ChainRank, a chain prioritisation method for contextualisation of biological networks

• DOI: 10.1186/s12859-015-0864-x
• Publication Date: 2016-01-04T23:46:55Z
• Authors: Ákos Tényi; Pedro de Atauri; David Gomez-Cabrero; Isaac Cano; Kim Clarke; Francesco Falciani; Marta Cascante; Josep Roca; Dieter Maier
• Keywords: Proteomics; 0301 basic medicine; Bioinformatics; Biochemistry; Models, Biological; Biologia computacional; Computational biology; Pulmonary Disease, Chronic Obstructive; 03 medical and health sciences; Bioinformàtica; Sistemes biològics; Humans; Metabolomics; Protein Interaction Maps; Databases, Protein; Muscle, Skeletal; Molecular Biology; Biological systems; Methodology Article; Computational Biology; Proteins; Computer Science Applications; Proteïnes; Signal Transduction

Abstract Background Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).

Results Our method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation.

Conclusions ChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank.