BioNet: an R-Package for the functional analysis of biological networks

Beißer, Daniela; Klau, Gunnar W.; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

doi:10.1093/bioinformatics/btq089

Cited by 209 publications

(206 citation statements)

References 8 publications

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“…Chuang et al 13 introduced a method to find subnetworks of interaction networks whose gene expression predicts progression to metastasis in breast cancer. This work extends an earlier approach to find differentially expressed subnetworks introduced in Ideker et al 14 and extended in Dittrich et al 15 and in Beisser et al 16 Also, Vaske et al 17 present a method to infer patient-specific genetic activities. The latter method relies on curated pathway interactions among genes and predict the degree of alteration of pathway's activities in the patient using probabilistic inference.…”

Section: Introductionsupporting

confidence: 53%

“…To the best of our knowledge this is the first approach to find subnetworks whose mutations are correlated with survival. Previous methods [13][14][15][16] utilized gene expression data and incorporate a variety of different scoring methods to identify subnetworks/modules and to compute their statistical significance. We accomplish this goal by extending the HotNet algorithm previously introduced by some of us.…”

Section: Introductionmentioning

confidence: 99%

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Discovery of Mutated Subnetworks Associated With Clinical Data in Cancer

et al. 2011

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A major goal of cancer sequencing projects is to identify genetic alterations that determine clinical phenotypes, such as survival time or drug response. Somatic mutations in cancer are typically very diverse, and are found in different sets of genes in different patients. This mutational heterogeneity complicates the discovery of associations between individual mutations and a clinical phenotype. This mutational heterogeneity is explained in part by the fact that driver mutations, the somatic mutations that drive cancer development, target genes in cellular pathways, and only a subset of pathway genes is mutated in a given patient. Thus, pathway-based analysis of associations between mutations and phenotype are warranted. Here, we introduce an algorithm to find groups of genes, or pathways, whose mutational status is associated to a clinical phenotype without prior definition of the pathways. Rather, we find subnetworks of genes in an gene interaction network with the property that the mutational status of the genes in the subnetwork are significantly associated with a clinical phenotype. This new algorithm is built upon HotNet, an algorithm that finds groups of mutated genes using a heat diffusion model and a two-stage statistical test. We focus here on discovery of statistically significant correlations between mutated subnetworks and patient survival data. A similar approach can be used for correlations with other types of clinical data, through use of an appropriate statistical test. We apply our method to simulated data as well as to mutation and survival data from ovarian cancer samples from The Cancer Genome Atlas. In the TCGA data, we discover nine subnetworks containing genes whose mutational status is correlated with survival. Genes in four of these subnetworks overlap known pathways, including the focal adhesion and cell adhesion pathways, while other subnetworks are novel.

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Section: Introductionsupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Discovery of Mutated Subnetworks Associated With Clinical Data in Cancer

et al. 2011

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“…24 Mouse gene symbols were mapped to human gene symbols as before to allow using the human interactome from the BioGRID data base. 25 BioNet was then used to find the highest-scoring protein-protein interaction subnetwork from the significant differentially expressed genes with an absolute log2 fold change larger than 0.5.…”

Section: Microarray Gene Expression Analysismentioning

confidence: 99%

Increased expression of c-Jun in nonalcoholic fatty liver disease

Dorn

Engelmann

Saugspier

et al. 2014

Laboratory Investigation

108

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“…Several methods were developed to identify modules of nodes which are jointly affected by the condition of interest. Two publicly available examples are the Cytoscape plugin jActiveModules (Ideker et al ., 2002) and the R package BioNet (Beisser et al ., 2010). …”

Section: From Omics To Systems Biologymentioning

confidence: 99%

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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BioNet: an R-Package for the functional analysis of biological networks

Abstract: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.

Cited by 209 publications

References 8 publications

Discovery of Mutated Subnetworks Associated With Clinical Data in Cancer

Discovery of Mutated Subnetworks Associated With Clinical Data in Cancer

Increased expression of c-Jun in nonalcoholic fatty liver disease

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

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