Dataset Information

Topological scoring of protein interaction networks.

ABSTRACT: It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic datasets from affinity purifications. Data is analyzed in a parallel fashion where a prey protein is scored in an individual affinity purification by aggregating information from the entire dataset. Topological scores span a broad range of values indicating the enrichment of an individual protein in every bait protein purification. TopS is applied to interaction networks derived from human DNA repair proteins and yeast chromatin remodeling complexes. TopS highlights potential direct protein interactions and modules within complexes. TopS is a rapid method for the efficient and informative computational analysis of datasets, is complementary to existing analysis pipelines, and provides important insights into protein interaction networks.

SUBMITTER: Sardiu ME

PROVIDER: S-EPMC6408525 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Topological scoring of protein interaction networks.

Sardiu Mihaela E ME Gilmore Joshua M JM Groppe Brad D BD Dutta Arnob A Florens Laurence L Washburn Michael P MP

Nature communications 20190308 1

It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic datasets from affinity purifications. Data is analyzed in a parallel fashion where a prey protein is scored in an individual affinity purification by aggregating in ...[more]

PMID: 30850613

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Project description:BACKGROUND:Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks. RESULTS:It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited. Here we present a novel method for analysis of chromatin interaction networks aimed towards identifying characteristic topological features of interaction graphs and confirming their potential significance in chromatin architecture. Our method automatically identifies all connected components with an assigned significance score above a given threshold. These components can be subjected afterwards to different assessment methods for their biological role and/or significance. The method was applied to the largest PCHi-C data set available to date that contains interactions for 17 haematopoietic cell types. The results demonstrate strong evidence of well-pronounced component structure of chromatin interaction networks and provide some characterisation of this component structure. We also performed an indicative assessment of potential biological significance of identified network components with the results confirming that the network components can be related to specific biological functionality. CONCLUSIONS:The obtained results show that the topological structure of chromatin interaction networks can be well described in terms of isolated connected components of the network and that formation of these components can be often explained by biological features of functionally related gene modules. The presented method allows automatic identification of all such components and evaluation of their significance in PCHi-C dataset for 17 haematopoietic cell types. The method can be adapted for exploration of other chromatin interaction data sets that include information about sufficiently large number of different cell types, and, in principle, also for analysis of other kinds of cell type-specific networks.

Dataset Information

Topological scoring of protein interaction networks.

Publications

Topological scoring of protein interaction networks.

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