ChainRank, a chain prioritisation method for contextualisation of biological networks

BMC Bioinformatics

Methodology Article

Dieter Maier¹  Isaac Cano²  Ákos Tényi²  Josep Roca²  Marta Cascante³  Pedro de Atauri³  Kim Clarke⁴  Francesco Falciani⁴  David Gomez-Cabrero⁵ 

[1] Biomax Informatics AG, D-82152, Planegg, Germany;Hospital Clínic-Institut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain;Centro de Investigación en Red de Enfermedades Respiratorias (CibeRes), 07110, Palma de Mallorca, Spain;Hospital Clínic-Institut d’Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Research Institute, Universitat de Barcelona, C/Villarroel 170, 08036, Barcelona, Spain;Departament de Bioquimica i Biologia Molecular, Facultat de Biologia-IBUB, Universitat de Barcelona, 08028, Barcelona, Spain;Integrative Systems Biology, University of Liverpool, L69 3BX, Liverpool, UK;Unit of computational Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institute and Karolinska University Hospital, SE-171 76, Stockholm, Sweden;

关键词: Biological networks;    Protein-protein interaction;    Data integration;    Filtering;    Computational biology;    Bioinformatics;    Systems biology;    COPD;   

DOI  :  10.1186/s12859-015-0864-x

 received in 2015-06-18, accepted in 2015-12-17,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundAdvances 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).ResultsOur 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.ConclusionsChainRank 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.

【 授权许可】

CC BY   
© Tényi et al. 2015