Development and implementation of an algorithm for detection of protein complexes in large interaction networks

Altaf-Ul-Amin, Md.; Shinbo, Yoko; Mihara, Kenji; Kurokawa, Ken; Kanaya, Shigehiko

doi:10.1186/1471-2105-7-207

Cited by 432 publications

(183 citation statements)

References 15 publications

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“…For the purpose of comparing ModuleSearch with other programs, we tested InterViewer (Han and Ju 2003;Cui et al 2008), CFinder (Adamcsek et al 2007), MCODE (Bader and Hogue 2003), IPCA (Li et al 2008) and DPClus (Altaf-Ul-Amin et al 2006) on the yeast protein -protein interaction data, and selected the modules with 3-20 proteins. InterViewer found 116 modules, and 111 of them range from 3 to 20.…”

Section: Comparison With Other Programsmentioning

confidence: 99%

“…IPCA (Li et al 2008) finds overlapping subgraphs from a protein-protein interaction network using the diameter and density of sub-graphs, but generates a large number of sub-graphs that are slightly different from each other by one or two proteins. DPClus (Altaf-Ul-Amin et al 2006) allows the user to find either overlapping or non-overlapping protein complexes from a proteinprotein interaction network.…”

Section: Introductionmentioning

confidence: 99%

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ModuleSearch: finding functional modules in a protein–protein interaction network

Cui

Shrestha

Han

2012

Computer Methods in Biomechanics and Biomedical Engineering

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Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein-protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein-protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.

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Section: Comparison With Other Programsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ModuleSearch: finding functional modules in a protein–protein interaction network

Cui

Shrestha

Han

2012

Computer Methods in Biomechanics and Biomedical Engineering

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“…Interaction-based approaches such as topological analysis (e.g., shortest path search Yu et al, 2014, centrality analysis Carrera et al, 2009, and network module detection Altaf-Ul-Amin et al, 2006), correlation network analysis (Provart, 2012), or enrichment analysis (Hung et al, 2012) have been used to construct and analyze biological networks from omics data. For example, GeneMANIA (Montojo et al, 2010; Zuberi et al, 2013) is a web-based interaction network for the visualization of physical, genetic, and functional interactions.…”

Section: Network Visualization and Pathway Analysis Tools For Interacmentioning

confidence: 99%

Integrated network analysis and effective tools in plant systems biology

Fukushima¹,

Kanaya²,

Nishida³

2014

Front. Plant Sci.

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One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

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“…Amin et al develops a cluster periphery tracking algorithm (DPclus) to locate protein complexes by keeping seeking the periphery of a detected cluster. 8 Limin et al improves the DPclus to mine protein complexes with two topological constraints and reduces the number of parameters in the algorithm IPCA. 9 Based on both graph-theoretical and gene-ontological properties, King et al proposes the restricted neighbors searching clustering (RNSC) to detect protein complexes.…”

Section: Introductionmentioning

confidence: 99%

Detecting Protein Complexes Based on Sequence Information in the Weighted Protein–Protein Interaction Network

Yang¹,

Wang²,

Lin³

et al. 2012

Jnl of Comp & Theo Nano

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We presented a novel algorithm called DPCWN (Detecting Protein Complexes based on Sequence Information in Weighted PPI Network) to discover protein complexes from the weighted PPI network.In the algorithm, biological information (protein amino acid background frequency) is introduced to rank cluster and the combination of density, network diameter and included angle cosine is used to locate the complexes. First, compared with other three competing methods, the experimental results demonstrated that DPCWN can achieve better performance. Second, the feasibility and the improved performance by adding the biological properties are proved. Third, the detected clusters are validated by biological function annotation. Finally, the robustness of our algorithm is demonstrated by the experimental reports. Therefore, DPCWN can provide a useful framework for the study of more comprehensive properties in the biological network.

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Development and implementation of an algorithm for detection of protein complexes in large interaction networks

Cited by 432 publications

References 15 publications

ModuleSearch: finding functional modules in a protein–protein interaction network

ModuleSearch: finding functional modules in a protein–protein interaction network

Integrated network analysis and effective tools in plant systems biology

Detecting Protein Complexes Based on Sequence Information in the Weighted Protein–Protein Interaction Network

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