BioSystems 101 (2010) 59–66
Contents lists available at ScienceDirect
BioSystems
journal homepage: www.elsevier.com/locate/biosystems
Comparative protein analysis of the chitin metabolic pathway in extant
organisms: A complex network approach
Aristóteles Góes-Neto a,∗ , Marcelo V.C. Diniz a , Leonardo B.L. Santos b , Suani T.R. Pinho b ,
José G.V. Miranda b , Thierry Petit Lobao c , Ernesto P. Borges d , Charbel Niño El-Hani e ,
Roberto F.S. Andrade b
a
Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, Feira de Santana, Bahia 44036-900, Brazil
Instituto de Física, Universidade Federal da Bahia, Campus Universitário de Ondina, Salvador, Bahia 40170-110, Brazil
c
Instituto de Matemática, Universidade Federal da Bahia Campus Universitário de Ondina, Salvador, Bahia 40170-110, Brazil
d
Escola Politécnica, Universidade Federal da Bahia, Rua Prof. Aristides Novis, 02 - Federação, Salvador, Bahia 40210-630, Brazil
e
Instituto de Biologia, Universidade Federal da Bahia, Campus Universitário de Ondina, Salvador, Bahia 40170-110, Brazil
b
a r t i c l e
i n f o
Article history:
Received 12 May 2009
Received in revised form 25 March 2010
Accepted 19 April 2010
Keywords:
Chitin
Comparative genomics
Complex networks
a b s t r a c t
Chitin is a structural endogenous carbohydrate, which is a major component of fungal cell walls and
arthropod exoskeletons. A renewable resource and the second most abundant polysaccharide in nature
after cellulose, chitin is currently used for waste water clearing, cosmetics, medical, and veterinary applications. This work comprises data mining of protein sequences related to the chitin metabolic pathway
of completely sequenced genomes of extant organisms pertaining to the three life domains, followed by
meta-analysis using traditional sequence similarity comparison and complex network approaches. Complex networks involving proteins of the chitin metabolic pathway in extant organisms were constructed
based on protein sequence similarity. Several usual network indices were estimated in order to obtain
information on the topology of these networks, including those related to higher order neighborhood
properties. Due to the assumed evolutionary character of the system, we also discuss issues related to
modularity properties, with the concept of edge betweenness playing a particularly important role in
our analysis. Complex network approach correctly identifies clusters of organisms that belong to phylogenetic groups without any a priori knowledge about the biological features of the investigated protein
sequences. We envisage the prospect of using such a complex network approach as a high-throughput
phylogenetic method.
© 2010 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
The rapidly developing theory of complex networks, based on
both graph theory and statistical mechanics, has been successfully
applied to uncover the organizing principles that govern the formation and evolution of various complex biological, technological,
and social systems (Barabasi and Oltvai, 2004).
A key challenge of contemporary biology is to carry out an
integrated theoretical and experimental program to map out,
understand, and model, in quantifiable terms, the topological and
dynamic properties of diverse biological networks (Barabasi and
∗ Corresponding author at: Universidade Estadual de Feira de Santana, Departamento de Ciências Biológicas, Av. Transnordestina, s/n, Bairro Novo Horizonte, Feira
de Santana, Bahia 44036-900, Brazil. Tel.: +55 7532248296; fax: +55 7532248132.
E-mail addresses: arigoesneto@pq.cnpq.br, arigoesneto@gmail.com,
arigoesneto@gmail.com, agoesnt@uefs.br (A. Góes-Neto).
URL: http://www.uefs.br/ppgbiotec (A. Góes-Neto).
0303-2647/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved.
doi:10.1016/j.biosystems.2010.04.006
Oltvai, 2004). Systems biology is concerned with phenomena that
arise when a number of interaction partners operate in complex
networks, and not only function individually (Stoll and Naef, 2004).
There is, therefore, a great heuristic potential in applying the theory of complex networks in the context of systems biology, as we
intend to do in our research project.
Recent studies using complex network approach in the fields
of both genomics and proteomics (Gavin et al., 2004; Boone et
al., 2007) have contributed to a better knowledge of the structure and dynamics of the complex webs of interactions of a
living cell. Although molecular biological networks are intricately
interconnected and interwoven inside an organism, at least three
distinct molecular networks can be distinguished: protein interaction, transcriptional (or gene regulatory), and metabolic networks.
Furthermore, proteins are also evolutionarily related through their
phylogeny (Silva and Stumpf, 2005).
Several features of complex systems can be identified and
understood through the recently developed complex network
framework (Albert and Barabási, 2002; Boccaletti et al., 2006; Costa
60
A. Góes-Neto et al. / BioSystems 101 (2010) 59–66
et al., 2007). As such systems contain a large number of variables,
the use of functional relationships among their constituents allows
us to construct an interaction network, which can offer a first indication of how the system is structured. Of course, the identification
of the proper functional relationships, which are responsible for
assigning the edges between nodes, is the main and crucial step in
this methodology. To use the simplest framework, we consider a
non-weighted, undirected complex network R with N nodes and E
edges. This means that no edge carries more information than the
others, and that if node i is connected to node j, then, node j is also
connected to node i.
In most of the studies that use network concepts, the properties of the systems are expressed by several parameters that
describe some topological properties of the network, such us the
average degree k, the clustering coefficient C, the mean minimal distance among the nodes d, and the network diameter D.
The degree ki of a node i counts the number of edges connected
to it, while k is the average number of edges per node over the
network.
The clustering coefficient Ci of node i is defined as the ratio
between the number of edges among the immediate neighbors of i
and ki (ki − 1)/2, which is the maximum number of edges between
the set of neighbors of i. Again, the average of Ci over i leads to the
network clustering coefficient C.
A finer description of the relationship among the nodes can be
expressed by the distribution function of node degree p(k), which
counts the relative number of nodes with a degree k.
If two nodes, i and j, are directly connected, the minimal distance
between them is 1. If this is not the case, they can be connected by
a path of edges in the network. More than one distinct path can be
used to go from i to j, but the shortest path di,j corresponds to the
path with the smallest number of edges connecting those nodes. If
two nodes i and j are not connected, the distance between them is
not well defined and, for the purpose of going running the process,
we define di,j = 0. The average shortest path di of node i is the mean
value, over j, of di,j , while the network average shortest path d is
obtained by averaging di over the whole set of network nodes.
Finally, the network diameter D is the largest value of di,j .
If any pair of nodes, i and j, can be connected through a path
over network edges, the network is formed by a single cluster. If
this is not the case, the network is split into several sub-graphs
or sub-networks, each one of them constituted by one cluster. If
this is the case, the further analysis of the network depends on
the way the sub-graphs are constituted. In many situations, as in
the network we consider in this work, it turns out that there is
only one giant cluster, which has a much more number of nodes
than all the other sub-graphs The identification and characterization of the largest cluster in the network, also called the largest
connected component, become quite relevant, as it displays the
topological features shared by the largest number of nodes in the
network.
A measure of the importance of a given edge between two nodes,
i and j, to the network structure is provided by the edge betweenness degree. It counts the number of shortest paths between the
N(N − 1)/2 pairs of nodes that go through that edge. If we successively eliminate the edges with highest values of betweenness
degree, as proposed by Newman and Girvan (Newman and Girvan,
2004), it is possible to identify community structures in networks
that are characterized by large modularity. This concept indicates
that a network is composed by several communities (or modules).
The nodes within each community are densely connected with
each other, but they are sparsely connected with nodes of other
communities.
Here, we used a complex network approach to investigate the
chitin metabolic pathway in a phylogenetic framework as it is
shown in Fig. 1. Chitin, the -1,4-linked linear homopolymer of Nacetylglucosamine, is a structural endogenous carbohydrate, which
is a major component of fungal cell walls (Bowmann and Free,
2006), cephalopod beaks (Hanlon and Messenger, 1996), integuments of larvae and young nematodes (Ax, 1996), and arthropod
exoskeletons (Merzendorfer, 2006). Chitin occurs only in extant
eukaryotic organisms of the Metazoa-Fungal clade. This suggests
that chitin may have evolved before the crown eukaryotic radiation. The same can be said of cellulose, which is highly similar
to chitin (Merzendorfer, 2006) and is found in eukaryotic cell
walls of Viridiplantae (plants and green algae) (Raven et al.,
Fig. 1. Reference pathway for chitin metabolism.
A. Góes-Neto et al. / BioSystems 101 (2010) 59–66
61
Table 1
Enzymes of the chitin metabolic pathway (E = Eukarya, B = Bacteria, A = Archaea; E.C. = Enzymatic classification. Numbers in parentheses after letters are the total of organismal
individual sequences per domain of each protein).
Protein
E.C. number
Metabolic role
Domain (#)
Phosphoglucoisomerase
glucosaminephosphate isomerase
phosphoglucosamine acetylase
acetylglucosamine phosphate deacetylase
acetylglucosamine phosphomutase
UDP-acetylglucosamine pyrophosphorylase
Chitin synthase
Chitinase
chitin deacetylase
Hexosaminidase
acetylglucosamine kinase
Chitosanase
hexokinase type IV glucokinase
5.3.1.9
2.6.1.16
2.3.1.4
3.5.1.25
5.4.2.3
2.7.7.23
2.4.1.16
3.2.1.14
3.5.1.41
3.2.1.52
2.7.1.59
3.2.1.132
2.7.1.1
Synthesis
Synthesis
Synthesis
Synthesis
Synthesis
Synthesis
Synthesis
Degradation
Degradation
Degradation
Activation
Degradation
Activation
E(16), B(472), A(12)
E(23), B(285), A(5)
E(3)
B(170), A(6)
E(5)
E(2), B(324), A(2)
E(22)
E(7), B(57)
E(2), B(25)
E(3), B(235)
E(1)
B(1)
E(2), B(15)
2004), and some protists, such as Oomycota (Alexopoulos et al.,
1996).
Chitin is synthesized by a sequence of six successive reactions:
(i) conversion of Glu-6P into Fru-6-P by phosphoglucoisomerases
(E.C. 5.3.1.9); (ii) conversion of Fru-6-P into GlcN-6-P by glucosaminephosphate isomerases (E.C. 2.6.1.16); (iii) acetylation of
GlcN-6-P generating GlcNAc-6-P by phosphoglucosamine acetylases (E.C. 2.3.1.4), (iv) interconversion of GlcNAc- 6-P into
GlcNAc-1-P by acetylglucosamine phosphomutases (E.C. 5.4.2.3) or,
alternatively, by acetylglucosamine phosphate deacetylases (E.C.
3.5.1.25); (v) uridilation of GlcNAc-1-P by UDP-acetylglucosamine
pyrophosphorylases (E.C. 2.7.7.23); and (vi) conversion of UDPGlcNAc into chitin by chitin synthases (E.C. 2.1.4.16) (Mio et al.,
1998; Lagorce et al., 2002).
Chitin degradation is achieved by chitinases (E.C. 3.2.1.14),
either by exochitinases, which convert chitin into Nacetylglucosamine residues, or by endochitinases, which convert
chitin into chitobiose, which, in turn, may be converted into
N-acetylglucosamine residues by hexoaminidases (E.C. 3.2.1.52).
N-acetylglucosamine residues may be activated by acetylglucosamine kinases - acetylglucosamine-6-P, restoring the precursor
of the short feedback cycle of chitin metabolism. Chitin may also
be deacetylated by chitin deacetylases (E.C. 3.5.1.41), converted
into chitosan, which is degraded by chitosanases (E.C. 3.2.1.132)
into glucosaminide, which, when converted into glucosamine,
may be activated by hexokinase type IV glucokinases (E.C.
2.7.7.1), which restore the precursor of N-acetylglucosamine-6-P,
Glucosamine-6-P, configuring a longer feedback cycle (Pirovani et
al., 2005).
In this paper, we use the complex network approach as a theoretical and methodological tool to perform a comparative study
of the enzymes related to the chitin metabolic pathway in extant
organisms of the three life domains, Archaea, Bacteria, and Eukarya,
and to explore how the information derived from network structure and statistics can be used to uncover and explain biological
patterns.
2. Material and Methods
Our database was composed by all the protein sequences corresponding to the
enzymes of the chitin metabolic pathway of completely sequenced genomes of
extant organisms pertaining to the domains Archaea, Bacteria, and Eukarya from
Genebank, NCBI (Benson et al., 1999), at May 19th, 2007.
Each individual protein sequence was stored in a single file containing the protein sequence itself and all the relevant associated information, such as indexers,
molecular source, structural and functional information, and complete taxonomic
classification of the organism from which the sequence was derived. Protein
sequences were initially categorized based on two criteria: (i) E.C. number (enzymatic classification) and (ii) presence or absence in seven distinct groups, according
to all possible combinations [in only one of the three domains (3), in two domains
(3), or in all the three domains (1)].
Similarity comparison of all protein sequences with each other was performed
by using BLAST 2.2.15 (Altschul et al., 1997), and three indexes were extracted, a
similarity index (%), score (bits), and e-value (probability). Then, according to the
similarity level between protein sequences, a similarity matrix was constructed and
submitted to symmetrization.
The adjacency matrix, constructed based on the information of the symmetrical
similarity matrix was used to generate complex networks for enzymatic classes as
well as one network containing all the sequences. In each of these complex networks, a vertex represents one protein sequence. As will be discussed in the next
Section, two vertices are connected by an edge when the similarity degree between
the two corresponding protein sequences, measured by the BLAST software, is larger
than a similarity threshold. Statistical indexes of the networks, such as degree distribution, clustering coefficient, average path-length, and edge betweenness, were
evaluated from the analysis of neighborhood matrices, which present explicitly
the neighborhood order associated with each node of the network (Andrade et al.,
2006).
All the programs were executed on LINUX-and WINDOWS-running computers,
MySQL was used as database, and scripts and auxiliary programs were written in
PERL, C, and FORTRAN 77. PAJEK (Batagelj et al., 2003) was used to generate network
images.
3. Results and Discussions
A total of 1695 protein sequences corresponding to the 13
enzymes of the chitin metabolic pathway were retrieved from
Genebank (Table 1). Only proteins from completely sequenced
organisms were used in order to guarantee the retrieval of all
possible isoforms of the 13 proteins in each sampled organism.
The remarkably higher numbers of bacterial records in some protein types reflected the fact that there are much more completely
sequenced organisms of the domain Bacteria in comparison with
the other two domains, Archaea and Eukarya.
Although sequences from eukaryotic representatives were not
found in two of the protein groups (E.C. 3.5.1.25 and E.C. 3.2.1.132),
this does not mean that they are absent from eukaryotic organisms, but simply reflects that they were not found in the completely
sequenced eukaryotic organisms until the date in which we
downloaded the database used in the present work. The whole
biochemical repertoire to carry out chitin synthesis and degradation, even in the absence of these two proteins, is only found in
eukaryotic organisms.
As previously pointed out, the protein network was set up with
the help of the concept of protein similarity, which can be quantified by the BLAST software. This software provides three distinct
measures of the proximity between the proteins: similarity, evalue, and score. The results in this work are primarily based on the
similarity value, which varies in the range [0,100], in an ascending
way, according to how close two proteins are.
Each pair i,j of proteins in the selected set was probed by the
BLAST software, in a directed way. This results in an evaluation of
the protein similarity for each of the pairs (i,j) and (j,i). These results
were stored in a similarity matrix S.
A. Góes-Neto et al. / BioSystems 101 (2010) 59–66
62
Fig. 4.
Fig. 2.
Several networks were set up and analyzed based on the matrix
S. We selected different threshold values Smin, and constructed a
network R(Smin ) by imposing the following condition: set an edge
between nodes i and j if, and only if, Sij ≥ Smin.
The data obtained indicates a slight degree of asymmetry
between components Sij and Sji. The probability to find an asymmetry of the components of an adjacency matrix with 40 ≤ Smin ≤ 60
(the more interesting region, according to our study) was smaller
than 1%. Then, the use of an undirected network was justified. Consequently, the matrix was made symmetrical based on the highest
value of a pair Sij and Sji .
In Fig. 2, we show the frequency distribution of similarity
degrees for the whole set of proteins and for the subset of UDPacetylglucosamine pyrophosphorylase (E.C. 2.7.7.23), which was
Fig. 3.
Fig. 5.
A. Góes-Neto et al. / BioSystems 101 (2010) 59–66
63
Fig. 6.
thoroughly explored by us in a case study also reported here. Both
of them show double peaks and the mean value is in the interval
(40–50).
In Fig. 3, we show the results for the number of edges L of networks constructed by the method explained above as function of
Smin , again for the whole set of proteins and for the subset of UDPacetylglucosamine pyrophosphorylase (E.C. 2.7.7.23). We adopted
a sigmoidal function since it properly fitted the data, and subsequently, evaluated its inflexion point.
In Table 2, we show, for some subsets of proteins of the chitin
metabolic pathway, the similarity mean value S and the inflexion point of the sigmoid curve. The correlation between these two
values is very impressive, and they are related to the fact that perturbations in the value of Smin produce a major effect, through the
removal or inclusion of a large number of edges precisely in the
vicinity of the mean value of similarity.
We will now turn to the discussion of our results for some network measures, more specifically, for the values of C and d, for
the whole protein set and for the subset of UDP-acetylglucosamine
pyrophosphorylase (E.C. 2.7.7.23) (Fig. 4a, b).
Table 2
Relationship between similarity mean value S and the inflexion point of the sigmoid curve for some subsets of enzymes of the chitin metabolic pathway.
Protein
Ss
Inflexion point
UDP-acetylglucosamine
pyrophosphorylase
Hexosaminidase
Hexokinase type IV glucokinase
Acetylglucosamine phosphate deacetylase
Chitinase
Glucosaminephosphate isomerase
41.27
42.66
32.48
33.38
32.44
31.66
38.55
34.27
34.26
32.64
31.79
38.44
These results show first, for Smin < 40%, a continuous change in
C and d. In this region, the networks consist of single clusters,
and the removal of some edges due to the increase of Smin does not
change much of their topologies. C remains almost constant, while
d increases smoothly as, on average, a larger number of steps is
required to connect a pair of nodes. For Smin > 40%, some isolated
nodes and small clusters begin to appear, but the largest cluster still
dominates the scene. For Smin ranging from 51 to 54, a sudden transition in network properties occurs, what suggests to call an interval
of critical values of Smin . This transition can be revealed by the sharp
decrease of d, which is related to a division of the network into
two large clusters, each with roughly half the number of nodes of
the previously largest cluster. At the same time, we note that C
remains large, indicating that, inside each of the clusters, the nodes
continue to be highly interconnected. Nevertheless, the effect of
network splitting is also revealed by the change in the derivatives
of both C and d as function of Smin , as it may be seen in Fig. 4.
These results are corroborated by the findings presented in
Fig. 5, where we show the size of the largest cluster as a function of
Smin , for the networks constructed with the whole set of proteins.
For the purpose of obtaining the desired phylogenetic classification of the organisms in the considered data basis, it is important to
consider those networks that are close to the region of critical values of Smin , i.e., at those values where the network topology changes
abruptly. The disruption of the whole network into clusters that
characterizes the critical region allows us to identify the distinct
communities that are entailed in the set of considered organisms.
As our results will show, such communities are related to the individual or composite classes of organisms to which the proteins
belong. The fact that the critical region extends over a finite interval of Smin value (instead of a single point, as one might expect) is
related to the fact that the different communities do not split from
the largest cluster at once.
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A. Góes-Neto et al. / BioSystems 101 (2010) 59–66
Fig. 7.
This fact can be further illustrated by Figs. 6 and 7, which
display finer details of the network built for the subset of UDPacetylglucosamine pyrophosphorylase (E.C. 2.7.7.23). Comparing
Fig. 6 with Fig. 7, it is clear that, while communities (modules)
can be clearly revealed when Smin = 51%, there are no communities
(modules) when Smin = 40%. This confirms that the modular structure is not ubiquitously revealed by the networks built for all Smin
values, but it is rather restricted to a critical range of such values.
In Fig. 8, we show a dendrogram for the subset of UDPacetylglucosamine pyrophosphorylase (E.C. 2.7.7.23), at Smin = 51%,
following the framework proposed by Newman and Girvan (2004).
It shows how the elimination of the edge of largest betweenness
degree also allows the identification of community structure, in
the same network at Smin = 51%, using the protein sequences of
UDP-acetylglucosamine pyrophosphorylase (E.C. 2.7.7.23).
The neighborhood matrix (Fig. 9) of the network for the subset of UDP-acetylglucosamine pyrophosphorylase (E.C. 2.7.7.23) at
Smin = 51% not only shows once again the modular structure of the
network, but also clearly depicts how far the retrieved communities
(modules) are to each other.
When we cross these findings derived from the complex network approach with taxonomic and phylogenetic data, sound
biological information can be promptly retrieved, even in the
absence of any previous knowledge about the biological issues
at stake. The modules that can be discerned at Smin = 51% correspond in a clear and rather precise manner to bacterial phyla and/or
classes. The reason why this analysis could be readily carried out
in the case of the domain Bacteria lies in the fact that most of the
protein sequences in the database are derived from this domain.
Notice that, in the subset of UDP-acetylglucosamine pyrophosphorylase (E.C. 2.7.7.23), we obtained 324 bacterial sequences, and only
two sequences each of Eukarya and Archaea (Table 1).
Community C1 is composed by 16 nodes, 14 of which are protein
sequences from representatives of the phylum Cyanobacteria, the
only bacterial group that comprises organisms capable of carrying
out oxygenic photosynthesis. One of the nodes corresponds to a
Fig. 8.
A. Góes-Neto et al. / BioSystems 101 (2010) 59–66
Fig. 9.
sequence from a species of Deinococcus-Thermus, a Gram-negative
diderm bacterial group of extremophiles that is closely related to
Cyanobacteria (Gupta, 2001).
Community C2 contains 134 nodes and, among them, 132 are
sequences from species of both -and ␥-Proteobacteria, which is
considered to be more closely related to each other than to any
other proteobacterial class (Gupta and Sneath, 2007). Community
C3 is entirely constituted by 76 sequences from Firmicutes species,
low G + C Gram-positive monoderm bacteria. Community C4 contains 26 vertices, of which 24 are sequences from the presumed
monophyletic group of ␣-Proteobacteria. Community C6 comprises
only nine sequences from the putative monophyletic group of Proteobacteria (Gupta and Sneath, 2007). Finally, community C5 is
entirely formed by sequences from Actinobacteria, high G + C Grampositive monoderm bacteria. Minor incongruences in the grouping
of nodes in the aforementioned communities (modules) are related
to the underrepresentation of eukaryal and archeal sequences in
the studied data set.
To close this Section, it is important to recall once again that
the results shown in Figs. 7–9 would be completely different if
values of Smin were not chosen in the critical region. The corresponding graphs below the critical region of Smin do not present
modular structure. Therefore, the power of the analysis reported
here to resolve the relationships between taxonomic groups from
a network approach to the comparison of protein sequences crucially depends on choosing the appropriate values of similarity, in
which the modularity of the network occurs. The critical region
was established based on the behavior of d, C, and the size of the
largest cluster shown in Figs. 4 and 5. These measures are related to
the behavior of the Euclidian distance between the neighborhood
matrices of successive values of similarity that determines very precisely the critical region, as was introduced in a recent paper by
some of us (Andrade et al., 2009). However, like other phylogenetic
analyses, this method does not set up a completely deterministic
criterion, in the sense that the number of communities is not determined by an automated manner. As already discussed, the fact that
the distinct modules do not detach from the largest cluster at a single value of Smin prevents us to find a uniquely defined critical value
where disruption occurs.
The modular structure of the network, revealed by the present
method, agrees with the phylogenetic relationships of the organisms with high reliability, since 99.6% of the nodes had a
neighborhood from the same taxonomic group.
4. Conclusions
The results obtained through the application of a complex network approach to the comparative analysis of protein sequences
related to the chitin metabolic pathway in extant organisms suggest
65
that this method can indeed retrieve sound biological information
even in the absence of previous knowledge about the systems under
analysis. Therefore, it can be used as a powerful tool to reveal relationship patterns among both organisms we have knowledge about
and organisms about which we do not have much information
available. The fact that the algorithm used to identify the communities (modules) and, consequently, the modularity of the network
does not use any a priori biological information suggests, in sum,
that this method can be applied as a new way of inferring molecular
phylogenies, in a high-throughput and automatic manner.
The next steps in our research program will be the application
of the method presented here to new sets of protein sequences,
and the comparison of the results obtained with the outcome of
other methods used to analyze phylogenetic relationships based
on molecular data in order to reveal their advantages and limitations. Although this is a huge task, the results of which deserve to
be discussed in another work, it is possible to advance that preliminary results for a much smaller data set than that used herein are
promising. For such case, we have found that the phylogenetic classification between the proposed method agree with those based on
the Bayesian, distance, maximum likelihood, and maximum parsimony criteria to an extent of, respectively, 46%, 51%, 51% and 33% of
the total number of organisms in the set. In half of the comparisons,
the agreement between the classification based on complex network approach and phylogenetic methods are above 50%, as well
as between each of the four phylogenetic methods. Interestingly,
the highest percentage of agreement always involves the maximum
likelihood method. Since the agreement between the classification
among the quoted methods using the same data set are about the
same order of magnitude, varying from 17% to 66%, we may conclude that the proposed methodology already shows to be reliable.
Acknowledgments
We would like to thank all who contributed directly or indirectly
to this work and, especially, the Graduate Program in Biotechnology
(PPGBiotec UEFS, http://www2.uefs.br/ppgbiotec).
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