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Träfflista för sökning "WFRF:(Marchal Kathleen) "

Sökning: WFRF:(Marchal Kathleen)

  • Resultat 1-6 av 6
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1.
  • De Maeyer, Dries, et al. (författare)
  • Network-Based Analysis of eQTL Data to Prioritize Driver Mutations
  • 2016
  • Ingår i: Genome Biology and Evolution. - Oxford : Oxford University Press. - 1759-6653 .- 1759-6653. ; 23;8:3, s. 481-494
  • 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)
  • PheNetic : network-based interpretation of molecular profiling data
  • 2015
  • Ingår i: Nucleic Acids Research. - : Oxford University Press. - 0305-1048 .- 1362-4962. ; 43:W1, s. 244-250
  • 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)
  • PheNetic : Network-based interpretation of unstructured gene lists in E. coli
  • 2013
  • Ingår i: Molecular Biosystems. - Cambridge : Royal Society of Chemistry. - 1742-206X .- 1742-2051. ; 9:7, s. 1594-1603
  • 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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4.
  • Fernández-Niño, Miguel, et al. (författare)
  • Identification of novel genes involved in acetic acid tolerance of Saccharomyces cerevisiae using pooled-segregant RNA sequencing
  • 2018
  • Ingår i: FEMS yeast research. - : Oxford University Press. - 1567-1356 .- 1567-1364. ; 18:8
  • Tidskriftsartikel (refereegranskat)abstract
    • Acetic acid tolerance of the yeast Saccharomyces cerevisiae is manifested in several quantifiable parameters, of which the duration of the latency phase is one of the most studied. It has been shown recently that the latter parameter is mostly determined by a fraction of cells within the population that resumes proliferation upon exposure to acetic acid. The aim of the current study was to identify genetic determinants of the difference in this parameter between the highly tolerant strain MUCL 11987-9 and the laboratory strain CEN. PK113-7D. To this end, a combination of genetic mapping and pooled-segregant RNA sequencing was applied as a new approach. The genetic mapping data revealed four loci with a strong linkage to strain MUCL 11987-9, each containing still a large number of genes making the identification of the causal ones by traditional methods a laborious task. The genes were therefore prioritized by pooled-segregant RNA sequencing, which resulted in the identification of six genes within the identified loci showing differential expression. The relevance of the prioritized genes for the phenotype was verified by reciprocal hemizygosity analysis. Our data revealed the genes ESP1 and MET22 as two, so far unknown, genetic determinants of the size of the fraction of cells resuming proliferation upon exposure to acetic acid.
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5.
  • Le Van, Thanh, et al. (författare)
  • Ranked Tiling
  • 2014
  • Ingår i: Machine Learning and Knowledge Discovery in Databases. - Berlin, Heidelberg : Springer. - 9783662448519 - 9783662448502 ; , s. 98-113
  • Konferensbidrag (refereegranskat)abstract
    • Tiling is a well-known pattern mining technique. Traditionally, it discovers large areas of ones in binary databases or matrices, where an area is defined by a set of rows and a set of columns. In this paper, we introduce the novel problem of ranked tiling, which is concerned with finding interesting areas in ranked data. In this data, each transaction defines a complete ranking of the columns. Ranked data occurs naturally in applications like sports or other competitions. It is also a useful abstraction when dealing with numeric data in which the rows are incomparable.We introduce a scoring function for ranked tiling, as well as an algorithm using constraint programming and optimization principles. We empirically evaluate the approach on both synthetic and real-life datasets, and demonstrate the applicability of the framework in several case studies. One case study involves a heterogeneous dataset concerning the discovery of biomarkers for different subtypes of breast cancer patients. An analysis of the tiles by a domain expert shows that our approach can lead to the discovery of novel insights.
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6.
  • Reyna, Matthew A, et al. (författare)
  • Pathway and network analysis of more than 2500 whole cancer genomes
  • 2020
  • Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11
  • Tidskriftsartikel (refereegranskat)abstract
    • The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
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  • Resultat 1-6 av 6

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