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Syst. Biol. 56(4):564–577, 2007
Copyright
c
Society of Systematic Biologists
ISSN: 1063-5157 print / 1076-836X online
DOI: 10.1080/10635150701472164
Improvement of Phylogenies after Removing Divergent and Ambiguously Aligned
Blocks from Protein Sequence Alignments
GERARD TALAVERA AND JOSE CASTRESANA
Department of Physiology and Molecular Biodiversity, Institute of Molecular Biology of Barcelona, CSIC, Jordi Girona 18, 08034 Barcelona, Spain;
E-mail: jcvagr@ibmb.csic.es (J.C.)
Abstract.—Alignment quality may have as much impact on phylogenetic reconstruction as the phylogenetic methods used.
Not only the alignment algorithm, but also the method used to deal with the most problematic alignment regions, may
have a critical effect on the final tree. Although some authors remove such problematic regions, either manually or using
automatic methods, in order to improve phylogenetic performance, others prefer to keep such regions to avoid losing any
information. Our aim in the present work was to examine whether phylogenetic reconstruction improves after alignment
cleaning or not. Using simulated protein alignments with gaps, we tested the relative performance in diverse phylogenetic
analyses of the whole alignments versus the alignments with problematic regions removed with our previously developed
Gblocks program. We also tested the performance of more or less stringent conditions in the selection of blocks. Alignments
constructed with different alignment methods (ClustalW, Mafft, and Probcons) were used to estimate phylogenetic trees by
maximum likelihood, neighbor joining, and parsimony. We show that, in most alignment conditions, and for alignments
that are not too short, removal of blocks leads to better trees. That is, despite losing some information, there is an increase
in the actual phylogenetic signal. Overall, the best trees are obtained by maximum-likelihood reconstruction of alignments
cleaned by Gblocks. In general, a relaxed selection of blocks is better for short alignment, whereas a stringent selection is more
adequate for longer ones. Finally, we show that cleaned alignments produce better topologies although, paradoxically, with
lower bootstrap. This indicates that divergent and problematic alignment regions may lead, when present, to apparently
better supported although, in fact, more biased topologies. [Bootstrap support; Gblocks; phylogeny; sequence alignment.]
Methods for the simultaneous generation of multiple
alignments and phylogenetic trees are actively being pur-
sued (Fleissner et al., 2005; Lunter et al., 2005; Redelings
and Suchard, 2005; Wheeler, 2001), but, at present, com-
mon practice of phylogenetic analysis requires, as a first
step, the generation of a multiple alignment of the se-
quences to be analyzed. It has been repeatedly shown
that the quality of the alignment may have an enor-
mous impact on the final phylogenetic tree (Kjer, 1995;
Morrison and Ellis, 1997; Ogden and Rosenberg, 2006;
Smythe et al., 2006; Xia et al., 2003). This is particularly
true when sequences compared are very divergent and
of different length, which makes necessary the introduc-
tion of gaps in the alignments.
Due to the computational requirements of optimal
algorithms for multiple sequence alignments, different
heuristic strategies have been proposed.The most widely
used approach has been the progressive method of align-
ment (Feng and Doolittle, 1987) that, together with en-
hancements related to the introduction of gap penalties,
was implemented in ClustalW (Thompson et al., 1994).
In progressive methods, an initial dendrogram gener-
ated from the pairwise comparisons of the sequences is
used to recursively build the multiple alignment, using
dynamic programming (Needleman and Wunsch, 1970)
in the last step. Dynamic programming is an exact algo-
rithm that assures the best possible alignments for given
gap penalties but, due to heavy computational require-
ments, it is only used for pairs of sequences or pairs of
clades of the dendrogram and not for the whole multi-
ple alignment. Several other heuristic multiple alignment
methods have been recently introduced. They include
T-Coffee (Notredame et al., 2000), Mafft (Katoh et al.,
2005; Katoh et al., 2002), Muscle (Edgar, 2004), Probcons
(Do et al., 2005), and Kalign (Lassmann and Sonnham-
mer, 2005), among others. All of them are based on the
progressive method but include several iterative refine-
ments to construct the final multiple alignment. The
latter methods have been shown to outperform purely
progressive methods in terms of alignment accuracy and,
some of them, even in computational time. However, it
has not been shown whether the greater alignment accu-
racy of more sophisticated methods leads to a significant
improvement in phylogenetic reconstruction.
Proteins have some regions that, due to their func-
tional or structural importance, are very well con-
served, whereas other regions evolve faster both in terms
of nucleotide substitutions and insertions or deletions
(Henikoff and Henikoff, 1994; Herrmann et al., 1996;
Pesole et al., 1992). That is, evolutionary rate heterogene-
ity affects to whole regions in addition to single positions.
This type of regional rate heterogeneity is very challeng-
ing for phylogenetic reconstruction, not only in terms of
homoplasy due to saturation (Yang, 1998), but also in
terms of errors in homology during alignment.
Dealing with regions of problematic alignment is a
matter of active debate in phylogenetics. Although some
authors consider that it is best to remove such regions
before the tree analysis (Castresana, 2000; Grundy and
Naylor, 1999; L¨oytynoja and Milinkovitch, 2001; Rodrigo
et al., 1994; Swofford et al., 1996), others think that there
is an important loss of information upon removal of any
fragment of the sequences already obtained (Aagesen,
2004; Lee, 2001) and that this practice should only be
used as the last resource (Gatesy et al., 1993). A third,
intermediate option, is the recoding of such regions us-
ing different strategies (Geiger, 2002; Lutzoni et al., 2000;
Young and Healy, 2003), which allows the use of at least
part of the information. Although these coded charac-
ters are most commonly analyzed with parsimony, it is
564
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2007 TALAVERA AND CASTRESANA—IMPROVEMENT OF PHYLOGENIES AFTER REMOVING BLOCKS 565
also possible to use them as independent partitions in
Bayesian or likelihood frameworks.
In the present work we test, by using simulated pro-
tein alignments with gaps, which are the best alignment
strategies for optimal phylogenetic reconstruction. Two
preliminary considerations are necessary here. First, sim-
ulations of sequences may not cover all the complexity
of evolution but have the advantage over real sequences
that we know the tree from which they have been gener-
ated. There are some alignment sets curated from struc-
tural information that can be used to test alignment
accuracy (Thompson et al., 2005), but the phylogenetic
tree is unknown in these sets, thus making problem-
atic their use for proving phylogenetic accuracy. Second,
we have been working with simulated sequences that
try to reflect the evolutionary patterns of proteins, and
thus many of the conclusions extracted from our work
cannot be directly extrapolated to other markers such
as rRNA, which show very different evolutionary con-
straints (Gutell et al., 1994; Kjer, 1995; Xia et al., 2003).
In our analysis we used different alignment strategies
of the simulated sequences to test if they make any dif-
ference in the final phylogenetic tree. We have selected
ClustalW as the currently most used progressive align-
ment method (Thompson et al., 1994) and Mafft (Katoh
et al., 2005) and Probcons (Do et al., 2005) as examples of
more recently developed methods that have been shown
to obtain very high scores in terms of alignment accuracy
(Blackshields et al., 2006; Nuin et al., 2006). Simultane-
ously with the performance of the alignment programs,
we tested whether removing blocks of problematic align-
ment actually leads to more accurate trees. We used for
this purpose our previously developed Gblocks program
(Castresana, 2000), which selects blocks following a re-
producible set of conditions. Briefly, selected blocks must
be free from large segments of contiguous nonconserved
positions, and flanking positions must be highly con-
served to ensure alignment accuracy. Several parameters
can be modified to make the selection of blocks more
or less stringent. Phylogenetic trees made by maximum
likelihood (ML), neighbor joining (NJ), and parsimony
of the reconstructed alignments show that, in almost all
conditions tested, and at least for alignments that are
not too short, the elimination of problematic regions by
Gblocks leads to significantly better phylogenetic trees.
M
ATERIALS AND METHODS
We simulated protein sequences by means of Rose
(Stoye et al., 1998). This program allows the simula-
tion of different substitution rates in different positions
with a predetermined spatial pattern. This is a very im-
portant feature for testing the behavior of a program
like Gblocks, which selects from alignments blocks of
contiguous conserved positions with few nonconserved
positions inside. This is the reason why a program that
simulates among-site rate heterogeneity, but not regional
heterogeneity, would not be valid to test the behavior
of Gblocks. Thus, an important preliminary step in our
simulations was the selection from real proteins of spa-
tial patterns of site rates in order to use these parameters
with Rose.
Selection of Evolutionary Rate Patterns
We extracted patterns of rate heterogeneity from
real protein alignments using the program TreePuzzle
(Strimmer and von Haeseler, 1996) with a model of
among-site rate heterogeneity that assumed a Gamma
distribution of rates. This distribution was approximated
with 16 rate categories, which is the maximum number
allowed in TreePuzzle. In particular, we took, from each
position, the category and associated relative rate that
contributed the most to the likelihood. Positions with
rates >1 receive more mutations than the average and po-
sitions with rates <1 receive fewer mutations. This list of
relative rates (whose average should be 1) were given to
Rose to simulate different positions with different rates,
creating conserved and divergent regions with lengths
and boundaries that approximated those of a real pro-
tein. Proteins for extracting rate patterns were NAD2 and
NAD4 (subunits 2 and 4 of the mitochondrial NADH de-
hydrogenase) from several metazoans (Castresana et al.,
1998b), and COG0285 from the COG database, which in-
cludes mainly bacterial sequences (Tatusov et al., 2003).
The three selected profiles produced similar conclusions
regarding the best block selection strategy, and we used
the NAD2 pattern to perform most of the tests. This
pattern contained 361 positions but, after the introduc-
tion of further gaps by the simulation algorithm, the
final simulated alignments reached approximately 400
positions. In order to simulate alignments of different
length, independent simulations obtained with this pat-
tern were concatenated 1, 2, 3, 4, and 8 times to generate
final alignments of, approximately, 400, 800, 1200, 1600,
and 3200 positions, respectively. The PAM evolutionary
model (Dayhoff et al., 1978) was used to simulate the
evolution of amino acids.
Selection of Phylogenetic Trees
Simulations with Rose were performed along phylo-
genetic trees of 16 tips with three different topologies,
a purely asymmetric tree (Fig. 1a), an intermediate tree
(Fig. 1b), and a symmetric tree (Fig. 1c). These known
trees or “real trees” were manually constructed. The av-
erage and maximum length from the root to the tips
was, for the asymmetric tree, 0.89 and 1.30 substitu-
tions/position, respectively. The other trees had very
similar values. The branch lengths of the three trees in
Figure 1 were multiplied by factors of 0.5, 1, and 2, re-
spectively, so that we used in total 9 phylogenetic trees.
These trees had several short internal branches that made
them difficult to resolve; thus, they are trees where the
alignment strategy as well as the phylogenetic algorithm
used were differentially effective. Simpler trees in terms
of longer internodes were easily and equally reproduced
by all methods and were not used here. Similarly, trees
with a total smaller divergence tended to produce con-
served alignments where the alignment method was not
an issue and also not used here. Finally, these trees did
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566 SYSTEMATIC BIOLOGY VOL. 56
FIGURE 1. Asymmetric (a), intermediate (b), and symmetric (c) trees used in the simulations. The scale bar, in substitutions/position,
corresponds to the trees with a divergence ×1.
not contain many closely related sequences, since we
wanted to specifically measure differences in reproduc-
ing the overall shape of the tree and not differences in
recovering the relationships among close sequences.
Gaps Introduced during the Simulations
The Rose program does not have any specific model
for the introduction of gaps along the alignment. Rather,
gaps are introduced with equal probability in all posi-
tions with a relative rate ≥1 (Stoye et al., 1998), which
is a limitation of this program. To try to overcome this
limitation, we used two different gap strategies within
Rose. First, we used a single gap threshold for the whole
alignment. After several trials, we considered a thresh-
old of 0.0007 as a reasonable one for the divergence
levels we analyzed, as deduced from visual inspection
of the alignment (that is, eyeing that blocks of diver-
gence and conservation were not so different from the
real proteins used to construct the rate profiles). Even so,
this threshold tended to produce too many gaps in con-
served regions (not shown). In addition, we also gener-
ated alignments with two different gap thresholds, 0.001
and 0.0001, which we associated, respectively, to diver-
gent and to conserved regions of the profiles. For doing
so, we divided the rate profiles in blocks of homoge-
neous divergence (that is, each block was either mostly
conserved or mostly divergent, which resulted in around
10 to 20 blocks for the different profiles). Then, we did
the simulations for each block separately, and with its
own gap threshold (high for divergent blocks and low for
more conserved blocks). Finally, the different simulated
blocks were concatenated. The phylogenetic results were
similar with both gap strategies, but we mostly worked
with simulations that had the two different gap thresh-
olds, which we considered more realistic. In all cases we
chose a vector of indels of the form [0.5, 0.4, 0.3, 0.2,
0.1], which reflects the relative frequency of indels with
lengths from 1 to 5 amino acids, respectively.
Realignments of Simulated Sequences
Alignments generated by Rose were cleaned fromgaps
and new alignments were reconstructed using ClustalW
version 1.83 (Thompson et al., 1994), Mafft version 5.531
(Katoh et al., 2002, 2005), and Probcons version 1.1 (Do
et al., 2005). Default parameters were used in ClustalW
and Probcons. All defaults were also used in Mafft ex-
cept that a neighbor joining instead of a UPGMA tree was
used as guide tree (option –nj). Alignments were cleaned
from problematic alignment blocks using Gblocks 0.91
(Castresana, 2000), for which two different parameter
sets were used. In one of them, which we call here strin-
gent selection, and which is the default one in Gblocks
0.91, “Minimum Number of Sequences for a Conserved
Position” was 9, “Minimum Number of Sequences for a
Flank Position” was 13, “Maximum Number of Contigu-
ous Nonconserved Positions” was 8, “Minimum Length
of a Block” was 10, and “Allowed Gap Positions” was
“None”. In the second set, which we call relaxed selec-
tion, we changed “Minimum Number of Sequences for
a Flank Position” to 9, “Maximum Number of Contigu-
ous Nonconserved Positions” to 10, “Minimum Length
of a Block” to 5, and “Allowed Gap Positions” to “With
Half”. The latter option allows the selection of positions
with gaps when they are present in less than half of the
sequences.
Original simulated alignments and Mafft realignments
for 30 example simulations (the first five simulations gen-
erated with the symmetric and asymmetric trees) are pro-
vided as supplementary information (available online at
http://systematicbiology.org).
Phylogenetic Reconstruction
Phylogenetic trees from the complete and the two dif-
ferent Gblocks alignments were estimated by ML, NJ,
and parsimony. For ML trees we used the Phyml pro-
gram version 2.4.4 (Guindon and Gascuel, 2003), with
the Jones-Taylor-Thornton model of protein evolution
(Jones et al., 1992) and four rate categories in the Gamma
distribution. The Gamma distribution parameter and
the proportion of invariable sites were estimated by the
program. For NJ trees we used Protdist of the Phylip
package version 3.63 (Felsenstein, 1989) with the Jones-
Taylor-Thornton model to calculate pairwise protein dis-
tances, and Neighbor of the same package to calculate the
NJ tree. For parsimony we used Protpars of the Phylip
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2007 TALAVERA AND CASTRESANA—IMPROVEMENT OF PHYLOGENIES AFTER REMOVING BLOCKS 567
package (Felsenstein, 1989) with 50 random initializa-
tions to ensure a thorough tree search. If no parsimony
tree was obtained, which occurred in less than 1% of the
simulations, the corresponding simulation was totally
excluded from the analysis. When several equally parsi-
monious trees were found, only the first one was used.
We did not do Bayesian trees because of the enormous
computational time required for doing enough number
of generations of all simulations performed.
For each alignment length, alignment strategy, and
phylogenetic method, 300 simulations were run in a grid
of 24 processors. The symmetric difference or Robinson-
Foulds (Robinson and Foulds, 1981) topological distance
from the calculated tree to the real tree was obtained us-
ing Vanilla 1.2 (Drummond and Strimmer, 2001), and the
average of all simulations calculated. This program re-
ports half the number of total discordant clades between
two trees. For bootstrap analyses, 100 bootstraps were
calculated. Due to heavy computational requirements of
the bootstrap analyses, the number of simulations was
reduced to 150. We checked that a higher number of boot-
straps and simulations did not improve the accuracy of
the bootstrap results. Bootstrap values were separately
calculated for right and wrong partitions of the tree with
the help of Bioperl functions (Stajich et al., 2002). Statisti-
cal differences among Robinson-Foulds distances in dif-
ferent alignment conditions were detected by the Tukey-
Kramer test with an alpha level of 0.05 using the JMP
package version 5.1 (SAS Institute, Cary, NC).
R
ESULTS AND DISCUSSION
General Alignment Strategy: Complete versus
Gblocks Alignments
The differences in alignments produced by different
methods can be appreciated in Figure 2. A fragment
of the alignment of simulated sequences (Fig. 2a) was
stripped of gaps and realigned by ClustalW (Fig. 2b),
Mafft (Fig. 2c), and Probcons (Fig. 2d). As it has been
noted before (Higgins et al., 2005), ClustalW tends to
produce more compact alignments. That is, ClustalW
generates many divergent regions that are almost de-
void of gaps, resulting in a relatively simple alignment
(Higgins et al., 2005). This can be clearly appreciated in
the most problematic region in the center of this align-
ment (Fig. 2b). Although Mafft also tends to make align-
ments more compact than the real ones (Fig. 2c), the
deviation from the real situation is not as large as with
ClustalW, at least with default gap penalties. Probcons
TABLE 1. Average number of positions of the complete alignments and the average percentage of positions selected by Gblocks with relaxed
and stringent conditions. Simulation of sequences was done following the asymmetric tree and the heterogeneity pattern of the NAD2 protein
concatenated two times.
ClustalW Mafft Probcons
Total % Gblocks % Gblocks Total % Gblocks % Gblocks Total % Gblocks % Gblocks
Divergence length relaxed stringent length relaxed stringent length relaxed stringent
×0.5 826.6 79.4 54.3 852.5 74.2 51.6 871.8 70.3 50.9
×1 862.4 64.2 42.0 903.7 59.0 39.8 966.4 51.8 37.6
×2 901.8 46.4 30.2 961.7 42.9 28.4 1117.9 34.7 24.5
produces the least compact alignments of the three pro-
grams tested (Fig. 2d). For example, simulations from
asymmetric trees with divergence ×1, which had an av-
erage original length of 1097 positions, were compacted
to an average of 966 positions by Probcons, to 904 posi-
tions by Mafft and to 862 positions by ClustalW (Table 1).
Similar relative degrees of compression were obtained in
other types of simulations.
Gblocks removes problematic regions of a multiple
alignment according to a number of rules. First, blocks
selected for inclusion must be free from a large number
of contiguous nonconserved positions, must be flanked
by highly conserved positions, and must have a mini-
mum length, as controlled by the corresponding param-
eters (see Materials and Methods). In addition, positions
with gaps can be removed either always or only when
more than half of the sequences contain gaps (Castre-
sana, 2000). The latter parameter has a large influence
on the total number of selected positions. We have used
Gblocks in simulated realigned sequences with two dif-
ferent conditions. The condition that we call stringent
does not allow any gap position. The relaxed condition
allows gap positions if they are present in less than half
of the sequences, and it is also less restrictive in the other
parameters (see Materials and Methods). The effect of
the two different parameter sets of Gblocks selection can
be appreciated in Figure 2, for ClustalW (Fig. 2b), Mafft
(Fig. 2c), and Probcons alignments (Fig. 2d). In both cases,
the relaxed parameters (grey blocks) allow the selection
of more positions than the stringent parameters (white
blocks). Table 1 shows the average number of positions of
the complete alignments and the percentage of positions
left after treatment with Gblocks with the two different
parameter sets. Values in this table are for the asymmetric
tree, but similar values were found for other trees.
In order to infer which type of alignment algorithm
(ClustalW, Mafft, or Probcons) and which treatment of
the resulting alignment (no treatment or Gblocks treat-
ment with stringent or relaxed conditions) was best for
phylogenetic analysis, we calculated phylogenetic trees
from all these alignments, and measured the topologi-
cal distance with respect to the real tree. Figure 3 shows,
for the simulations with the asymmetric tree, the aver-
age topological distances to the real tree from the trees
generated with ClustalW alignments, with and with-
out the use of Gblocks. In addition, the distance to the
tree obtained from the Gblocks complementary align-
ment (that is, the alignment resulting after concatena-
tion of all the blocks rejected by Gblocks) is also shown.
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568 SYSTEMATIC BIOLOGY VOL. 56
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DDCTRSGKVKQYFGAQYAA--MGVIYSLIPQCLQVKITSRIDYKNFICAQKACAK-----PG--IPEFGS-------------AG--R---A-SGAESDFGQVDPANKGYKTDRFTVQWQY-RGRGRADIKYHWHACSYQQISA
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DDCTRSGRVQQYFSAQYLS--GGIIYSLIPKCLQVKFTSCIDYKSFICSPAACAD-----SP--ACYADATW----FFQFKLSDG--V----PGNAGSDFPQVDPANEGYKSERFTVQWKY-KAPDRATINHHWSVKTYRAEST
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DDCLRSGNRQQYFTAVYGNL--GVPTSLIPNCLQVKFTSVIQFSTFIYAPPKCP-----QDTPGGA-----------STFSMHVSADSGYSQVEGEN HGLKMGHFDVQW--YRPRARAVIDHHWSALQNR
EDCARSGKVQQYFSAQYMSA--VIIYSLIPQCLQVKFTSCIDYKSLICSPAACG-------EPGTCYADKTWFFQFKLTAGLEGNAGSDFPQVDPANEGYKSERFTVQW-KYKARDRATIQHHWSV
KTYRSQ-SK
DDCTRSGKVQQYFSAQYMIG--GVIYSLIPQCLQVKFTSCINFKSFICPPAACA-----ENLPERC---QFWF----FDTGEGGGAGSDFPQVDPANDGYKAERFTVQW-HY KPRDRAAISHHWSAKSLRKN-SL
DDCTRSGKVQQYFSAQYLGG--GVVYSLIPQCHQVKFTSKIDYKSLICAPAACG-----VDFPANC---QTWF--FGGGGTLSGGAGSDFPQVDPANDGYKAERFTVQW-KYQAKNRASINHHWSAKSYRKK-SP
SDCTRSGKVQQYFTAQYMSQ--
GKICSLIPDCLKVKFTSCLDYKSFNV SAAACG-------DPGTCYAARAWF FQFKLSVGLDGNAGSAYEQASPANEGYKSERFTVQW-KYKARDRATIQHHWSVKVYRRRTTT
DDCTREGRVEQYFSANYRSS--GILYSLILVCLQVKFTACINFKSFSCSPASCG-------TPSLCYADKNWFYQFKLS--VEGNGGSNFPQVDPANDGYKTDRFTVQW-VY KARDRASIKHHWSVDTYR---EG
F
SFFGN
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EDCLRSGKVQQYFSAQYLD---GVGVSLIPQCLQVEFTSRIDFKSFVCHPAECGLSTPAGCAQW------------AEAGGAGSDFPQVDVANSGYKAERFTVQW-QYKTRNRATIDHHRSAKSLPKKS
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TFICSPAACGPPGTCYADKVWFFHFKLSN---GLDGSAGSDFPQVDPAN EGYKSERFTVQW-KYRARDRANIQHHWSVKTYRSQSK
GDCTRAGKVQEYFSAQYLAI--GKAYALIPQCLQVKFTSRIDYKDFICSPGACGAPANCYYNVVWVHQFKLDA---GGSVNAGSDFPRVDPANGGFKKKRFTVQW-KYGARDRVAIEHHWSAKTFRQRSG
NDCTRSGKVQQYFSAQYIGN--AVRTSLIPLCLQVNFTSRSDFKVFACAPAECGDVGLTLPAPRACHVWHFAEGTAHAAANAGT
DFPQIEGANKGYKAERFTVQW--KYVQSRARIVHHWSARTLRKRSL
NDCLRSGKVQVYFSAQYANS--GVKAALIPEALQVKFTSFIDFKSFVCSPAQCGVSLPAGVGPWYNA-ILFPE---GATGGAGSDFPQVEPANNGYKAERFGVQW-AYLTRNRATINHHWSARVLPKKSF
-DCTRSGQVQQYFSAQYKAA--GVVYSLIQQCLQVKFTSRVDYKSFICSPNACGQPARAYYGKT----FKLSA---GVDGNAGSEFLQIDPANDGYKSERFTVQW-KYRAR
DRATIN HHWSVKTYRGQSK
-ECTRSGKVQQFFSPQYITSFFGPIYSIIPQCLQVNFTARIDFKTFVCSKGACGLVAPVTCKEWFFT-----G---GLKGGAGSDYAQVDPANGGYKAERFTVQWPEIKARSRATIDHHWSAKAYHKKSL
DDCLRSGKVQQYFSAQYMGN--GVKASLIPQCLQVKFTSKIDFTSFICVPTECGISLPADCAAWF-----FPD---VDRGGAGSDFPQVDPGNDGYKAEHFTVQW-KYKARNRTTINHHWSAKTL
RKKS
-DCTRSGRVQQYFSAQYLSG--GIIYSLIPKCLQVKFTSCIDYKSFICSPAACADSPACYADATWFFQFKLSD---GVPGNAGSDFPQVDPANEGYKSERFTVQW-KYKAPDRATINHHWSV KTYRAEST
DDCLRSGNRQQYFTAVYGNLG--VPTSLIPNCLQVKFTSVIQFSTFIYAPPKCPQDTPGGASTFS------------MHVSADSGYSQVEGENHGLKMGHFDVQW--YRPRARAVIDHHWSALQNRSFFG
-DCARSGKVQQYFSAQY
MSA--VIIYSLIPQCLQVKFTSCIDY KSLICSPAACGEPGTCYADKTWFFQFKLTA---GLEGNAGSDFPQVDPAN EGYKSERFTVQW-KYKARDRATIQHHWSVKTYRSQSK
-DCTRSGKVQQYFSAQYMIG--GVIYSLIPQCLQVKFTSCINFKSFICPPAACAENLPERCQFWFFD-----T---GEGGGAGSDFPQVDPANDGYKAERFTVQW-HYKPRDRAAISHHWSAKSLRKNSL
-DCTRSGKVQQYFSAQYLGG--GVVYSLIPQCHQVKFTSKIDYKSLICAPAACG---VDFPANCQTWFFGGGG---TLSGGAGSDFPQVDPANDGYKAERFTVQW-KYQAKN
RASINHHWSAKSYRKKSP
-DCTRSGKVQQYFTAQYMSQ--GKICSLIPDCLKVKFTSCLDYKSFNVSAAACGDPGTCYAARAWFFQFKLSV---GLDGNAGSAYEQASPANEGYKSERFTVQW-KYKARDRATIQHHWSVKVYRRRTT
-DCTREGRVEQYFSANYRSS--GILYSLILVCLQVKFTACINFKSFSCSPASCGTPSLCYADKNWFYQFKLS-----VEGNGGSNFPQVDPANDGYKTDRFTVQW-VYKARD RASI
KHHWSVDTYREGSC
L
L
EDCLRSGKVQQYFSAQYLD---GVGVSLIPQCLQVEFTSRIDFKSFV CHPAECGLS-----TPA-GCAQWA-------------EAGGAGSDFPQVDVANSGYKAERFTVQWQ-YKTRNRATIDHHRSAKSLPKKSL
DDCTRSGKVKQYFGAQYAAM--GVIYSLIPQCLQVKITSRIDYKNFICAQKACAKP-----GIPEF-------G--S---A--GRASGAESDFGQVDPANKGYKTD RFTVQWQ-YRGRGRADIKYHWHACSYQQISA
EDCTRSGKVQQYFSAQYMST--GIIC
SLIPQCLQVKFTSCIDYKTFICSPAACGPP-----GTCYADKVWFFHFKLS---N--GLDGSAGSDFPQVDPANEGYKSERFTVQWK-YRARDRANIQHHWSVKTYRSQSK
GDCTRAGKVQEYFSAQYLAI--GKAYA LIPQCLQVKFTSRIDYKDFICSPGACGAP-----ANCYYNVVWVHQFKLD---A--GGSVNAGSDFPRVDPANGGFKKKRFTVQWK-YGARDRVAIEHHWSAKTFRQRSG
NDCTRSGKVQQYFSAQYIGN--AVRTSLIPLCLQVNFTSRSDFKVF
ACAPAECGDVGLTLPAPR-ACHVWH F----AEGTA--HAAANAGTDFPQIEGANKGYKAERFTVQWK-Y-VQSRARIVHHWSARTLRKRSL
NDCLRSGKVQVYFSAQY ANS--GVKAALIPEALQVKFTSFIDFKSFVCSPAQCGVS-----LPA-GVGPWYNAILFP---E--GATGGAGSDFPQVEPANNGYKAERFGVQWA-YLTRNRATINHHWSARVLPKKSF
EDCTRSGQVQQYFSAQYKAA--GVVYSLIQQCLQVKFTSRVDYKSFICSPNACGQP-----ARAYYGKTFK----LS---A--GVD
GNAGSEFLQIDPANDGYKSERFTVQWK-YRARDRATINHHWSVKTYRGQSK
DECTRSGKVQQFFSPQY ITSFFGPIYSIIPQCLQVNFTARIDFKTFVCSKGACGLV-----APV-TCKEWFF----T---G--GLKGGAGSDYAQVDPANGGYKAERFTVQWPEIKARSRATIDHHWSAKAYHKKSL
DDCLRSGKVQQYFSAQYMGN--GVKASLIPQCLQVKFTSKIDFTSFICVPTECGIS-----LPA-DCAAWFF----P---D--VDRG
GAGSDFPQVDPGNDGYKAE HFTVQWK-YKARNRTTINHHWSAKTLRKKSL
DDCTRSGRVQQYFSAQYLSG--GIIYSLIPKCLQVKFTSCIDYKSFICSPAACADS-----PACYADATWFFQFKLS---D--GVPGNAGSDFPQVDPANEGYKSERFTVQWK-YKAPDRA TINHHWSVKTYRAES T
DDCLRSGNRQQYFTAVY GNL--GVPTSLIPNCLQVKFTSVIQFSTFIYAPPKCPQD-----TPG-GASTF-------------SMHVSADSGYSQVEGENH
GLKMGHFDVQW--YRPRARAVIDHHWSALQNRSFFG
EDCARSGKVQQYFSAQYMSA--VIIYSLIPQCLQVKFTSCIDYKSLICSPAACGEP-----GTCYADKTWFFQFKLT---A--GLE GNAGSDFPQVDPANEGYKSERFTVQWK-YKARDRATIQHHWSVKTYRSQSK
D
DCTRSGKVQQYFSAQYMIG--GVIYSLIPQCLQVKFTSCINFKSFICPPAACAEN-----LPE-RCQFWFF----D---T--GEGGGAGSDFPQVDPAND GYKAERFTVQWH-YKPRDRAAISHHWSAKSLRKNSL
DDCTRSGKVQQYFSAQYLGG--GVVYSLIPQCHQVKFTSKIDYKSLICAPAACGVD-----FPA-NCQTWFF----G---GGGTLSGGAGSDFPQVDPANDGYKAERFTVQWK-YQAKNRASINHHWSAKSYRKKSP
SDCTRSGKVQQYFTAQYMSQ--GKICSLIPDCLKVKFTSCLD YKSFNVSAAACGDP-----GTCYAARAWFF
QFKLS---V--GLDGNAGSAYEQASPANE GYKSERFTVQWK-YKARDRATIQHHWSVKVYRRRTT
DDCTREGRVEQYFSANYRSS--GILYSLILVCLQVKFTACINFKSFSCSPASCGTP-----SLCYADKNWF YQF--K---L--SVEGNGGSNFPQVDPANDGYKTD RFTVQWV-YKARDRASIKHHWSVDTYREGSC
d)
FIGURE 2. Fragment of a simulated alignment (a) and the realignment of the same sequences (after gap removal) by ClustalW (b), Mafft
(c), and Probcons (d). The simulation corresponds to an asymmetric tree with divergence ×1. The blocks below each alignment represent the
fragments selected by Gblocks with relaxed conditions (grey blocks) and with stringent conditions (white blocks). Positions of the alignments
where more than 50% of the sequences are identical are shown with black boxes.
Figure 4 represents for each tree (and for two representa-
tive lengths, 800 and 3200 amino acids, as representatives
of single-gene and concatenated-gene phylogenies) the
best alignment strategies after statistically comparing the
average topological distances by means of the Tukey-
Kramer test. An overview of these two figures shows
that, when the alignments are cleaned by Gblocks with
any of the two parameter sets used (dotted lines in Fig-
ure 3), the topological distance to the real tree decreases
with respect to the complete alignment (solid, red line)
in almost all divergences and alignment lengths tested,
and with the three tree reconstruction methods used:
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2007 TALAVERA AND CASTRESANA—IMPROVEMENT OF PHYLOGENIES AFTER REMOVING BLOCKS 569
FIGURE 3. Average Robinson-Foulds distances to the real tree from the tree calculated with ClustalW complete alignments (solid, red line with
crossed symbols), the same alignments after treatment with Gblocks relaxed (dotted, blue line with diamonds) and stringent (dotted, green line
with squared symbols) conditions, and the complementary alignments of the Gblocks relaxed alignment (solid, orange line with triangles). The
asymmetric tree with three different divergence levels was used for the simulations with different alignment lengths. Trees were reconstructed
by ML, NJ, and parsimony.
ML, NJ, and parsimony. The improvement in topolog-
ical accuracy upon Gblocks treatment is more noticeable
for the highest divergences (×2). This is expected since
there are more problematic blocks in these alignments,
as shown by the lower percentage of positions selected
by Gblocks (Table 1). In addition, the improvement from
Gblocks treatment is particularly large for NJ and parsi-
mony. These two methods produce quite poor topologies
when using the complete alignments but, upon using
Gblocks, particularly with the most stringent conditions
(green line, squared symbols), there is a substantial gain
in topological accuracy. ML produces the overall best
trees (see also below) although, in the lowest divergence
(×0.5), there is almost no difference in topological qual-
ity between the Gblocks and the complete alignments.
In fact, for short genes (400 to 800 amino acids) the com-
plete alignment gives rise to better trees than the Gblocks
alignments, although there is no statistical difference be-
tween the complete alignment and the Gblocks align-
ment with relaxed parameters (Fig. 4).
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570 SYSTEMATIC BIOLOGY VOL. 56
FIGURE 4. ClustalW alignment strategies that give rise to the statistically best topologies. When two or more strategies do not show statistical
differences in Robinson-Foulds distances, all equivalent strategies are represented. The complete alignment is represented by a black block, and
the relaxed and stringent Gblocks strategies by grey and white blocks, respectively.
It is thus shown from the example above that the re-
moval of divergent and problematic regions of an align-
ment is, in principle, beneficial for phylogenetic analyses
of relatively divergent sequences. In fact, it is true, as pre-
viously argued (Aagesen, 2004; Lee, 2001), that there is
some phylogenetic information in the blocks removed
by methods like Gblocks. This can be appreciated in Fig-
ure 3, which shows the topological distances to the real
trees from the trees obtained with the blocks excluded by
Gblocks (complementary alignment; solid, orange line).
These distances, although very large, become quite re-
duced for long alignments, indicating that trees obtained
from the complementary regions are not random; that is,
there is some phylogenetic information in the regions re-
jected by Gblocks. However, what seems to matter is not
the total phylogenetic signal but the signal-to-noise ratio.
Despite the relatively simple simulations performed, re-
gions excluded by Gblocks seem to add more noise than
signal, thus lowering the quality of the trees from the
complete alignments with respect to the Gblocks-cleaned
alignments.
Similar conclusions about the beneficial effect of
Gblocks can be drawn from Mafft alignments of the same
asymmetric trees (Figs. 5 and 6). In this case, Gblocks is
not an advantage over the complete alignment in the two
most conserved alignments (×0.5 and ×1) when using
the ML method although, again, Gblocks relaxed and
the complete alignments are not statistically different.
The picture for Probcons (Fig. 1 of the online Appendix,
available at http://systematicbiology.org) is similar to
that for Mafft. Figure 2 of the online Appendix shows
a comparison of the three alignment programs with de-
fault gap costs, using the trees produced after Gblocks
cleaning with relaxed conditions. Under the conditions
of these simulations, ClustalW is slightly worse, regard-
ing the trees produced, than the two other programs. The
performances of Mafft and Probcons are very similar, and
only for NJ and parsimony Probcons alignments work
slightly better. Probcons, however, is highly demand-
ing in computational time. Thus, for the rest of the tests
we only compared the performances of ClustalW and
Mafft.
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2007 TALAVERA AND CASTRESANA—IMPROVEMENT OF PHYLOGENIES AFTER REMOVING BLOCKS 571
FIGURE 5. Average Robinson-Foulds distances to the real tree from the tree calculated with Mafft complete alignments (solid, red line with
crossed symbols), the same alignments after treatment with Gblocks relaxed (dotted, blue line with diamonds) and stringent (dotted, green line
with squared symbols) conditions, and the complementary alignments of the Gblocks relaxed alignment (solid, orange line with triangles). The
asymmetric tree with three different divergence levels was used for the simulations with different alignment lengths. Trees were reconstructed
by ML, NJ, and parsimony.
The results for the symmetric and intermediate trees of
both alignment algorithms are shown in the correspond-
ing columns of Figures 4 and 6 for the ClustalW and
Mafft methods, respectively (and in Figures 3 to 6 in the
online Appendix for all alignment lengths). Two results
are noteworthy from these analyses. First, differences
in phylogenetic performance between different align-
ments derived from symmetric trees are quantitatively
smaller, in agreement with a previous work (Ogden and
Rosenberg, 2006). See, for example, the similarity of the
three graphs of ML trees of ClustalW alignments (Fig. 3
in the online Appendix). Second, in these trees there are
two conditions where the Gblocks alignments produce
ML trees that are statistically worse than the complete
alignments: the symmetric and intermediate trees of di-
vergence ×1 with Mafft alignments of 800 amino acids
(Fig. 6). These are the only two conditions where we ob-
served this. However, we do not think that this justifies
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572 SYSTEMATIC BIOLOGY VOL. 56
FIGURE 6. Mafft alignment strategies that give rise to the statistically best topologies. When two or more strategies do not show statistical
differences in Robinson-Foulds distances, all equivalent strategies are represented. The complete alignment is represented by a black block, and
the relaxed and stringent Gblocks strategies by grey and white blocks, respectively.
not using Gblocks in these types of trees, even if we
could know the shape of the tree in advance. In real
alignments, evolution must be much more complex than
what we simulated. For example, we did not simu-
late biased amino acid compositions (Castresana et al.,
1998a) or different models of evolution in different parts
of trees (Philippe and Laurent, 1998), all of which will
have stronger biasing effects in nonconserved blocks. Be-
cause the difference in topological accuracy between the
Gblocks and the complete alignments is very small in
these two conditions, it is very likely that the addition of
any of these effects in the simulations would have made
both the Gblocks relaxed and complete alignments of at
least equal performance.
All simulations shown so far were performed follow-
ing a pattern of rate variation of the NAD2 protein. To
test the influence of different rate patterns, we used in
the simulations profiles derived from two other proteins
(NAD4 and COG0285). From the Mafft alignments of
these simulations we calculated the corresponding ML
trees (Fig. 7 in the online Appendix). Different patterns
(and thus different percentages of block selection) gave
rise to different performances of the complete and the
Gblocks alignments, but the results were similar in rela-
tive terms. We also tested the performance of a different
gap model, in which gaps were introduced homoge-
neously along the alignment, instead of using two differ-
ent gap thresholds in different regions of the alignments
(see Materials and Methods). The results were again sim-
ilar with the simpler gap strategy, as shown for the ML
reconstruction of the asymmetric trees (Fig. 8 of the On-
line appendix).
Phylogenetic Methods Used
The data shown above indicate that ML is the phyloge-
netic method that best extracts reliable information from
problematic alignment regions, since trees derived from
complete alignments are relatively good. This contrasts
with the trees obtained by NJ and parsimony, which are
quite poor from the complete alignments, indicating that
they greatly benefited from the use of Gblocks. ML is also
the method that produces the overall best trees, in agree-
ment with previous simulation analysis (see references
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2007 TALAVERA AND CASTRESANA—IMPROVEMENT OF PHYLOGENIES AFTER REMOVING BLOCKS 573
FIGURE 7. Average Robinson-Foulds distances to the real tree from the tree calculated with Mafft complete (solid line, solid symbols) and
ClustalW complete alignments (solid line, empty symbols). The tree distances obtained with the same alignments after treatment with Gblocks
with relaxed conditions (dotted lines) are also shown. Trees were reconstructed by ML (circles), NJ (squares), and parsimony (triangles). The
most divergent asymmetric tree was used for the simulations.
in Felsenstein, 2004). To show this, Figure 7 presents the
superimposed graphs for the most divergent asymmet-
ric tree as an example. The better performance of ML
in all alignment conditions is clearly appreciated in this
graph.
Short versus Long Alignments
Alignment length turned out to be a very important
factor to be taken into account when deciding the best
alignment cleaning strategy. Figures 3 and 5 show that,
in general, for shorter alignments the best Gblocks con-
dition is the relaxed one, whereas for longer alignments
the stringent condition tends to work better. This can also
be appreciated by comparing the slopes of the graphs
corresponding to the complete alignments, and those of
the Gblocks alignments with relaxed and stringent con-
ditions. The slope downwards (towards better trees) is
less pronounced for the complete alignments and more
pronounced for Gblocks with stringent conditions. This
means that for single genes (400 to 800 amino acids) the
gain in signal-to-noise ratio after elimination of prob-
lematic blocks may not compensate the total loss of in-
formation. However, for longer alignments, for example,
those used in phylogenomic studies where several genes
are concatenated (Delsuc et al., 2005; Jeffroy et al., 2006),
there is enough total information so that selecting the
best pieces with Gblocks using the stringent conditions
allows to get closer to the real tree. This basic tendency
is observed under all simulation conditions we tested.
Bootstrap Support in Trees Obtained
from Gblocks Alignments
Previous performance tests of Gblocks with real data
showed that Gblocks alignments obtained less support
in ML analysis, because the number of trees not sig-
nificantly different from the ML tree was smaller in
the complete alignment than in the Gblocks alignment
(Castresana, 2000). Later, in numerous studies in our
group and in other groups, the same effect was observed
using bootstrap values of NJ trees, which were lower
in the Gblocks alignments. Our simulations reproduced
the same behavior again. In NJ trees obtained from 100
bootstrap samples, the average bootstrap support of all
partitions was higher for the complete alignments, and
lower for Gblocks alignments (Fig. 8). However, the same
simulations (see topological distances of NJ trees in Fig-
ures 3 and 5) showed that the best trees were obtained
with Gblocks conditions and the worse topologies with
the complete alignments, thus following the opposite di-
rection, regarding quality, to the bootstrap values, at least
for the maximum divergence. A similar trend was found
for NJ trees of simulations with symmetric trees (Fig. 9
of the online Appendix) and for bootstrapped ML trees
(Fig. 10 of the online Appendix). One may think that the
bootstraps of Gblocks trees are lower due to the smaller
length of the Gblocks alignments, but it is still very para-
doxical that the best topology is associated to a lower
bootstrap.
The explanation for this contradictory behavior of
Gblocks may be that divergent and problematic align-
ment regions are biased towards an erroneous topology
(Lake, 1991). This could happen if the initial guide tree
used in the progressive alignment methods is conducting
very strongly the alignment in the divergent and most
gappy regions, where alignment programs may easily
create similarity at the expense of homology (Higgins
et al., 2005). In addition, when alignment software is
faced with an ambiguous alignment decision, the algo-
rithmic solution makes consistent but arbitrary decisions
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574 SYSTEMATIC BIOLOGY VOL. 56
FIGURE 8. Average bootstrap values of NJ trees obtained from ClustalW (a) and Mafft (b) alignments simulated from the asymmetric tree
with three different divergence levels. Complete (solid, red line), Gblocks relaxed (dotted, blue line with diamonds), and Gblocks stringent
(dotted, green line with squared symbols) alignments are shown.
FIGURE 9. Average Robinson-Foulds distances from the ClustalW guide tree to the real tree (red line with crossed symbols), from the guide
tree to the NJ tree of the Gblocks alignment with relaxed conditions (green line with squared symbols), and from the guide tree to the NJ tree
of the complementary positions of the same Gblocks alignment (blue line with diamonds). The asymmetric tree with three different divergence
levels was used for the simulations.
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2007 TALAVERA AND CASTRESANA—IMPROVEMENT OF PHYLOGENIES AFTER REMOVING BLOCKS 575
that bias the support indices. That is, this repeated align-
ment decisions will increase the bootstrap support, and
this bias will be stronger in the most divergent regions,
where there is more uncertainty. Three results are con-
sistent with this possibility. Firstly, we have observed
in our simulations that the initial guide dendrogram
used by ClustalW is indeed very different from the real
tree, as measured by the Robinson-Foulds distance of
both trees (Fig. 9). If all divergent regions tend to eas-
ily reproduce this initial dendrogram, we would expect
that the guide tree is more similar to the tree obtained
from the Gblocks excluded regions than to the Gblocks
alignment. Figure 9 shows that this is the case, partic-
ularly in the most divergent simulations. Secondly, we
see that the effect of increased bootstrap support in the
complete alignment with respect to the Gblocks align-
ments is higher in ClustalW, which highly depends on
the initial dendrogram, than in Mafft (Fig. 8). For exam-
ple, in simulations of 400 amino acids and at ×2 diver-
gence, there is an increase from 60% to 76% bootstrap
support in ClustalW when comparing the Gblocks strin-
gent and complete alignments, and only from 60% to
70% in Mafft. In the latter method, the successive it-
erations of the alignment algorithm may make the fi-
nal alignment more independent from the initial crude
dendrogram, thus explaining that trees generated from
these alignments are slightly less biased. And thirdly,
when we calculated separately bootstraps of right and
wrong partitions for each tree we observe, apart from
lower values for wrong partitions, a slightly higher bias
in them (Fig. 11 of the online Appendix). The bias is
also present in the right partitions, probably because
some of the recurrent software decisions in the diver-
gent regions are actually correct. Thus, the bias coming
from divergent regions seems to increase the bootstrap
of all partitions, although the effect is slightly larger in
the wrong ones. All this indicates that bootstrap sup-
port cannot be used as a measure of reliability of the
tree topology when divergent regions are present in the
alignment.
C
ONCLUSIONS
We have shown, under the conditions of these simu-
lations, that the information contained in divergent and
ambiguously aligned regions of multiple alignments is,
in general, not beneficial for phylogenetic reconstruction.
Thus, using Gblocks or a similar method for removing
problematic blocks seems to be justified for phylogenetic
analysis, particularly for divergent alignments. In this
work, we have used simulations of moderately diver-
gent and very heterogeneous proteins, which are typ-
ically used in deep phylogenies (i.e., bacterial groups,
eukaryotes lineages, metazoan phyla). However, we do
not know how removal of blocks would affect more con-
served and less heterogeneous alignments. We have also
not tested how a finer tuning of parameters of align-
ment programs and Gblocks may improve the phyloge-
nies. Although we have only used protein alignments,
the same conclusions are expected to apply to protein-
coding DNA alignments of similar divergence. On the
other hand, although we predict that the general con-
clusion that ambiguously aligned regions in any data set
are best excluded when they provide more noise than sig-
nal, rRNA alignments as well as alignments from non-
coding DNA have very different features from coding
alignments, and our simulations were not specifically
designed to explore the properties of these kinds of se-
quences. However, our purpose in this work is not giving
strict rules about the best alignment strategy and asso-
ciated parameters. Rather, our simulations are mainly
informative about general tendencies. Thus, in the fol-
lowing we summarize important tendencies observed in
our simulations and give some general rules regarding
the best alignment strategy that can be applied to real
situations of protein alignments.
NJ and parsimony seem to be unable to extract
useful phylogenetic information from the problematic
alignment regions, because the complete alignments are
always much worse than the Gblocks treated alignments,
so using Gblocks seems particularly advisable for these
methods. Most probably, these two methods are not able
to take into account the multiple substitutions that oc-
cur in these excessively saturated blocks. On the other
hand, ML, less affected by saturation, is able to extract
some information from these blocks, since in some condi-
tions the complete alignments are similar or even better
than the Gblocks alignments. However, the misidenti-
fied homology that may occur in these regions affects
all phylogenetic methods, which may explain why us-
ing Gblocks is more beneficial at high divergences for all
methods.
Regarding the use of stringent or relaxed conditions
for Gblocks, two important rules can be extracted from
our analysis. First, for ML trees relaxed conditions of
Gblocks seem to give rise to better trees, whereas for NJ
and parsimony stringent conditions are better. Second,
alignment length is a crucial parameter to be taken into
account. For short alignments, such as in studies of sin-
gle short genes, the removal of blocks by Gblocks may
leave too few positions, so in these cases it may be better
to use very relaxed conditions of Gblocks. In the short-
est alignments, which have very little information, use
of Gblocks may be even detrimental. At any rate, one
should be aware that with this type of short alignments
it is only possible to obtain a very approximate topology,
possibly quite distant from the real tree. For phyloge-
nomic studies, where there is enough information from
the concatenation of several genes (Jeffroy et al., 2006),
the use of Gblocks with stringent conditions tends to give
rise to the best phylogenetic trees.
A
CKNOWLEDGMENTS
This work was supported financially by a research grant in bioinfor-
matics from the Fundaci´on BBVA (Spain), and grant number BIO2002-
04426-C02-02 from the Plan Nacional de Investigaci´on Cient´ıfica,
Desarrollo e Innovaci ´on Tecnol´ogica (I+D+I) of the MEC, cofinanced
with FEDER funds. We thank V. Soria-Carrasco for useful technical as-
sistance, and three anonymous reviewers, K. Kjer, and R.D.M. Page for
critical comments that helped improve the manuscript.
Downloaded By: [USYB - Systematic Biology] At: 02:07 18 July 2007
576 SYSTEMATIC BIOLOGY VOL. 56
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First submitted 7 February 2007; reviews returned 6 March 2007;
final acceptance 24 March 2007
Associate Editor: Karl Kjer
Editors: Rod Page and Jack Sullivan
