- Software
- Open Access
GraphAlignment: Bayesian pairwise alignment of biological networks
- Michal Kolář^{1, 2},
- Jörn Meier^{1},
- Ville Mustonen^{1, 3},
- Michael Lässig^{1} and
- Johannes Berg^{1}Email author
https://doi.org/10.1186/1752-0509-6-144
© Kolář et al.; licensee BioMed Central Ltd. 2012
- Received: 10 May 2012
- Accepted: 7 November 2012
- Published: 21 November 2012
Abstract
Background
With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks.
Results
We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0.
On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as$\mathcal{O}\left({N}^{2.6}\right)$.
On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment.
Conclusions
The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.
Keywords
- Graph alignment
- Biological networks
- Parameter estimation
- Bioconductor
Background
The advent of high-throughput techniques has generated new types of large-scale molecular interaction data, conveniently represented by graphs or networks. Examples include metabolic networks formed by enzymes and metabolites[1], gene co-expression networks with edges between pairs of genes indicating a certain correlation between their expression levels[2], residue contact maps as representations of protein structures[3, 4], and protein-protein interaction networks, where edges between vertices indicate a physical interaction between proteins[5]. For an introduction, see reference[6].
Cross-species analysis of bio-molecular networks aims to identify sub-networks which are evolutionarily conserved as well as network parts that have evolved rapidly. Similarly to comparison of biological sequences[7], alignment of biological networks is an important tool for quantitative evolutionary studies[2, 8–16]. However, such alignment poses a challenging computational problem, which goes beyond the well-established concepts and methods of sequence alignment and of subgraph matching (isomorphism)[17]. It involves an evolutionary process in which a pair of networks derives from a common ancestor (which accounts for a certain degree of similarity), and each network has since evolved independently (which results in edge changes, vertex changes, and vertices losing their alignment partner).
Several graph alignment methods have been proposed towards this goal, based on three main ideas: The alignment can be based on the similarity of vertices, and map vertices onto each other that, e.g., share a certain sequence similarity (if vertices represent genes or proteins) or if aligned enzymes catalyze the same reaction (if vertices represent enzymes in a metabolic network). This approach allows identification of ancestral networks[14], network parts enriched in conserved edges[10, 12, 16], or selection between paralogous genes[13].
A second and complementary approach focuses on the topology of the graphs and disregards sequence information or other properties of the vertices. It searches for similar topological structures in two graphs, for instance by maximizing the number of aligned edges. This approach has been used, for example, to detect common regulatory motives in gene regulatory networks[18, 19] or to perform global network alignment[20].
A third strategy relies both on information encoded in vertices and in edges. This “hybrid” and more comprehensive approach compares graphs based on the evolution of both vertices and edges. The key problem is the relative weight given to the similarity of vertices and to the similarity of edges when constructing the alignment. Several algorithms have been proposed[11, 21–27], which generally use ad hoc scoring parameters. Two exceptions are GraphAlignment[28] and Græmlin 2.0 (hereafter Græmlin,[22]), which use parameters inferred from a training set or from an initial alignment of high-fidelity vertices (Græmlin, GraphAlignment), or in an iterative scheme (GraphAlignment). Here we describe a software package implementing the GraphAlignment algorithm.
The scoring parameters may indeed be inferred from a training dataset formed by a library of known orthologous genes and their interactions. This approach would be conceptually similar to the inference of the BLOSUM matrices[29] used for biological sequence comparison. As bio-molecular networks differ in many aspects, including experimental techniques and post-processing methods, no such parametrisation is available for their comparison. The parameters, however, can be also inferred from the actual data being aligned, similarly to the inference of the optimal affine gap penalties from the sequences being compared[30, 31]. The ability to infer principled scoring parameters directly from the data is essential.
Further methods are developed that incorporate additional information resources to perform network alignment. The global network alignment method PINALOG[32] incorporates functional annotation of proteins in addition to their sequence and network topology. DOMAIN algorithm uses protein domains, rather than proteins, to form the interaction network[33]. Several above mentioned methods perform also multiple-species alignment and either use or infer phylogeny (e.g.,[20, 22, 34]). Methods for querying large networks for small subgraphs, e.g, pathways or protein complexes, have been also developed[35–37], reviewed in[38].
GraphAlignment differs from the above approaches[11, 21–27] by two key features: (a) An explicit model of network evolution is used to infer alignment parameters from the data. (b) Based on this evolutionary model, networks are aligned using a probabilistic scoring system. We compare our software and Græmlin as the only algorithms that can automatically score both sequence and network information. To that end we perform the simplest task, pairwise alignment.
For case studies applying our approach to mammalian gene co-expression networks and to herpesviral protein-protein-interaction networks, see[28] and[31]. An overview of related methods for probabilistic network analysis is given in ref.[39].
Implementation
The input of the algorithm are two networks, and mutual similarities of their vertices. The algorithm treats the networks G and G^{ ′ } symmetrically, thus comparison of G with G^{ ′ } will result in the same alignment as comparison of G^{ ′ } with G. Each network G is represented by an adjacency matrix A, whose entries A specify the edge between vertices i and j: The entries of the adjacency matrix may be binary, with A_{ ij } = 1 indicating the presence of an edge between i and j, and A_{ ij } = 0 its absence. They may be continuous, e.g., to describe weighted edges in gene co-expression networks. Adjacency matrices may be symmetric, thus describing undirected networks (e.g., gene co-expression networks), or asymmetric for directed networks (e.g., metabolic networks). The mutual similarity between vertices in the two networks is specified by matrix Θ, whose entries${\theta}_{i{i}^{\prime}}$ quantify, for example, the overall sequence similarity between the gene represented by vertex i in one network and the gene represented by vertex i^{′} in the other. Any other measure of the vertex similarity is possible and may be given in arbitrary units (Figure1b). The algorithm will infer appropriate scoring automatically based on available data.
tends to zero in the limit$\tau \to \infty $, as then the edge states carry no information on their shared ancestry, and, hence, the edges states a and a^{′} carry no information on whether i should be aligned with i^{′}and j with j^{′}.
with j^{′} ≠ i^{′}, which weighs the presence of vertex similar pairs that are not orthologous, tend to zero, and the vertex similarities${\theta}_{i{i}^{\prime}}$ and${\theta}_{i{j}^{\prime}}$ convey no information on alignment of i and i^{′}. The background distribution P(θ) may be obtained as the distribution of vertex similarities between vertices that emerged or disappeared in one of the networks after the speciation. The similarity of vertices itself may be evaluated as sequence similarity for vertices representing genes or proteins (in gene co-expression networks and protein-protein interaction networks, respectively) or by the measure of functional similarity for vertices representing enzymes (in metabolic networks).
The parameters of the scoring function, i.e, s_{edge}, s_{aligned} and s_{not-aligned}, depend on the evolutionary dynamics of both edges and vertices since speciation. To infer these parameters from the data, we use a simple iterative approach[28]: Starting with an initial alignment, parameters are estimated so that the likelihood of the alignment is maximised. The algorithm then iterates the steps of (i) aligning the graphs using the estimated parameters and (ii) estimating the maximum likelihood parameters until convergence. Upon convergence, the algorithm returns both the optimal scoring parameters and the corresponding best alignment of the networks. The package GraphAlignment features built-in functions that establish the maximum-likelihood scoring parameters according to this scheme. The ability to find the appropriate scoring parameters from the studied graphs is unique to GraphAlignment, with a notable exception of Græmlin[22].
To find high-scoring graph alignments in step (i), we use an iterative heuristic described in[28]. This procedure is based on mapping to the quadratic assignment problem, solved iteratively by calls to a linear assignment solver, with added noise to help the alignment to escape from local score maxima, as in simulated annealing[40].
Results and discussion
In Berg and Lässig[28] and Kolář et al.[31], our algorithm has been applied to gene co-expression networks and small protein-protein interaction networks. Here, we concentrate on evaluation of the computational complexity of the algorithm and comparison of its accuracy to the Græmlin algorithm[22], which is the only other algorithm able to infer principled scoring parameters automatically. We use both simulated and empirical bio-molecular data.
Alignment of simulated networks
While experimental data provide the ultimate test set for the algorithms, and we will use them in the following section, we do not know the true evolutionary history of the networks and thus, we cannot assess the accuracy of the aligners fully. To that end we use simulated data. In the numerical experiment, pairs of orthologous vertices (orthologs) are assigned from the outset and, depending on the level of divergence, may have retained their vertex similarity (vertex homologs), interaction similarity (topological homologs or analogs) or both.
GraphAlignment and Græmlin are able to infer the scoring parameters either from a training set of known orthologous genes and their interactions or from some valid initial alignment of the actual network data being aligned. Here, we concentrate on the latter option. Both algorithms are given the same initial alignment of the networks that is formed by vertices with high vertex and topological similarity, and the parameters are inferred from this initial alignment.
We assess the computational cost and accuracy in three different scenarios which test three different aspects of the algorithms. In all the scenarios, we construct pairs of networks which contain 80% of orthologous vertices and 50% of all possible edges present. In scenario (i) we compare two networks with a substantial proportion of vertex homologs and a smaller set of analogous vertices, i.e., vertices that do not have any vertex similarity, yet they are, by their interactions, well anchored to the subnetworks consisting of vertex-orthologous vertices. Thus this scenario tests the ability of the algorithm to identify analogous vertices by properly evaluating the edge (interaction) similarity. We implement the scenario (i) by networks with 60%-interaction similarity between the orthologous pairs and with 62.5% of the orthologous pairs (50% of all vertices) having also a high vertex similarity. The interaction terms are randomly chosen from a uniform distribution and may be interpreted as edge weights or probabilities of the edge existence. We also assessed the scenario (i) with interaction terms selected from a normal distribution and obtain similar results (Additional file1). An example of the corresponding Θ(i,i^{′}) matrix of vertex similarities and correlation matrix of interaction similarities is given in Additional file1: Figure S3(i, ia).
In scenario (ii), we test whether the algorithm is able to decide on an ortholog between two paralogous vertices. Specifically, we ask whether the algorithm is able to decide between two vertices in G^{′} with equal vertex similarity to i in G, one of which has also interaction similarity with i (the true ortholog) and the other shares no interactions (the spurious ortholog). We implement this scenario similarly to scenario (i) with 12.5% of the orthologs (10% of all vertices) having a paralog with no topological similarity. An example of the corresponding similarity structures is given in Additional file1: Figure S3(ii).
Computational complexity
The typical-case computational cost of GraphAlignment is smaller than its theoretical worst-case complexity, which is dominated by the computational costs of the linear assignment solver[41] and by conversion of the edge score to an instance of the linear assignment problem. The overall worst-case complexity of the algorithm is$\mathcal{O}\left({N}^{3}\right)$.
Accuracy
Both algorithms studied here rely on the initial alignment of high-fidelity vertices, which in our numerical experiment are represented by the orthologs with high vertex and topological similarity, and on inference of the scoring parameters from this initial alignment. Thus, it is not surprising that both algorithms correctly identified these orthologs in virtually all cases (corresponding to green diagonals in Figure2). The algorithms differ, however, in their ability to align analogs (orthologs with no vertex similarity and high topological similarity in scenarios (i-iii)) and to decide on the true ortholog between two paralogs in scenarios (ii) and (iii).
While GraphAlignment performs pairwise alignment of the networks and its results are straightforwardly interpretable, Græmlin groups the vertices from both networks into equivalence classes which may contain several vertices from each network. When interpreting Græmlin’s results, there are two options to consider the vertices correctly aligned. We can consider the matching vertices of the two networks to be correctly aligned when they are in the same equivalence class a nd there is no other vertex in the class (the strict rule), or we can consider them correctly aligned whenever they are in the same equivalence class (the relaxed rule). It is worth noting that in scenarios (ii) and (iii) the relaxed rule will consider the vertex correctly aligned even if the equivalence class contains both its homologous paralogs and the alignment actually does not decide on the correct partner. A vertex is considered misaligned when it is in an equivalence class (of size greater than 1) where its matching vertex is not present. If the class contains vertices from a single graph only, these are not considered misaligned.
Addition of the spurious terms into the vertex similarity matrix θ in scenario (iii) does not influence the accuracy of GraphAlignment but decreases accuracy of the Græmlin algorithm, which is not able to form the equivalence classes correctly anymore and misaligns many vertices, see Figures5(iii) and6(iii).
Alignment of empirical bio-molecular networks
To compare the performance of GraphAlignment and Græmlin on diverse bio-molecular networks, we have downloaded publicly available datasets of bacterial and eukaryotic protein-protein interaction networks (PIN) and gene co-expression networks. We let the algorithms compare PIN of proteobacteria Escherichia coli, Caulobacter crescentus and Campylobacter jejuni, and of yeast Saccharomyces cerevisiae, mouse and human. Next, we employ the algorithms to compare gene co-expression networks of gamma-proteobacteria Escherichia coli, Salmonella enterica and Shewanella oneidensis and a firmicute, Bacillus subtilis. The specificity and coverage of the resultant alignments are tested against the orthologous groups defined in the eggNOG database v3.0[42].
Protein sequences of all species have been downloaded from the eggNOG database. PIN of the bacterial species have been downloaded from the STRING database v9.0[43]. Human and murine PIN have been obtained from the IntAct database v3.1 ([44], accessed on August 6, 2012). Only high-confidence experimental interactions are kept (STRING: score ≥ 0. 7, IntAct: miscore ≥ 0. 35, no spoke-expanded interactions). To diversify the entering data, the PIN and protein sequences of human have been downloaded from the Additional file of the reference[45], and the yeast PIN and protein sequences from the Additional file of the reference[46] and the Saccharomyces genome database (http://www.yeastgenome.org,, accessed on August 8, 2012)[47], respectively.
To create the gene co-expression networks, we have downloaded large gene expression compendia of Escherichia coli, Salmonella enterica and Bacillus subtilis from the Colombos database ([48], accessed on August 31, 2012). The database contains 2369, 925, and 397 carefully normalised expression profiles, respectively. Further, we use gene expression compendia of Escherichia coli and Shewanella oneidensis downloaded from the Many Microbe Microarrays Database (M^{3D},[49], accessed on September 6, 2012), which contain 907 and 245 expression profiles, respectively. Gene–gene co-expression levels are estimated by absolute Spearman rank correlation. Values lower than 0.5 are hard-thresholded to 0, except for the datasets from M^{3D}, which are thresholded at 0.8 and 0.85, respectively. All final correlation coefficients are statistically significant (Storey’s q < 0. 001). Only the genes detected in at least 75% of the profiles are evaluated.
Bio-molecular networks used in the analyses
Protein-protein interaction networks | |||||||
---|---|---|---|---|---|---|---|
Source | StringDB | IntAct | Ref. [46] | Ref. [45] | |||
Species | ecoli | ccres | cjeju | mmusc | hsapi | scere | hsapi |
Vertices | 822 | 477 | 369 | 7977 | 8984 | 2384 | 9141 |
Edges | 1777 | 601 | 687 | 1594 | 26818 | 16070 | 41456 |
Gene co-expression networks | |||||||
Source | Colombos | M3D | |||||
Species | ecoli | sente | bsubt | ecoli | sonei | ||
Vertices | 1219 | 1104 | 2212 | 2162 | 2358 | ||
Edges | 5589 | 4731 | 11181 | 4379 | 3823 |
Computational complexity
Accuracy
To determine the quality of the resultant alignments, we estimate their sensitivity and coverage. As there is no gold standard with which to compare the results, we define sensitivity as the fraction of the aligned pairs, or Græmlin equivalence classes, which share the eggNOG orthologous group among all aligned pairs or classes. This measure of sensitivity is intrinsically biased, as the eggNOG orthologous groups are based on sequence comparison. Thus, the vertices which are orthologous, yet their sequences have diverged beyond recognition by the methods used to construct the eggNOG orthologous groups, do not contribute to this measure. We define coverage as the fraction of the eggNOG orthologous groups shared by the two species and correctly identified by the network alignment. Specifically, for GraphAlignment, let NA be the number of aligned pairs and NC be the number of the correctly aligned pairs in which the vertices (proteins or genes) belong to the same orthologous group as defined by eggNOG. Let NO be the total number of orthologous groups shared by the vertices of the networks being compared. Then, we define the sensitivity as NC/NA and coverage as NC/NO. For Græmlin, we define NA as the number of equivalence classes in which both species are represented. As in case of the simulated networks, we consider two rules for counting the number of correctly aligned equivalence classes NC: an equivalence class is correctly aligned either when all vertices are in the same eggNOG orthologous group and there is no vertex belonging to a different orthologous group in the class (the strict rule), or we consider the class correctly aligned whenever any two vertices belong to the same orthologous group (the relaxed rule). As the relaxed rule cannot decide between protein families, we will concentrate on the strict rule. Definition of the sensitivity and coverage remain the same.
GraphAlignment and Græmlin performance on empirical bio-molecular networks
Comparison | Escherichia coli vs. Caulobacter crescentus | Escherichia coli vs. Campylobacter jejuni | ||||
---|---|---|---|---|---|---|
Algorithm | GraphAlignment | Græmlin | Blast BBH | GraphAlignment | Græmlin | Blast BBH |
NA | 445 | 467 | 462 | 354 | 363 | 357 |
NC | 319 | 309 (333) | 333 | 247 | 241 (253) | 253 |
NO | 331 | 331 | 331 | 255 | 255 | 255 |
NC / NA [%] | 71.7 | 66.2 (71.3) | 72.1 | 69.8 | 66.3 (69.7) | 70.9 |
NC / NO [%] | 96.4 | 93.4 (101) | 101 | 96.9 | 94.5 (99.2) | 99.2 |
Edge / vertex score | 2505 / 2774 | - | - | 2592 / 2253 | - | - |
Comparison | Homo sapiens vs. Mus musculus | Homo sapiens vs. Saccharomyces cerevisiae | ||||
Algorithm | GraphAlignment | Græmlin | Blast BBH | GraphAlignment | Græmlin | Blast BBH |
NA | 7919 | 7907 | 7862 | 2369 | 1213 | 988 |
NC | 5743 | 6327 | 6375 | 581 | 869 (882) | 808 |
NO | 6402 | 6402 | 6402 | 965 | 965 | 965 |
NC / NA [%] | 72.5 | 80.0 (80.0) | 81.1 | 24.5 | 71.6 (72.7) | 81.8 |
NC / NO [%] | 89.7 | 98.8 (98.8) | 99.6 | 60.2 | 90.1 (91.4) | 83.7 |
Edge / vertex score | 2034 / 64661 | - | - | 20025 / 3963 | - | - |
GraphAlignment and Græmlin performance on empirical bio-molecular networks
Comparison | Escherichia coli vs. Salmonella enterica | Escherichia coli vs. Bacillus subtilis | ||||
---|---|---|---|---|---|---|
Algorithm | GraphAlignment | Græmlin | Blast BBH | GraphAlignment | Græmlin | Blast BBH |
NA | 624 | 687 | 662 | 585 | 459 | 401 |
NC | 539 | 492 (562) | 557 | 259 | 237 (296) | 274 |
NO | 543 | 543 | 543 | 284 | 284 | 284 |
NC / NA [%] | 86.4 | 71.6 (81.8) | 84.1 | 44.3 | 51.6 (64.5) | 68.3 |
NC / NO [%] | 99.3 | 90.6 (104) | 103 | 91.2 | 83.5 (104) | 96.5 |
Edge / vertex score | 1453 / 4789 | - | - | 1979 / 2550 | - | - |
Conclusions
Here we describe a software package for alignment of biomolecular networks based on a hybrid method developed in[28], GraphAlignment, and compare it to the algorithm Græmlin 2.0. We find advantages on both sides: the standalone Græmlin is able to perform multiple network comparisons and provides additional functionalities, e.g., clustering. As revealed on simulated data, GraphAlignment outperforms Græmlin in the use of interaction information for network alignment. We attribute the observed differences to the full use of interaction information: when an edge between a pair of aligned nodes is absent in both networks, GraphAlignment will typically reward the alignment of the nodes by a small score; Græmlin does not consider this piece of information. Consequently, Græmlin tends to align dense conserved clusters. This behaviour is advantageous for detection of such clusters, but may not be optimal in global alignment of sparse networks.
Comparison of empirical bacterial protein-protein interaction networks shows that GraphAlignment performs slightly better than Græmlin considering both sensitivity and coverage. Comparing the interaction networks of human and mouse based on the IntAct database, the situation is reversed. Moreover, we have observed limitations of the GraphAlignment algorithm in comparison of yeast and human protein-protein interaction networks, where the performance of the algorithm is decreased, most probably because the Bayesian scheme cannot deal with biased data or with the heterogenous rate of edge dynamics. On bacterial gene co-expression networks, GraphAlignment provides better coverage than Græmlin, while the sensitivity of both algorithms is similar. Considering the computational complexity, GraphAlignment is as efficient as Græmlin on small bacterial networks, while it lags significantly on large eukaryotic networks.
The simplicity and generality of GraphAlignment edge scoring makes this algorithm an appropriate choice for global alignment of networks. The underlying model is independent of the interpretation of edge weights, i.e., whether these weights represent probabilities of interaction between adjacent vertices or measure interaction strength. Since the algorithm is based on a well-defined evolutionary model, its parameters can be optimized by Bayesian methods. The GraphAlignment procedure of data input, estimation of scoring parameters and alignment of the networks is thoroughly documented in the package vignette, which also contains example sessions. Furthermore, we have shown that GraphAlignment is more robust to noise, an intrinsic factor of biological data, which is represented in our simulated data by spurious vertex similarities.
Availability and requirements
The GraphAlignment algorithm is provided as an R package available from Bioconductorhttp://www.bioconductor.org and runs on all major platforms. Computationally intensive routines are coded in C. The software package can be used freely and with no restrictions for non-commercial purposes. It contains a code implementing the Jonker-Volgenant algorithm[41] to solve linear assignment problems. The code was written by Roy Jonker, MagicLogic Optimization Inc. and is copyrighted, 2003 MagicLogic Systems Inc., Canada. The code may be used freely for non-commercial purposes. For full details see the package vignette, the web pagehttp://www.thp.uni-koeln.de/∼berg/GraphAlignment and the case studies[28, 31].
Declarations
Acknowledgements
This work was supported by Deutsche Forschungsgemeinschaft [grants SFB 680, SFB-TR12, and BE 2478/2-1]; and by the Academy of Sciences of the Czech Republic [grant AV0Z50520514 to MK].
Authors’ Affiliations
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