Computing topological parameters of biological networks

Assenov, Yassen; Ramírez, Fidel; Schelhorn, Sven-Eric; Lengauer, Thomas; Albrecht, Mario

doi:10.1093/bioinformatics/btm554

Cited by 1,546 publications

(1,137 citation statements)

References 15 publications

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“…∆SSA1 network with 4402 nodes, and 19373 edges, and the ∆SSB1 network with 4402 nodes and 18585 edges. Network and node attributes were calculated within Cytoscape 41 using the NetworkAnalyser 42 plug‐in. The calculated parameters together with their definitions are listed below:

Node degree; degree of node n is the number of edges incident to n .

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

Jarnuczak¹,

Eyers

Schwartz³

et al. 2015

Proteomics

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Molecular chaperones play an important role in protein homeostasis and the cellular response to stress. In particular, the HSP70 chaperones in yeast mediate a large volume of protein folding through transient associations with their substrates. This chaperone interaction network can be disturbed by various perturbations, such as environmental stress or a gene deletion. Here, we consider deletions of two major chaperone proteins, SSA1 and SSB1, from the chaperone network in Sacchromyces cerevisiae. We employ a SILAC‐based approach to examine changes in global and local protein abundance and rationalise our results via network analysis and graph theoretical approaches. Although the deletions result in an overall increase in intracellular protein content, correlated with an increase in cell size, this is not matched by substantial changes in individual protein concentrations. Despite the phenotypic robustness to deletion of these major hub proteins, it cannot be simply explained by the presence of paralogues. Instead, network analysis and a theoretical consideration of folding workload suggest that the robustness to perturbation is a product of the overall network structure. This highlights how quantitative proteomics and systems modelling can be used to rationalise emergent network properties, and how the HSP70 system can accommodate the loss of major hubs.

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Node degree; degree of node n is the number of edges incident to n .

…”

Section: Methodsmentioning

confidence: 99%

“…A more detailed description of network parameters together with comprehensive references to literature is also available from 42 and on the web at http://med.bioinf.mpi‐inf.mpg.de/netanalyzer/help/2.7/ …”

Section: Methodsmentioning

confidence: 99%

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

Jarnuczak¹,

Eyers

Schwartz³

et al. 2015

Proteomics

View full text Add to dashboard Cite

show abstract

“…The Betweenness Centrality and Clustering Coefficient of each protein were calculated in reference to the entire protein-protein interaction network using the Network Analyzer plugin 35 in Cytoscape. 36 …”

Section: Network Parametersmentioning

confidence: 99%

Domain distribution and intrinsic disorder in hubs in the human protein–protein interaction network

2010

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Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non-hubs in protein-protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non-hubs. We found that proteins with a more diverse domain composition were over-represented in hubs when compared with non-hubs, with the number of interactions in hubs increasing with domain diversity. Conversely, the fraction of intrinsic disorder in hubs decreased with increasing number of ordered domains. The difference in the levels of disorder was more prominent in hubs and non-hubs with fewer domains. Functional analysis showed that hubs were enriched in kinase and adaptor domains acting primarily in signal transduction and transcription regulation, whereas non-hubs had more DNA-binding domains and were involved in catalytic activity. Consistent with the differences in the functional nature of their domains, hubs with two or more domains were more likely to connect distinct functional modules in the interaction network when compared with single domain hubs. We conclude that the availability of greater number and diversity of ordered domains, in addition to the tendency to have promiscuous domains, differentiates hubs from non-hubs and provides an additional means of achieving interaction promiscuity. Further, hubs with fewer domains use greater levels of intrinsic disorder to facilitate interaction promiscuity with the prevalence of disorder decreasing with increasing number of ordered domains.

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“…In this study, we examined the topological features of networks, including degree (connectivity), degree distribution, clustering coefficient and Betweenness [43]. The topological features of networks were implemented in the RBGL package (R version 2.15.2, Bioconductor version 2.3) and a cytoscape plugin NetworkAnalyzer (Version 2.8, Cytoscape 2.8) [44]. And the network visualization was performed using Cytoscape (http://www.cytoscape.org/).…”

Section: Methodsmentioning

confidence: 99%

Detection of type 2 diabetes related modules and genes based on epigenetic networks

et al. 2014

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BackgroundType 2 diabetes (T2D) is one of the most common chronic metabolic diseases characterized by insulin resistance and the decrease of insulin secretion. Genetic variation can only explain part of the heritability of T2D, so there need new methods to detect the susceptibility genes of the disease. Epigenetics could establish the interface between the environmental factor and the T2D Pathological mechanism.ResultsBased on the network theory and by combining epigenetic characteristics with human interactome, the weighted human DNA methylation network (WMPN) was constructed, and a T2D-related subnetwork (TMSN) was obtained through T2D-related differentially methylated genes. It is found that TMSN had a T2D specific network structure that non-fatal metabolic disease causing genes were often located in the topological and functional periphery of network. Combined with chromatin modifications, the weighted chromatin modification network (WCPN) was built, and a T2D-related chromatin modification pattern subnetwork was obtained by the TMSN gene set. TCSN had a densely connected network community, indicating that TMSN and TCSN could represent a collection of T2D-related epigenetic dysregulated sub-pathways. Using the cumulative hypergeometric test, 24 interplay modules of DNA methylation and chromatin modifications were identified. By the analysis of gene expression in human T2D islet tissue, it is found that there existed genes with the variant expression level caused by the aberrant DNA methylation and (or) chromatin modifications, which might affect and promote the development of T2D.ConclusionsHere we have detected the potential interplay modules of DNA methylation and chromatin modifications for T2D. The study of T2D epigenetic networks provides a new way for understanding the pathogenic mechanism of T2D caused by epigenetic disorders.

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Computing topological parameters of biological networks

Cited by 1,546 publications

References 15 publications

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

Quantitative proteomics and network analysis of SSA1 and SSB1 deletion mutants reveals robustness of chaperone HSP70 network in Saccharomyces cerevisiae

Domain distribution and intrinsic disorder in hubs in the human protein–protein interaction network

Detection of type 2 diabetes related modules and genes based on epigenetic networks

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