| 1. |
- De Maeyer, Dries, et al.
(författare)
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Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
- 2016
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Ingår i: Genome Biology and Evolution. - Oxford : Oxford University Press. - 1759-6653. ; 23;8:3, s. 481-494
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Tidskriftsartikel (refereegranskat)abstract
- In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html.
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| 2. |
- De Maeyer, Dries, et al.
(författare)
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PheNetic : network-based interpretation of molecular profiling data
- 2015
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Ingår i: Nucleic Acids Research. - : Oxford University Press. - 0305-1048 .- 1362-4962. ; 43:W1, s. 244-250
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Tidskriftsartikel (refereegranskat)abstract
- Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.
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| 3. |
- De Maeyer, Dries, et al.
(författare)
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PheNetic : Network-based interpretation of unstructured gene lists in E. coli
- 2013
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Ingår i: Molecular Biosystems. - Cambridge : Royal Society of Chemistry. - 1742-206X .- 1742-2051. ; 9:7, s. 1594-1603
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Tidskriftsartikel (refereegranskat)abstract
- At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network.
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