Comparison of phylogenetic trees through alignment of embedded evolutionary distances
© Choi and Gomez; licensee BioMed Central Ltd. 2009
Received: 2 April 2009
Accepted: 15 December 2009
Published: 15 December 2009
The understanding of evolutionary relationships is a fundamental aspect of modern biology, with the phylogenetic tree being a primary tool for describing these associations. However, comparison of trees for the purpose of assessing similarity and the quantification of various biological processes remains a significant challenge.
We describe a novel approach for the comparison of phylogenetic distance information based on the alignment of representative high-dimensional embeddings (xCEED: Comparison of Embedded Evolutionary Distances). The xCEED methodology, which utilizes multidimensional scaling and Procrustes-related superimposition approaches, provides the ability to measure the global similarity between trees as well as incongruities between them. We demonstrate the application of this approach to the prediction of coevolving protein interactions and demonstrate its improved performance over the mirrortree, tol-mirrortree, phylogenetic vector projection, and partial correlation approaches. Furthermore, we show its applicability to both the detection of horizontal gene transfer events as well as its potential use in the prediction of interaction specificity between a pair of multigene families.
These approaches provide additional tools for the study of phylogenetic trees and associated evolutionary processes. Source code is available at http://gomezlab.bme.unc.edu/tools.
Understanding historical relationships between genes, proteins and species is a core aspect of evolutionary biology, with the phylogenetic tree playing a fundamental role in analysis and visualization. However, major challenges still exist in the representation and analysis of the information encoded within phylogenetic trees. For instance, inferring the "true" tree is fundamentally a difficult problem, leading to continuous refinement of reconstruction methods . Similarly, methodologies for tree comparison are also undergoing significant development . In this instance, the typical goal is to compare trees in order to determine their degree of similarity, providing one mechanism to test a variety of hypotheses regarding evolutionary associations. For example, comparison of gene trees with organismal trees allows the detection of non-standard events such as horizontal gene transfer [3, 4]. Comparison of species trees can be used to give a picture of host-parasite symbiosis as is seen, for example, in the case of attine ants, their fungal cultivars, and the Escovopsis parasite . Another example is the prediction of protein-protein interactions, as it has been shown that interacting proteins often appear to coevolve with one another [6–8]. Such instances of coevolution are largely based on the premise that in order to maintain their interaction (and thus their broader functionality), changes in one gene/protein will be coordinated with changes in the other, and this process of coevolution or correlated evolution can be observed through the similarity of their phylogenetic trees [9, 10].
While there are a variety of methods available for the comparison of trees, two general categories of approaches are clearly distinguishable. The first class of approaches focuses on comparing trees through topological features, for example quantifying the number of shared/non-shared substructures (e.g. subtrees of four leaf nodes) between a pair of trees [11, 12] or finding the minimum number of operations (e.g. nearest neighbor interchange) to transform one tree into another [13–15]. The second class of approaches compares the distance or path length information directly. Specifically, in these approaches assessing the similarity between two trees is reduced to a problem of finding the degree of correlation (most commonly the Pearson correlation) between the elements within the respective distance matrices. The "mirrortree" method is based on such an approach and was developed for the prediction of protein-protein interactions . Continued work in this area has led to multiple modifications of the basic mirrortree approach including the use of patristic distances obtained from the corresponding neighbor-joining tree instead of the observed inter-protein distances , the correction of patristic distance matrices for their inherent similarity due to background "tree of life" evolution [17–19], and the incorporation of ancestor node information into the distance matrices .
While methods based on distance matrix similarities have proven to be of particular value, several substantial disadvantages exist. For instance, these methods assume that each value in a distance matrix is independent of the other distance values. This is generally not the case as, if a distance (path length) between two leaf nodes changes, lengths of all other paths involving the modified edge(s) also change. Therefore, any method in which the distance matrices are directly manipulated without considering this dependency may bias the reported correlations. It is also difficult to extend these existing approaches, for example, to incorporate robust estimation into the identification of outlying lineages between compared trees. Furthermore, by definition, it is not possible to handle trees of different size or to align multiple trees simultaneously. Finally, prior knowledge cannot be readily incorporated so as to help guide comparisons.
Here, we report a novel method for the comparison of evolutionary distance matrices (and hence trees) based on the superimposition of Euclidean embeddings that best realize the given distance relationships. Specifically, we start from a set of aligned sequences and generate distance matrices based on either distance information calculated directly from the alignment, or distances derived from a corresponding neighbor joining tree. From these distance matrices we then map each sequence to a Euclidean space via metric multidimensional scaling (MDS). This operation produces a multidimensional structure or point pattern, where each point represents a taxon, and the distance relationships between all points is maintained from the original distance matrix. For the purpose of comparing two trees, the same operation is applied to the second distance matrix, generating the second Euclidean embedding. Finally, we superimpose one embedded point pattern onto the other with the degree of fit being determined by the least squares sum of deviations between corresponding point pairs or by some other measure as described below.
In cases where the identification of incongruent region between trees is desired, robust structure alignment (vCEED) can be performed using "Verboonian" Procrustes , which penalizes less for the existence of outliers when compared to rCEED. As a result, one can detect local regions of similarity even in the presence of outliers and/or identify outliers relative to a common shared structure. The identification of horizontal gene transfer (HGT) events is an area where outlier detection within a phylogenetic tree is needed and we provide an example of the applicability of vCEED to this problem. As with rCEED, we can also use vCEED to detect coevolving protein interactions, especially in cases where a reference structure is not available and/or target structures (trees) contain outlying taxa and show its in this. We also compare the performance of vCEED with that of rCEED and other existing methods.
Finally, alignment without either a reference structure or mapping information can be performed with a Gaussian mixture model superimposition approach (gCEED). As a proof-of-concept for the potential broader utility of this approach, we describe its application to the prediction of protein interaction specificity between multigene families. As a whole, the xCEED methodology provides a novel approach to the tree comparison problem and the study of related evolutionary processes.
Results and Discussion
Prediction of protein interactions
We first applied both rCEED and vCEED to the prediction of protein interactions through the detection of a coevolutionary signal between orthologous protein families. While analogous to the approaches of [17, 18], rCEED attempts to address some of their weaknesses. Specifically, in the tol-mirrortree approach, Pazos and colleagues subtracted the distance matrix of 16S rRNA from that of each protein, and then measured the correlation between these "difference of distance" matrices . However, direct subtraction of rRNA from protein distances is problematic, as their evolutionary rates are different and it is not clear as to how to properly scale such differencing procedures. In phylogenetic vector projection, Sato and colleagues formed a vector from the lower triangular region of each distance matrix  and computed a difference vector between a gene vector and the same gene vector projected onto that of 16S rRNA. Again the correlation between distance matrices is measured with these difference (normalized) vectors. While avoiding direct subtraction of amino acid and rRNA distances, this approach (as does the tol-mirrortree approach) still assumes that all pairwise distances are independent. Not accounting for non-independence between distances can potentially cause bias in evaluation of correlation between two distance matrices .
The rCEED approach addresses these issues by viewing the leaf nodes in an embedded structure as independent variables. To measure the degree of coevolution, we estimate how similar the deviations from the reference structure are for each embedded structure. Doing this makes it possible to remove the background tree-of-life correlation without direct subtraction of rRNA distances from amino acid distances or assuming independence between distances. Specifically, we fit the reference structure(s) onto the first embedded structure and then onto the second structure separately (see Figure 2). Afterwords, we superimpose these two reference structures onto each other while carrying along their associated structures, which are the actual targets of interest. After this superimposition we can remove the reference structures, and then measure the degree of similarity between the remaining two target structures. As a single outlier can make the estimation of correlation coefficients unreliable  we also evaluated the use of vCEED in this application as it is specifically tailored for dealing with outliers (see following section as well as Methods for more details).
AUCs of tested approaches for detecting protein interactions via coevolution.
AUC (PR curve)1
AUC (ROC curve)2
0.763 ± 0.013
0.766 ± 0.012
0.763 ± 0.013
0.687 ± 0.013
0.722 ± 0.014
phylogenetic vector projection
0.704 ± 0.013
0.687 ± 0.013
Interactions identified in DIP6
Detection of horizontal gene transfer
In Figure 3(a) it can be seen that errors are distributed across all pairs, as would be done using the basic xCEED method using least squares (e.g. rCEED with a reference structure). However, in this example there is a substructure that is in fact identical between the two that is lost as a result of the spreading of errors throughout the alignment. In contrast, Figure 3(b) shows the case where we have used Verboonian robust Procrustes (vCEED) for the alignment. In this case we have found and aligned the identical substructures; allowing identification of both this region of high-similarity as well as the outliers which deviate significantly between the two distance matrices.
This ability to detect local similarity and/or outliers is of particular utility in the identification of horizontal gene transfer (HGT) events. In HGT, a gene or group of genes is transferred laterally from another species, rather than inherited vertically from the parent(s). There are a variety of approaches to predict the occurrence of HGT based on, for example, codon usage, patterns of sequence homology, and patterns of gene distribution [25, 26]. However, the most robust method for detecting HGT is through the comparison of phylogenetic trees of different genes. When a species accepts a gene laterally from another species, the location of the recipient species in the phylogenetic tree will be unusually close to the location of the donor species, which can be detected through manual analysis of the tree. Using vCEED, we can detect possible HGT by comparing a tree that potentially harbors one or more HGT events with a reference tree that does not, and then identifying the associated outliers as likely HGT candidates.
The Rickettsiales (blue) identified by Omelchenko and colleagues were also included in our outlier list, although they were not the most deviating. Note that being an outlier does not certify that the gene was horizontally transferred. Other mechanisms for this deviation can also occur including large differences in evolutionary rate or poor quality of the sequence alignment. Therefore, while this approach can potentially aid in the automatic prediction of potential HGT events, manual inspection of the phylogenetic tree may still be required. For example, the Firmicutes genes, L183602 and SA1103, while being slight outliers, are in a monophyletic subtree of Firmicutes (purple) and can thus be excluded from further consideration.
Interaction specificity between multigene families
As demonstrated earlier, we can use either rCEED or vCEED to compare trees so as to predict the potential interaction of a pair of protein families. Again, these approaches require the use of mapping information to link the leaves of the two trees. There are applications, however, where one would like to compare trees that lack mapping information or where the recovery of mapping information is the primary goal. An important example of this type is in trying to determine likely interaction specificity between a pair of protein or domain families (e.g. receptor-ligand binding, etc.) [8, 28–30].
Two primary methods for specificity prediction, MATRIX  and MORPH , currently exist, and like all methods, have their own inherent strengths and weaknesses. With MATRIX, a significant weakness is that the tree structure is completely ignored throughout the specificity search. MATRIX also requires multiple simulated annealing runs (≥ 100 runs with trees of 15 leaves or more) to determine which pairings are most frequent. Perhaps most important, both MATRIX and MORPH assume that there is a one-to-one correspondence between members of the two protein families; i.e. protein A from family 1 interacts solely with protein B from family 2. Thus it is not possible to generalize to the more realistic situation where we are looking at specificities between protein families of different size. In addition it precludes the possibility of many-to-many or multiple interaction partners for a given protein.
Here we adapt the use of a registration algorithm based upon Gaussian mixture models with our basic embedding and alignment approach . In this case, we regard each vertex in the embedded structure (i.e. each leaf in the phylogenetic tree) as the mean of a Gaussian component such that the entire embedding is represented as a mixture model (see Methods). The central idea is that if we have two structures that are highly similar, as we align one structure closer to the other, their corresponding mixture models become accordingly similar. By trying to minimize the divergence between the two mixture models, we can eventually find the best superimposition. We refer to this method of alignment as Gaussian CEED or gCEED for short. Using gCEED, we attempted to determine the specificity information between protein families provided in Ramani et al. .
The final result after complete alignment is shown in Figure 6(c). Here we can see that gCEED successfully predicted the interaction specificity for 12 out of 20 individual interactions. The other misassigned 8 pairs were degenerate cases and their interaction specificity could not be further defined due to a lack of structural information. The reason for this can in part be observed within Figure 6(a), where the four proteins from each family (marked with arrows) can be observed to be very close to each other (short branch lengths from their common ancestor). In such instances it is difficult for the algorithm to find a correct high-probability mapping as multiple alignments are equally viable. Nevertheless, the interaction specificity at the protein-family level was correctly predicted. In addition, over half of the specific interactions could be recovered solely from the alignment of these structures.
Stringent accuracy of specificity prediction.
Protein Family Name
GyrA/B, ParC/E (α-proteobacteria)
Lyt-type regulator/sensors (E. coli/B. subtilis)
GyrA/GyrB (Gram positive bacteria)
Acetyl CoA carboxylase α/β (proteobacteria)
ParC/ParE (Gram positive bacteria)
Pyruvate dehydrogenase α/β (bacteria)
GyrA/B, ParC/E (Gram positive bacteria)
DNA polymerase III E2/E3 (bacteria)
Succinate CoA synthetase α/β (archaea)
Ntr-type regulator/sensors (8 bacteria)
Succinate CoA synthetase α/β (proteobacteria)
Omp-type regulator/sensors (5 bacteria)
CCR-type chemokine/receptor (mouse/human)
Acetyl CoA carboxylase α/β (Gram positive bacteria)
CKR-type chemokine/receptor (mouse/human/rat)
CheA/CheY (11 bacteria)
Nar-type regulator/sensors (8 bacteria)
Cit-type regulator/sensors (E. coli/B. subtilis)
ABC transporter membrane/binding protein (E. coli)
ABC transporter membrane protein 1/2 (E. coli)
ABC transporter membrane binding protein (H. influenzae)
Two-component sensor/regulators (E. coli)
ABC transporter membrane protein 1/2 (H. influenzae)
Omp-type regulator/sensors (E. coli/B. subtilis)
Omp-type regulator/sensors (E. coli)
Omp-type regulator/sensors (B. subtilis)
Lyt, Ple, and other type regulator/sensors (8 bacteria)
As a demonstration of this functionality within gCEED, we again used the case of GyrA and GyrB interactions. We first made the GyrA tree progressively smaller by sampling from nineteen down to ten sequences from the total of twenty GyrA orthologs, with 100 different combinations for each size. We then performed specificity prediction by aligning each sampled GyrA tree with the complete 20-node GyrB tree. To evaluate our performance, we introduce the vicinity hit rate as a means to estimate how close each node's true interacting partner is in relation to others within the aligned structures. Specifically, we define the vicinity hit rate as the ratio of nodes that have their true interacting parter within top three highest predicted probability partners. Thus the vicinity hit rate allows for situations where the true interacting partner is very close (but not the closest) to the predicted interaction partner as determined through the alignment.
We would expect that additional information in the form of prior knowledge of an existing protein interaction pair would help to improve predictive performance. Such knowledge can be readily introduced into the gCEED alignment scheme and results of knowing just a single pair a priori are shown in Figure 7(b). Here we picked a random, but correct pair of interacting proteins between the two trees to serve as the a priori known information. As these proteins interact, we assume that they must be near each other in the final superimposition. We thus impose a constraint in the optimization of Equation (12), where the two proteins are kept within a pre-specified distance range (0.05 in this work).
Results show that use of prior knowledge provides a significant improvement in the stability of the vicinity hit rate, with a mean hit rate of approximately 60% even when reducing tree size to nearly half of its original value. In addition, using the structural information provided by the known interaction pair, we were able to avoid degenerate cases (shaded box in Figure 7(b)). In the comparisons between trees with greatest difference in size, the average vicinity hit rate of ten-node sample trees was 32.0% without prior knowledge versus 53.2% when using a single known protein pair. Together, these results suggest the potential for using gCEED in realistic situations where differences in tree sizes exist and/or prior information is available.
In this work, we have described a novel approach for the comparison of phylogenetic trees, represented as embedded structures, and shown several examples of its application. First, when applied to the prediction of protein interactions, we see an improvement in prediction accuracy using the rCEED/vCEED approach when compared to other available approaches. We note, that high similarity between two embedded structures does not require that there is a physical interaction between members, but is only suggestive of the possibility. Similarly, the physical interaction between two proteins does not necessitate coevolution. Thus coevolutionary approaches such as those presented here can only identify a portion of the complete interactome within a given species. For the enhanced prediction of protein interactions, approaches such as rCEED/vCEED may show their greatest efficacy when combined with other computational approaches (e.g. [32–34]).
With vCEED, we were also able to perform a local alignment between structures, providing the opportunity to detect outliers that often indicate unusual evolutionary events including the horizontal gene transfer described here. While phylogenetic methods which detect incongruity between trees are generally considered the gold-standard for HGT detection, these methods are not readily automatable and require extensive manual analysis. Our results suggests that vCEED has significant potential in aiding such identifications.
By using the information inherent in the representation of a tree as an embedded structure, we were able to demonstrate the ability to align and measure the similarity between trees even when correspondence information is not available or when their sizes are different. While a basic example, the need to establish interaction specificity between interacting protein families supports the development of new approaches, and in this regard, gCEED shows significant promise.
While the embedding and superimposition of taxa within a Euclidean space in no way supersedes the use of a phylogenetic tree, it does provide several useful capabilities. For instance, embedding generates a deterministic structure that bypasses ambiguities associated in direct tree comparisons by transforming a specific distance matrix into a single specific shape enabling consistent comparison between trees. Similarly, use of a representative embedding also makes it possible to take into account the entire point-pattern structure all at once when determining correlation, rather than examining pair-by-pair correlation as in the mirrortree or related approaches. Finally, the representation of trees as embedded structures provides the capability to compare trees of different size, which is a built-in limitation of correlation-based methods. In this case, it becomes a matter of comparing two structures using procedures based on registration approaches such as the gCEED approach proposed in this work. As a whole, the xCEED approach provides an additional set of tools for the study of phylogenetic trees and associated evolutionary processes.
For the prediction of protein interactions, we tested our method using data identical to that used by Pazos and colleagues . This data set consists of experimentally characterized interactions among Escherichia coli proteins deposited in the February 2004 version of the DIP database . For each protein in the interaction data, orthologs from 43 other prokaryotic species were collected to form each protein family. Among all the possible pairs of protein families, those that have less than ten common matching species (or taxa) were removed, leaving 19,972 suitable test protein interaction pairs (118 different proteins in total). From this complete set of protein interaction data, there were 115 experimentally characterized, true-positive, interaction pairs. We updated this set of interactions by checking all the 19,972 test interactions with the July 2007 version of DIP, and found that 388 of them were experimentally validated (an increase of 223 true-positive interactions from the 2004 version of DIP). We used this updated data set when measuring the discrimination power of our method. Along with this set of true interactions, a set of negative interactions was formed from the complement of this data - i.e. protein pairs not experimentally shown to be interacting. Thus a total of 19,584 negative interactions were formed in this way. For specificity prediction we used the data from .
Each protein family was aligned with clustalw, and distance matrices were calculated with the protdist routine from phylip. These distance matrices are different from those used in  in that our data are created directly from the sequence alignments rather than from neighbor-joined trees. However, for comparison we also performed the same test with those used in . The sequences and distance matrices of 16S rRNA were downloaded from the Ribosomal Database Project II .
The basic xCEED approach: Classical MDS and superimposition with Procrustes
The approach we have developed is based upon extensions to the methods of multidimensional scaling and Procrustes analysis and we discuss these two fundamental approaches now. First, classical MDS attempts to find a Euclidean embedding of the data while simultaneously trying to preserve their interpoint distances . Given distance matrix D = [d ij ], we first compute the contrast matrix M which is defined to be equivalent to , where C is the centering matrix I - 1'1 (1 is a row vector of ones and n is the number of nodes), and . After performing eigenvalue decomposition on M, which gives M = QΛQ', we get X = QΛ1/2, which gives the coordinates of the points embedded in a, potentially high-dimensional, Euclidean space. Note that we truncate the negative eigenvalues in Λ since D is a Euclidean matrix if and only if M is positive semi-definite, which then defines the maximum dimensionality. Again, distances between points in this new structure representation are those that were provided by the original distance matrix for the tree.
where U and V is the left and right singular matrices that are coming from the singular value decomposition of Z'CW(= UΣV'), where Σ is the matrix of singular values.
Reference-based comparison of embedded evolutionary distances (rCEED): application to the quantification of protein coevolution
where is the centroid of a reference structure. Because the number of common species will be different from one pair of protein families to another pair, their distributions in the space will have different variances. As a result, they are all normalized in (5), so that we can compare the strength of the coevolutionary signal among differently sized pair sets of protein families.
Verboonian robust superimposition (vCEED): application to the detection of horizontal gene transfer
Since both transformation parameters (s, R, and t) and weight matrix (P) are unknown, we estimate them using Expectation-Maximization, where we alternate between the computation of transformation parameters using a fixed weight matrix P and the updating of P based upon the current estimation of transformation. Through this iterative process, the weight value in P gets smaller if an error term is larger than the pre-specified threshold, c. In the work described here, we used an empirically chosen value of 0.01 for c.
Superimposition without correspondence information (gCEED): application to the prediction of interaction specificity
For the derivation of (12), see . We assumed isotropy, so Σ i = Σ j = σ2I for all i and j's. We further assumed that the weights of all Gaussian components are equal such that α i = 1/m and β j = 1/n.
We would like to thank Suzy Vasa for her work in the early stages of this project and Dr. Florencio Pazos for helpful conversations regarding his data. We also thank Dr. Yufeng Liu and Dr. Marc Niethammer for useful discussions regarding algorithmic aspects of this work. This material is based upon work supported by, or in part by, an National Institute of Health grant DK37871 and the U. S. Army Research Laboratory and the U. S. Army Research Office under contract/grant number W911NF-09-0049. Financial support for these studies was also provided, in part, by the United States Environmental protection Agency grant RD833825. However, the research described in this article has not been subjected to the Agency's peer review and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.
- Felsenstein J: Inferring Phylogenies. Sinauer. 2004.Google Scholar
- Page RDM: Tangled Trees: Phylogeny, Cospeciation, and Coevolution. Chicago, IL 60637 USA: University of Chicago Press; 2002.Google Scholar
- Addario-Berry L, Hallett MT, Lagergren J: Towards Identifying Lateral Gene Transfer Events. Pacific Symposium on Biocomputing 2003, 279–290.Google Scholar
- MacLeod D, Charlebois R, Doolittle F, Bapteste E: Deduction of probable events of lateral gene transfer through comparison of phylogenetic trees by recursive consolidation and rearrangement. BMC Evolutionary Biology 27.Google Scholar
- Currie CR, Wong B, Stuart AE, Schultz TR, Rehner SA, Mueller UG, Sung GH, Spatafora JW, Straus NA: Ancient Tripartite Coevolution in the Attine Ant-Microbe Symbiosis. Science (5605):386–388.Google Scholar
- Goh CS, Bogan AA, Joachimiak M, Walther D, Cohen FE: Co-evolution of proteins with their interaction partners. J Mol Biol 2000, 299(2):283–93. 10.1006/jmbi.2000.3732View ArticlePubMedGoogle Scholar
- Fryxell KJ: The coevolution of gene family trees. Trends Genet 1996, 12(9):364–369. 10.1016/S0168-9525(96)80020-5View ArticlePubMedGoogle Scholar
- Moyle WR, Campbell RK, Myers RV, Bernard MP, Han Y, Wang X: Co-evolution of ligand-receptor pairs. Nature 1994, 368(6468):251–255. 10.1038/368251a0View ArticlePubMedGoogle Scholar
- Yeang CH, Haussler D: Detecting Coevolution in and among Protein Domains. PLoS Comput Biol (11):e211.Google Scholar
- Kann MG, Shoemaker BA, Panchenko AR, Przytycka TM: Correlated evolution of interacting proteins: looking behind the mirrortree. J Mol Biol 2009, 385: 91–98. 10.1016/j.jmb.2008.09.078PubMed CentralView ArticlePubMedGoogle Scholar
- Robinson DF, Foulds LR: Comparison of phylogenetic trees. Mathematical Biosciences (1–2):131–147.Google Scholar
- Estabrook GF, McMorris FR, Meacham CA: Comparison of Undirected Phylogenetic Trees Based on Subtrees of Four Evolutionary Units. Systematic Zoology (2):193–200.Google Scholar
- Robinson DF: Comparison of labeled trees with valency three. Journal of Combinatorial Theory, Series B 105–119.Google Scholar
- Waterman MS, Smith TF: On the similarity of dendrograms. Journal of Theoretical Biology 789–800.Google Scholar
- Hein J, Jiang T, Wang L, Zhang K: On the complexity of comparing evolutionary trees. Discrete Appl Math 1996, 71(1–3):153–169. 10.1016/S0166-218X(96)00062-5View ArticleGoogle Scholar
- Pazos F, Valencia A: Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng 2001, 14(9):609–614. 10.1093/protein/14.9.609View ArticlePubMedGoogle Scholar
- Pazos F, Ranea JAG, Juan D, Sternberg MJE: Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome. J Mol Biol 2005, 352(4):1002–1015. 10.1016/j.jmb.2005.07.005View ArticlePubMedGoogle Scholar
- Sato T, Yamanishi Y, Kanehisa M, Toh H: The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics 2005, 21(17):3482–3489. 10.1093/bioinformatics/bti564View ArticlePubMedGoogle Scholar
- Sato T, Yamanishi Y, Horimoto K, Kanehisa M, Toh H: Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions. Bioinformatics 2006, 22(20):2488–2492. 10.1093/bioinformatics/btl419View ArticlePubMedGoogle Scholar
- Craig RA, Liao L: Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices. BMC Bioinformatics 2007, 8: 6. 10.1186/1471-2105-8-6PubMed CentralView ArticlePubMedGoogle Scholar
- Verboon P, Heiser W: Resistant orthogonal procrustes analysis. Journal of Classification (2):237–256.Google Scholar
- Allen MP: Understanding Regression Analysis. Springer; 2004.Google Scholar
- Warner RM: Applied Statistics: From Bivariate Through Multivariate Techniques. Sage Publications, Inc; 2007.Google Scholar
- DeLong ER, DeLong DM, Clarke-Pearson DL: Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics (3):837–845.Google Scholar
- Brown JR: Ancient horizontal gene transfer. Nat Rev Genet 121–132.Google Scholar
- Ragan MA: Detection of lateral gene transfer among microbial genomes. Current Opinion in Genetics Development (6):620–626.Google Scholar
- Omelchenko M, Makarova K, Wolf Y, Rogozin I, Koonin E: Evolution of mosaic operons by horizontal gene transfer and gene displacement in situ. Genome Biology (9):R55.Google Scholar
- Ramani AK, Marcotte EM: Exploiting the co-evolution of interacting proteins to discover interaction specificity. J Mol Biol 2003, 327: 273–84. 10.1016/S0022-2836(03)00114-1View ArticlePubMedGoogle Scholar
- Jothi R, Kann MG, Przytycka TM: Predicting protein-protein interaction by searching evolutionary tree automorphism space. Bioinformatics 2005, 21(Suppl 1):i241–50. 10.1093/bioinformatics/bti1009PubMed CentralView ArticlePubMedGoogle Scholar
- Jothi R, Cherukuri PF, Tasneem A, Przytycka TM: Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions. J Mol Biol 2006, 362(4):861–875. 10.1016/j.jmb.2006.07.072PubMed CentralView ArticlePubMedGoogle Scholar
- Jian B, Vemuri BC: A Robust Algorithm for Point Set Registration Using Mixture of Gaussians. iccv 2005, 2: 1246–1251.Google Scholar
- Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA 1999, 96(8):4285–4288. 10.1073/pnas.96.8.4285PubMed CentralView ArticlePubMedGoogle Scholar
- Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, Eisenberg D: Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285(5428):751–753. 10.1126/science.285.5428.751View ArticlePubMedGoogle Scholar
- Gomez SM, Noble WS, Rzhetsky A: Learning to predict protein-protein interactions from protein sequences. Bioinformatics 2003, 19(1367–4803):1875–81. 10.1093/bioinformatics/btg352View ArticlePubMedGoogle Scholar
- Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The Database of Interacting Proteins: 2004 update. Nucl Acids Res (suppl-1):D449–451.Google Scholar
- Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic acids research (22):4673–4680.Google Scholar
- Felsenstein J: PHYLIP (Phylogeny Inference Package) version 3.6. Distributed by the author. Department of Genome Sciences, University of Washington, Seattle 2005.Google Scholar
- Cole JR, Chai B, Farris RJ, Wang Q, Kulam-Syed-Mohideen AS, McGarrell DM, Bandela AM, Cardenas E, Garrity GM, Tiedje JM: The ribosomal database project (RDP-II): introducing myRDP space and quality controlled public data. Nucl Acids Res (suppl-1):D169–172.Google Scholar
- Borg I, Groenen PJF: Modern Multidimensional Scaling: Theory and Applications. New York, NY 10013 USA: Springer New York; 2005.Google Scholar
- Huber PJ: Robust Statistics. New York, NY, USA: John Wiley & Sons, Inc; 1981.View ArticleGoogle Scholar
- Beaton AE, Tukey JW: The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data. Technometrics (2):147–185.Google Scholar
- Wand MP, Jones MC: Comparison of Smoothing Parameterizations in Bivariate Kernel Density Estimation. Journal of the American Statistical Association (422):520–528.Google Scholar
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