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Network-based analysis of eQTL data to prioritize driver mutations

(2016) GENOME BIOLOGY AND EVOLUTION. 8(3). p.481-494
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Bioinformatics: from nucleotids to networks (N2N)
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
Keywords
IBCN, gene prioritization, experimental evolution, biological networks, coexisting ecotypes, drug resistance, TERM EXPERIMENTAL EVOLUTION, SET ENRICHMENT ANALYSIS, ESCHERICHIA-COLI, BALANCED POLYMORPHISM, ANTIBIOTIC-RESISTANCE, BENEFICIAL MUTATIONS, BACTERIAL POPULATION, COLORECTAL CANCERS, ADAPTATION, DYNAMICS

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Citation

Please use this url to cite or link to this publication:

Chicago
De Maeyer, Dries, Bram Weytjens, Luc De Raedt, and Kathleen Marchal. 2016. “Network-based Analysis of eQTL Data to Prioritize Driver Mutations.” Genome Biology and Evolution 8 (3): 481–494.
APA
De Maeyer, D., Weytjens, B., De Raedt, L., & Marchal, K. (2016). Network-based analysis of eQTL data to prioritize driver mutations. GENOME BIOLOGY AND EVOLUTION, 8(3), 481–494.
Vancouver
1.
De Maeyer D, Weytjens B, De Raedt L, Marchal K. Network-based analysis of eQTL data to prioritize driver mutations. GENOME BIOLOGY AND EVOLUTION. 2016;8(3):481–94.
MLA
De Maeyer, Dries, Bram Weytjens, Luc De Raedt, et al. “Network-based Analysis of eQTL Data to Prioritize Driver Mutations.” GENOME BIOLOGY AND EVOLUTION 8.3 (2016): 481–494. Print.
@article{7206826,
  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},
  author       = {De Maeyer, Dries and Weytjens, Bram and De Raedt, Luc and Marchal, Kathleen},
  issn         = {1759-6653},
  journal      = {GENOME BIOLOGY AND EVOLUTION},
  keyword      = {IBCN,gene prioritization,experimental evolution,biological networks,coexisting ecotypes,drug resistance,TERM EXPERIMENTAL EVOLUTION,SET ENRICHMENT ANALYSIS,ESCHERICHIA-COLI,BALANCED POLYMORPHISM,ANTIBIOTIC-RESISTANCE,BENEFICIAL MUTATIONS,BACTERIAL POPULATION,COLORECTAL CANCERS,ADAPTATION,DYNAMICS},
  language     = {eng},
  number       = {3},
  pages        = {481--494},
  title        = {Network-based analysis of eQTL data to prioritize driver mutations},
  url          = {http://dx.doi.org/10.1093/gbe/evw010},
  volume       = {8},
  year         = {2016},
}

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