with maximum
TP and minimum FP) scenarios.
To obtain an idea of the best possible estimate of the
number of detectable LGTs in our simulations, we ran
RIATA-HGT on the true gene trees (those used for
sequence simulations), and kept as a reference the best

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Figure 2 Accuracy of transfer events detection. A: numbers of true
positive LGTs (TP). B: numbers of false positive LGTs (FP). LGT events detected in maximum-likelihood gene trees are displayed as a function of
the real number of transfers per tree. Results were computed with a
support threshold for topological conflict significance of 0.90. As EEEP
and RIATA-HGT may propose multiple scenarios, grey areas represent
the variability between the worst and the best scenarios. Areas are delimited by blue borders for RIATA-HGT and green borders for EEEP. Prunier (fast version) results are drawn in magenta. The black curves
symbolize the reference boundary: those curves were obtained using
RIATA-HGT (best scenario selected) on true gene trees (before sequence simulation). The green numbers below the true positives area
of EEEP represent the number of unresolved cases (either quoted as
"Unsolved" by the program, or resulting from a program crash). The
blue numbers above RIATA-HGT TP area indicate the two cases for
which the program crashed. A program crash is counted as 0 TP and 0
FP. The grey line shows a relationship 1:1 between the two axes. The
estimated number of transfers by each method is the sum of the
curves shown in A and B.

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Many studies trying to resolve controversial phylogenetic
relationships among species have used LGT detection as
a first step to retrieve sets of orthologous genes (e.g., [2226]. In such case, it is essential that all LGT events are
correctly detected in a gene family. We thus measured the
proportion of gene trees in which all events of LGT were

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Figure 3 Inference of correct complete LGT scenarios. Proportion
of correctly inferred LGT scenarios, i.e. scenarios with 100% TP and 0%
FP as a function of the true number of LGT. Results were obtained with
a support threshold for topological conflict significance of 0.90. As
EEEP and RIATA-HGT may propose multiple scenarios, grey areas represent the variability between the worst and the best scenarios. Areas
are delimited by blue borders for RIATA-HGT and green borders for EEEP. Prunier (fast version) results are plotted in magenta. The black curve
symbolizes the reference boundary was obtained using RIATA-HGT
(best scenario selected) on true gene trees.

Abby et al. BMC Bioinformatics 2010, 11:324
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correctly identified (100% TP and 0% FP) (Fig. 3, see additional file 4 "Pc_true_LGT_events_BP60.pdf" for results
with a 0.60 threshold). For Prunier and EEEP, the percentage of exact scenarios is high for low numbers of transfers, but drops relatively quickly to zero. For gene families
with three transfers, these methods predict the correct
scenario in about 50% of the cases. For RIATA-HGT, the
proportion of correctly predicted scenarios is low even
with few transfers, owing to its high rate of false positives.
Transferred sequences

The positive predictive value (PPV) is the proportion of
transferred sequences among those predicted to be transferred. The negative predictive value (NPV) is the proportion of non-transferred sequences among those
predicted to be non-transferred. In this evaluation, we do
not present the results for EEEP as its rate of failure with
high rates of transfers precludes fair comparisons among
approaches. We measured the predictive power of
RIATA-HGT, selecting the best among all inferred scenarios, and Prunier for a set of simple agreement functions, defined by thresholds ranging from 0.50 to 0.95
(Fig. 4). We also tested for Prunier another agreement
function which uses comparisons of the likelihood of
entire tree topologies by the ELW test [15] (Prunierslow). All values were computed on the 330 simulated
gene trees.
The NPV is very similar among both methods (between
75% and 80%), and variations of the agreement function
have only little effect on this value, although for all
parameters, RIATA-HGT is slightly better than Prunier.
In contrast, the PPV varies greatly with the threshold,
especially for Prunier, but with consistently higher PPV
than RIATA-HGT. These results can be summarized by
computing the accuracy of the two methods, which is the
proportion of correctly classified sequences (transferred
or not). The accuracy ranges between 73% and 77%
(mean of 74%) for RIATA-HGT, and lies between 77%
and 79% (mean of 78%) for Prunier.
Impact of the root of the species tree on reconstructed
scenarios

Prunier proposes by default a scenario for every possible
root of the species tree (see Materials and Methods). Different rootings of the species tree are expected to give different LGT scenarios, because the choice of a root
constrains the clades that can be transferred. Especially,
the number of transfer events is expected to be different.
We examined the possibility that LGTs could inform on
the true position of the root. For each of the 77 possible
roots of our 40 leaves species tree, we counted the number of inferred LGTs in the 330 simulated gene families.
For instance, the total number of LGTs ranged between
1370 and 1549 with a threshold of 0.90. The true root was

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among the two locations with the minimal number of
LGT, the other one being on a neighboring edge. However, a Wilcoxon paired test showed that only 50 among
the 77 possible roots were significantly different from the
best root. This establishes LGT as a potential tool for
rooting species trees but suggests that many gene families
and relatively high rates of transfer are necessary to discern the true root.
Application to a biological dataset

We used the dataset of Brown and colleagues [22] which
contains 23 universal proteins distributed in 45 species
from the three domains of life. This study pioneered a
number of more recent analyses in which LGTs are
searched in order to obtain a set of orthologous genes
that can be concatenated to resolve a specific question
[24-26]. In their article, Brown et al. focused on the elimination of gene transfers among domains, by manually
removing those gene families that supported a nonmonophyletic bacterial domain. Nine gene families were
removed on this criterion, which reduced the dataset to
14 genes. Two different species trees were reconstructed:
a first one based on the whole dataset (23 genes), which
was deemed artifactual due to LGT and a second one
based on the cleansed dataset (14 genes). The two trees
mainly disagreed on the position of the early diverging
bacterial phyla, respectively spirochaetes and hyperthermophiles.
Although detecting transferred genes with a certain reference tree and using the remaining sequences to infer a
tree would be a circular reasoning, it is possible to use our
algorithm to test the hypothesis that a dataset is devoid of
LGT. We ran Prunier (slow and fast version with a threshold of 0.80 and 0.90) using both trees as a reference, and
looked at the number of transfers inferred in the 23
genes. The simulation results showed very similar results
for the slow and fast version of Prunier (for example, the
number of detected transfers by the slow and fast methods and the threshold of 0.80 were correlated with R =
0.97). With real data, we also observed a high correlation
among different agreement methods (the best being
between slow and fast_0.80: R = 0.63) but with marked
differences for some gene families. For instance the initiation factor 2 (IF-2) gene tree is found to be completely
congruent with the 23-gene reference tree using the slow
test, while the fast version infers 9 transfers. Reciprocally,
some other genes showed higher transfer rates with the
slow test, for instance ribosomal protein S11 where 13
(23-gene tree) or 11 (14-gene tree) LGTs were detected
with the slow version versus one with the fast version, for
both reference trees. The mean number of transfers
invoked in the two sets of genes identified by Brown et al.
is significantly different, for the fast version, regardless of
the reference tree (Wilcoxon test between numbers of

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Figure 4 Accuracy of transferred sequences detection. RIATA-HGT (best scenarios selected) and Prunier (fast version) results are respectively indicated by triangles and circles. The color code corresponds to the different support values tested as a threshold for topological conflict significance.
The figure represents the positive predictive value as a function of the negative predictive value (see Material and Methods) of the two methods. Black
lines represent the limits given by RIATA-HGT on true trees. These statistics show how often predictions are correct when sequences are classified as
transferred (PPV) or not transferred (NPV). A perfect method would have PPV = 1 and NPV = 1.

transfer events for each tree shown in Table 1: p-value =
0.04 for both fast_0.80 and fast_0.90 with the 23-gene
tree, p-value = 0.004 and 0.03 with the 14-gene tree for
fast_0.80 and fast_0.90, respectively). This suggests that
the manual criterion originally used by Brown et al. [22]
for gene exclusion correctly identified genes with high
transfer rates. Nevertheless, many among the 14 genes
retained in the second dataset show a significant amount
of transfers with our method. This raises the question of
whether the combination of such a limited amount of

genes with such a strong degree of conflict can really
yield a reliable species tree.

Discussion
Detecting LGTs using phylogenetic approaches is a challenge for several reasons. First, the reconstruction of
optimal scenarios explaining the discrepancies between
two trees is a complex algorithmic problem. Second, in
practice, not all of these discrepancies require a biological
explanation, because reconstructed gene trees are imper-

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Table 1: Analysis of 23 universal gene families with Prunier with two reference trees
Protein

Alignment
length

LGT inferred by Prunier (slow and fast version)

(* indicates LGT diagnosis by Brown et al. )

23 genes tree reference tree

14 genes tree reference tree

Slow

Fast-80

Fast-90

Slow

Fast-80

Fast-90

aspartyl-tRNA synthetase

249

4

8

8

6

7

7

glutamyl-tRNA synthetase

188

12

12

12

13

11

11

leucyl-tRNA synthetase

358

9

11

11

11

13

13

initiation factor 2

337

8

9

0

3

3

1

elongation factor G

536

10

11

5

11

9

4

elongation factor Tu

340

0

9

9

7

9

9

ribosomal protein L2

192

2

13

1

6

8

0

ribosomal protein S5

131

7

2

2

4

2

2

ribosomal protein S8

118

7

5

0

7

5

0

ribosomal protein S11

110

13

1

1

11

1

1

DNA-directed RNA polymerase β chain

537

0

8

2

8

7

2

DNA topoisomerase I

236

10

13

4

8

12

3

DNA polymerase III subunit

194

3

2

1

2

2

0

signal recognition particle protein

298

3

8

1

4

5

1

alanyl-tRNA synthetase (*)

502

5

7

3

4

11

3

histidyl-tRNA synthetase (*)

166

7

18

9

14

15

11

isoleucyl-tRNA synthetase (*)

552

8

9

7

10

9

8

methionyl-tRNA synthetase (*)

306

11

9

7

0

12

10

phenylalanyl-tRNA synthetase β subunit (*)

177

7

11

3

6

11

3

threonyl-tRNA synthetase (*)

305

13

16

16

11

16

16

valyl-tRNA synthetase (*)

538

9

12

12

7

9

8

aminopeptidase P (*)

95

19

21

21

24

21

21

rRNA dimethylase (*)

126

11

13

2

8

11

2

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fect representations of a true gene history. We aimed at
testing approaches that propose reconciliation scenarios
in the typical situation faced by biologists, i.e., the comparison of an unrooted gene tree reconstructed from
sequences with a rooted reference. Two published methods, EEEP and RIATA-HGT corresponded to these criteria. We propose here a new algorithm that applies to this
situation, along with an evaluation of its performance in
comparison to EEEP and RIATA-HGT on a simulated
dataset.
Simulating LGT

It is difficult to simulate datasets that compare to real
ones, in particular in terms of the phylogenetic artifacts
they produce. We used a procedure [20] that creates SPR
movements on a rooted reference tree and then simulates
different evolutionary rates among branches of the tree
and sequence sites, attempting to produce as realistic as
possible gene alignments. We then reconstructed ML
trees with a model of sequence evolution different from
the one used for sequence simulation.
The results of our simulations show that working on
reconstructed gene trees poses a substantial problem to
LGT detection methods. This is evident from the comparison of the result of RIATA-HGT when used on simulated vs. reconstructed gene trees (Reference vs. RIATAHGT results in Fig. 2 to 4). The best scenario among
those provided by RIATA-HGT on the simulated gene
trees (without sequence simulation) was used as a measure of the best scenario inferable. According to this reference, most transfers (>80%) are detectable and the
expected amount of false positives is relatively low (Fig.
2B).
All methods were able to correctly identify simulated
LGT events to a certain extent. EEEP appeared quite
accurate when proposing a solution, but its frequency of
failure (that is, the program stops without LGT scenario
output) makes it difficult to use with high levels of transfers. In contrast, the two other programs almost always
gave a result and generally produced reasonably good
solutions, even with complex transfer scenarios. However, RIATA-HGT is characterized by high rates of false
positives which explain a consistently low proportion of
exact RIATA-HGT scenarios even with few LGT events
(Fig. 3).
A new approach for transfer detection driven by statistical
criterion

Our new approach, Prunier, sequentially cuts branches
that are found to generate significant conflict among
trees. We reasoned that a fast way to reach agreement
between trees is to first cut those branches that are
responsible for the highest part of the conflict. Accordingly, Prunier relies on a ranking of branches that are

Page 9 of 13

common to the two trees based on the amount of conflict
that is removed when the branch is cut. In the current
implementation, this amount of conflict is a function that
depends on the statistical support of internal branches in
the gene tree (see Material and Methods) and the branch
with the highest rank is cut first. The algorithm is thus
directed by statistical information provided by branch
support in the gene tree and estimating the statistical
support of branches is a prerequisite of the application of
Prunier. This contrasts with EEEP and RIATA-HGT,
where branch support is only used to discard some irrelevant groupings. These approaches rely on combinatorial
algorithms that search to enumerate all topological solutions. In contrast, Prunier uses statistical information in
the gene tree to guide its search and avoid non significant
transfer events to be invoked. As a result, RIATA-HGT
and EEEP usually propose a set of scenarios that are
equivalent in terms of number of transfers, when Prunier
always proposes a unique scenario. Although providing a
single solution is not necessarily an advantage, the scenarios found by Prunier are always better than the best
among those proposed by RIATA-HGT, as Prunier consistently infers fewer false positives for equivalent number of true positives. In comparison with EEEP, the
algorithmic shortcut used by Prunier does not seem to
alter the performance in terms of quality of the results
but appears as a gain in efficiency as Prunier always terminates. We can presume that the type of statistics used
by Prunier for branch support is critical for its performance. The LR-ELW values computed by the Treefinder
program [19] seem to give good results, at least with simulated datasets, but others like bootstrap could also be
tested.
Program parameters: threshold and agreement function

Concerning the choice of a support threshold in the fast
version, it is important to tune this parameter according
to user needs. Higher support value thresholds should be
favored when seeking transfer events with high confidence. In contrast, lower thresholds should be preferred
when trying to identify orthologous sequence sets. However, the negative predictive value (NPV) does not vary a
lot when decreasing the threshold, whereas the positive
predictive value (PPV) drops quickly (Fig. 4). This suggests that higher threshold values are a relatively good
choice in all situations. When comparing Prunier in its
fast and slow versions on simulated data, it appeared that
using the maximum-likelihood test ELW as an agreement
function instead of a function based on branch support
values did not improve the quality of transfer detection
(Fig. 4), and that the resulting LGT scenarios are strongly
correlated. In this case, it does not seem beneficial to use
such a computationally intensive method in Prunier.
However, in contrast with simulated results, the fast and

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Methods
Implementation and availability of Prunier

Prunier is implemented in C++ language, using the Bio++
library [28]. Maximum-likelihood (ML) estimation of

É

É

É

É

É

Conclusions
We propose a new method, Prunier, based on the statistical reconciliation of a gene tree and a reference tree by
searching for the maximum statistical agreement forest.
We compared Prunier and two other programs, EEEP
[12] and RIATA-HGT [11] on a simulated dataset
attempting to reflect realistic conditions of gene families
analyses. Prunier performance and robustness demonstrates its appeal over other tested methods. It proposes a
unique scenario of LGTs that compares to the best
selected scenarios of the two other methods, produces
fewer false positives, in particular compared to RIATAHGT, and is able to infer true transfer events even for the
most complex gene histories, what EEEP fails to do. Being
fast and accurate, Prunier can be used to study phylogenomic datasets.

We have presented the MSAF problem with a rooted species tree and an unrooted gene tree as input. However,
the exact position of the root of the species tree is often
not known. A solution would be to try every root, and
give as many scenarios as there are branches in TS. But
many root positions are equivalent in the sense that an
LGT scenario for a given root is also valid for all roots
that are not included in a transferred subtree. We developed a way to explore more efficiently a set of root positions. Prunier can take two unrooted trees as input, and
output several statistical agreement forest (SAF) decompositions, one for each set of equivalent roots.
Let R be a subset of edges of TS, denoting the possible
root locations. R, TS and TG are sets of edges, so set operations or \ are used. At first, R might be either the set of
all edges in TS, if no information is available on the root
position in the species tree, or any subset of branches
given by the user. A non conflicting edge e of TS cuts TS
into two disjoint subtrees TS(Si) and TS(Sj). Sj is a possible
common clade if Ris not fully included in TS(Si). For each
possible common clade Si a conflict score is computed if
TG(Si) statistically agrees with TS(Si) and the top scoring
Si is selected. If for the top scoring Si, (TS(Si R) = Ø, Si is
considered as a group of transferred species and is
removed from TS and TG. If (TS(Si) R) = Ø, the two possible positions of the root, R TS(Si) and R TS(Sj), are
examined: either the root is not in Si and Si is considered
as a group of transferred species or the root is in Si and
the next top scoring common clade is searched with R = R
TS(Si). It results in a bifurcating procedure where the
number of explored scenarios is lower than the number
of possible roots of TS. The procedure to approach the
MSAF can be described as follows:
Input: two unrooted trees TS and TG, and a subset R of
edges of TS where the root may lie: {TS, TG, R}.
Output: a decomposition in a SAF for each root position contained in R.
• If TS and TG disagree:
▪ Find the common clade Si, such that TS (Si) and
TG (Si) agree, with maximum conflict score
▪ If (TS(Si) R) ≠ Ø then
Recursively run the program with input {TS, TG,
TS(Si) R}
// 1st hypothesis on rooting: Ts (Si) contains the
root, we choose another set of species Si
É

One particular feature of Prunier is its ability to search
efficiently for scenarios of LGT using an unrooted reference tree, thereby proposing different solutions for different possible roots. Interestingly, different roots in the
species tree yield different LGT scenarios, and an overall
score could be computed on all 330 gene trees. We
showed that the best rooted tree (minimum number of
LGTs) is the true root of the reference tree. This suggests
that non-optimal roots tend to produce more LGTs than
the true one. This echoes with previous reports that LGT
events, when seen as a shared character, could sometimes
provide phylogenetic information [27]. We demonstrate
here that Prunier provides information for rooting the
reference tree, since the true root position was the best
(with high support threshold value) or among the best
(for lower threshold values, data not shown) in terms of
LGT number. Although the number of gene trees necessary to unambiguously root a reference tree is probably
high, the LGT criterion could be used to exclude potential roots.

Dealing with possible rootings

É

Rooting the species tree

branch lengths and trees, and ELW tests are performed
by Treefinder [19]. It is available at: http://pbil.univlyon1.fr/software/prunier

É

slow implementations of Prunier gave correlated but
sometimes contrasting estimates of LGT numbers on a
real dataset (Table 1). It is difficult to argue for the use of
"slow" vs. "fast" agreement functions on such data. However, all methods detected a large number of transfers in
most gene families, including those conserved for concatenation. This means that there remains a strong phylogenetic conflict among concatenated genes. Interestingly,
tRNA synthetases, which have been reported as prone to
transfer, yield particularly high numbers of LGTs.

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▪ Recursively run the program with input
{TS\TS(Si), TG\TG(Si), R\Ts(Si)}, and add Si to the
SAF
// 2nd hypothesis on rooting: TS(Si) does not contain the root, we prune it
• For each possible root of the initial set R, output
the SAF.
Definition of the Agreement function

A MSAF of a species tree and a gene tree has two types of
components: a backbone tree where the root of the species tree lies, which is interpreted as the set of non transferred sequences, and as many subtrees as inferred
transfers. The backbone tree in TG contains the root but
without information on its exact position. In contrast, all
transferred subtrees are rooted by the backbone in both
TS and TG . An appropriate agreement function must
hence handle both rooted (transferred) and unrooted
(backbone) trees.
The method can potentially handle any agreement
function. We implemented two variants, respectively
called "slow" and "fast" versions.
- A "slow" implementation that depends on the ELW
test with a default p-value of 0.05 [15]. Because ELW is an
unrooted test, our agreement function first checks for the
presence of a well supported bipartition in conflict with
the position of the root. The threshold for this additional
criterion is set by default at 0.90.
- A "fast" implementation where two (rooted or
unrooted) trees are said to be in agreement if the two
trees do not contain any supported conflicting edge
according to a user defined threshold. This test handles
both rooted and unrooted trees.
Scoring candidate transfers

For each edge e of the tree, we have a support value SV(e).
In our implementation, we use the LR-ELW (local rearrangement ELW) support values given by Treefinder [19],
but any other statistics can be used.
For each clade Si, let e1,...,en be the conflicting edges of
TG and TS that are no longer conflicting in TG\Siand
TS\Si.The score of S i is defined by:
n

Score(S i ) =

∑ (SV(e ))
j

5

(1)

j =0

This value is meant to assess the amount of conflict the
group S i is responsible for. The power law, empirically set
to the value of 5, is a way of favoring groups removing
highly supported conflicting bipartitions.

Simulations of LGT

We used the program described by Galtier [20] to simulate LGT and sequence alignments. This program starts
from a rooted species tree and modifies it to introduce
evolutionary rate variations between species. Then gene
trees are generated from the reference trees. Transfers are
created by subtree pruning and regrafting (SPR operations) of random clades, where donors and acceptors are
drawn according to a Poisson law. Time constraints are
given by branch lengths, allowing transfers to occur only
between contemporary lineages. Variations of evolutionary rate among genes (diameter of the tree), and between
lineages are finally introduced. Then artificial sequences
are simulated along each gene tree, under a given model
of substitution. Evolution of sequences also includes random substitution rate variations between sites.
We simulated a dataset based on a 40-taxon species
tree, with a uniform gene length distribution between 100
and 400 amino-acids, under a JTT model of substitution
[29]. Number of transfers increased from 0 to 10. We simulated 30 alignments and trees per number of gene transfers, resulting in a final dataset of 330 simulations.
In order to increase the level of difficulty of our simulated dataset, and thus come closer to real biological datasets, maximum-likelihood (ML) gene trees were
reconstructed with Treefinder [19] (WAG [30] + G8 + I +
F) from the simulated gene alignments. The simulated
dataset can be downloaded at http://pbil.univ-lyon1.fr/
software/prunier/simulated_dataset_prunier/ or found in
the additional file 1: "simulated_dataset_prunier.zip".
Dealing with invisible LGT

Certain simulated transfers have no impact on the phylogeny, and thus are phylogenetically undetectable, even
if they are biologically sound. A transfer between sister
taxa, or between a father and his son will not provoke any
topological conflict. These transfers were filtered out, in
order to quantify how many transfer events, among those
detectable, have been correctly detected. Nevertheless,
one has to keep in mind that these transfers might exist,
and that the number of detected LGTs is therefore underestimated by phylogenetic methods of transfer detection.
Multiple transfers can also be indistinguishable when
ancestors of sister groups independently received gene
from a same species. For example if two sister groups
receive a gene from the same donor, a detection based on
topology discrepancies would infer one LGT instead of
two. These transfers were not sorted out and some
instances of such transfers can be present in the dataset.
This is why we used a topological reconciliation of simulated gene trees before sequence simulation to roughly
estimate the amount of detectable transfers.

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Benchmark on simulations: estimating the performance of
LGT detection methods

Different statistics were used to evaluate the performance
of all LGT detection programs. For transfer events, we
calculated the number of true and false positives (TP and
FP). It was not possible to compute a number of true and
false negatives (TN and FN) for transfer events because
sequences that are not cut out of the tree do not correspond to a single event. Therefore, we also defined TP, FP,
TN and FN for leaves of the tree that were either correctly
or incorrectly cut out by an inferred transfer event. This
allowed us to compute a sensitivity (true positive rate),
specificity, positive and negative predictive values (PPV
the precision, and NPV respectively), and accuracy (P
standing for positives, N standing for negatives) such
that:
TP
TP + FN
TN
Specificity =
TN + FP
TP
PPV =
TP + FP
TN
NPV =
TN +FN
F
TP + TN
Accuracy =
P+N
Sensitivity =

(2)

Comparing root scenarios

In order to compare scenarios of transfers between all
possible roots, we performed all possible paired-Wilcoxon tests between number of transfers per gene families, and applied to the computed p-values a false
discovery rate correction for multiple comparisons [31].
Biological dataset

We used the dataset of Brown and colleagues [22] (Table
1). This dataset gathers 23 universal proteins, present in
45 species of the three kingdoms of life. These proteins
are mainly part of the translational apparatus (tRNA synthetases and ribosomal proteins). The others play a role
in transcription, replication or other basal metabolic
functions. We ran Prunier (slow and fast versions, the latter with two different support value thresholds: 0.80 and
0.90) on these genes, using both reference trees proposed
by Brown et al.: the initial 23-gene tree, and the 14-gene
tree obtained on a cleansed set of genes, after the removal
of 9 genes suspected of lateral gene transfer on the basis
of the non-monophyly of the bacterial domain.

Availability and requirements
• Project name: Prunier

• Project home page: http://pbil.univ-lyon1.fr/software/prunier
• Operating systems: Linux and Mac OS
• Programming language: C++ (Bio++ library: http:/
/kimura.univ-montp2.fr/BioPP/ )
• Other requirements: Treefinder: http://
www.treefinder.de/
• License: freeware
• Any restrictions to use by non-academics: none

Additional material
Additional file 1 The 330 simulated alignments and corresponding
gene trees (true and ML) along with the 40-taxa reference species
tree.
Additional file 2 True positives (transfer events) with a 0.60 threshold
for EEEP, Prunier and RIATA-HGT. For detailed legend see Fig. 2.
Additional file 3 False positives (transfer events) with a 0.60 threshold for EEEP, Prunier and RIATA-HGT. For detailed legend see Fig. 2.
Additional file 4 Proportion of correct complete scenarios with a 0.60
threshold for EEEP, Prunier and RIATA-HGT. For detailed legend see Fig.
3.
Abbreviations
LGT: Lateral Gene Transfer; ML: Maximum likelihood; ELW: Expected likelihood
weights; LR: Local rearrangement; MAF: Maximum agreement forest; SAF: Statistical agreement forest; MSAF: Maximum statistical agreement forest; PPV:
Positive predictive value; NPV: Negative predictive value; FP: False positives; TP:
True positives; FN: False negatives; TN: True negatives
Authors' contributions
SSA, ET, MG and VD designed the algorithm and the simulation procedure. SSA
implemented the program and conducted the experiments. SSA, ET, MG and
VD wrote the paper. All authors read and approved the final version of this
manuscript.
Acknowledgements
SSA is supported by the French "Ministére de l'enseignement supérieur et de la
recherche" and the University of Lyon 1. The authors thank Nicolas Galtier for
providing his program for transfer simulations, Julien Dutheil for his help in
using the Bio++ library, Bastien Boussau and Alain Guénoche for fruitful discussions, Simon Penel, Stéphane Delmotte and Bruno Spataro for recommendations using computational resources. The authors also thank the cc-in2p3 for
computational resources. This work has been supported by the Agence
Nationale de la Recherche grant ANR-08-EMER-011-03 "PhylAriane" and
BLAN08-1_335186 "EcoGenome".
Author Details
Université de Lyon; Université Lyon 1; CNRS; INRIA; UMR 5558, Laboratoire de
Biométrie et Biologie Evolutive, 43 boulevard du 11 novembre 1918, F-69622
Villeurbanne, France
Received: 2 March 2010 Accepted: 15 June 2010
Published: 15 June 2010
© 2010 Abby available 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.
This is an Open Access from: http://www.biomedcentral.com/1471-2105/11/324
BMC article is et al; licensee BioMed Central Ltd.
Bioinformatics 2010, 11:324

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doi: 10.1186/1471-2105-11-324
Cite this article as: Abby et al., Detecting lateral gene transfers by statistical
reconciliation of phylogenetic forests BMC Bioinformatics 2010, 11:324

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