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Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.

ABSTRACT: It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system.

SUBMITTER: Tripathi S

PROVIDER: S-EPMC4797184 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.

Tripathi Shailesh S Moutari Salissou S Dehmer Matthias M Emmert-Streib Frank F

BMC bioinformatics 20160318

<h4>Background</h4>It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next element ...[more]

PMID: 26987731

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Project description:Biological networks catalog the complex web of interactions happening between different molecules, typically proteins, within a cell. These networks are known to be highly modular, with groups of proteins associated with specific biological functions. Human diseases often arise from the dysfunction of one or more such proteins of the biological functional group. The ability, to identify and automatically extract these modules has implications for understanding the etiology of different diseases as well as the functional roles of different protein modules in disease. The recent DREAM challenge posed the problem of identifying disease modules from six heterogeneous networks of proteins/genes. There exist many community detection algorithms, but all of them are not adaptable to the biological context, as these networks are densely connected and the size of biologically relevant modules is quite small. The contribution of this study is 3-fold: first, we present a comprehensive assessment of many classic community detection algorithms for biological networks to identify non-overlapping communities, and propose heuristics to identify small and structurally well-defined communities-core modules. We evaluated our performance over 180 GWAS datasets. In comparison to traditional approaches, with our proposed approach we could identify 50% more number of disease-relevant modules. Thus, we show that it is important to identify more compact modules for better performance. Next, we sought to understand the peculiar characteristics of disease-enriched modules and what causes standard community detection algorithms to detect so few of them. We performed a comprehensive analysis of the interaction patterns of known disease genes to understand the structure of disease modules and show that merely considering the known disease genes set as a module does not give good quality clusters, as measured by typical metrics such as modularity and conductance. We go on to present a methodology leveraging these known disease genes, to also include the neighboring nodes of these genes into a module, to form good quality clusters and subsequently extract a "gold-standard set" of disease modules. Lastly, we demonstrate, with justification, that "overlapping" community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.

Project description:BackgroundCommunity detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities.ResultsHere we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest.ConclusionsFor Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time.

Project description:Biological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interaction networks, metabolic networks, and regulatory networks to identify functional modules and assign functions to certain components of the system. Network modules do not occur by chance, so identification of modules is likely to capture the biologically meaningful interactions in large-scale PPI data. Unfortunately, existing computer-based clustering methods developed to find those modules are either not so accurate or too slow.We devised a new methodology called SCAN (Structural Clustering Algorithm for Networks) that can efficiently find clusters or functional modules in complex biological networks as well as hubs and outliers. More specifically, we demonstrated that we can find functional modules in complex networks and classify nodes into various roles based on their structures. In this study, we showed the effectiveness of our methodology using the budding yeast (Saccharomyces cerevisiae) protein-protein interaction network. To validate our clustering results, we compared our clusters with the known functions of each protein. Our predicted functional modules achieved very high purity comparing with state-of-the-art approaches. Additionally the theoretical and empirical analysis demonstrated a linear running-time of the algorithm, which is the fastest approach for networks.We compare our algorithm with well-known modularity based clustering algorithm CNM. We successfully detect functional groups that are annotated with putative GO terms. Top-10 clusters with minimum p-value theoretically prove that newly proposed algorithm partitions network more accurately then CNM. Furthermore, manual interpretations of functional groups found by SCAN show superior performance over CNM.

Dataset Information

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.

Publications

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.

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