MOTIVATION: The ultimate goal of any experiment is to understand the biological phenomena underlying the condition investigated. This process often results in genes network through which a certain biological mechanism is explained. Such networks have been proven to be extremely useful, for the prediction of mechanisms of action of drugs or the responses of an organism to a specific impact (e.g. a disease, a treatment, etc.). Here, we introduce an approach able to build a network that captures the putative mechanisms at play in the given condition, by using datasets from multiple experiments studying the same phenotype. This method takes advantage of known interactions extracted from multiple sources such as protein-protein interactions and curated biological pathways. Based on such prior knowledge, we overcome the drawbacks of snap-shot data by considering the possible effects of each gene on its neighbors. RESULTS: We show the effectiveness of this approach in three different case studies and validate the results in two ways considering the identified genes and interactions between them. We compare our findings with the results of two widely-used methods in the same category as well as the classical approach of selecting differentially expressed (DE) genes in an investigated condition. The results show that 'neighbor-net' analysis is able to report biological mechanisms that are significantly relevant to the given diseases in all the three case studies, and performs better compared to all reference methods using both validation approaches. AVAILABILITY AND IMPLEMENTATION: The proposed method is implemented as in R and will be available an a Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The ultimate goal of any experiment is to understand the biological phenomena underlying the condition investigated. This process often results in genes network through which a certain biological mechanism is explained. Such networks have been proven to be extremely useful, for the prediction of mechanisms of action of drugs or the responses of an organism to a specific impact (e.g. a disease, a treatment, etc.). Here, we introduce an approach able to build a network that captures the putative mechanisms at play in the given condition, by using datasets from multiple experiments studying the same phenotype. This method takes advantage of known interactions extracted from multiple sources such as protein-protein interactions and curated biological pathways. Based on such prior knowledge, we overcome the drawbacks of snap-shot data by considering the possible effects of each gene on its neighbors. RESULTS: We show the effectiveness of this approach in three different case studies and validate the results in two ways considering the identified genes and interactions between them. We compare our findings with the results of two widely-used methods in the same category as well as the classical approach of selecting differentially expressed (DE) genes in an investigated condition. The results show that 'neighbor-net' analysis is able to report biological mechanisms that are significantly relevant to the given diseases in all the three case studies, and performs better compared to all reference methods using both validation approaches. AVAILABILITY AND IMPLEMENTATION: The proposed method is implemented as in R and will be available an a Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Tanya Barrett; Stephen E Wilhite; Pierre Ledoux; Carlos Evangelista; Irene F Kim; Maxim Tomashevsky; Kimberly A Marshall; Katherine H Phillippy; Patti M Sherman; Michelle Holko; Andrey Yefanov; Hyeseung Lee; Naigong Zhang; Cynthia L Robertson; Nadezhda Serova; Sean Davis; Alexandra Soboleva Journal: Nucleic Acids Res Date: 2012-11-27 Impact factor: 16.971
Authors: Manway Liu; Arthur Liberzon; Sek Won Kong; Weil R Lai; Peter J Park; Isaac S Kohane; Simon Kasif Journal: PLoS Genet Date: 2007-06 Impact factor: 5.917