ExprEssence - Revealing the essence of differential experimental data in the context of an interaction/regulation net-work

Warsow, Gregor; Greber, Boris; Falk, Steffi; Harder, Clemens; Siatkowski, Marcin; Schordan, Sandra; Som, Anup; Endlich, Nicole; Schöler, Hans R.; Repsilber, Dirk; Endlich, Karlhans; Fuellen, Georg

doi:10.1186/1752-0509-4-164

Cited by 72 publications

(72 citation statements)

References 61 publications

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“…We investigated high-resolution population networks using the open graph visualization platform Cytoscape (Shannon et al, 2003) and the plugin MultiColoredNodes (Warsow et al, 2010). In the final network visualization, the uppermost hierarchical population structure as inferred from SPC is presented in terms of node size, number of edges between nodes and width of edges.…”

Section: High-resolution Population Networkmentioning

confidence: 99%

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

et al. 2016

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Pollination is a key ecosystem service for agricultural systems and Western honey bees, Apis mellifera, are the most important managed pollinators. Major losses of managed honey bee colonies reinforced the need to take advantage of locally adapted subspecies and ecotypes to buffer populations against various stressors. However, introductions of non-native honey bees from distant lineages are likely to undermine respective conservation efforts unless reliable and cost effective tools can be used to identify hybridization. The purpose of this study is to characterize current population structure and genetic diversity, and to assess the degree of admixture between native and introduced honey bees. Moreover, we aim to select a reduced number of genetic markers to improve conservation management strategies. We take advantage of recent developments in next-generation sequencing and network-based clustering to investigate conservation efforts for the native European Dark honey bee, A. m. mellifera, which is threatened by introgression in most of its range. We collected whole-genome sequence information from haploid drones of A. m. mellifera, A. m. carnica, and Buckfast sampled throughout Switzerland (N = 81), as well as from four Swiss A. m. mellifera conservation areas (N = 39) and from one conservatory in the French Alps (N = 31). Population structure analyses based upon 3.375 M genome-wide SNPs discerned samples by subspecies and geographic origin (Switzerland or France). Ancestry inference indicated admixed individuals in all of the protected areas, calling for improved management efforts. After testing different subsets of ancestry informative SNPs using three different selection strategies (F ST , PCA-based or at random), as few as 50 SNPs are found to be sufficient to differentiate native from introduced honey bees. Therefore, our data suggests that a low-density SNP panel can be a precise and cost-effective tool to support conservation management efforts for managed pollinators.

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Section: High-resolution Population Networkmentioning

confidence: 99%

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

et al. 2016

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“…Expression levels for the three species were represented as log 2 (eye-antennal/wing) values in node colors using the MultiColoredNodes cytoscape plugin (version 2.4.12) (Warsow et al 2010). …”

Section: Gene Regulatory Network Visualizationmentioning

confidence: 99%

Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions

et al. 2012

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The identification of transcription factor binding sites, enhancers, and transcriptional target genes often relies on the integration of gene expression profiling and computational cis-regulatory sequence analysis. Methods for the prediction of cis-regulatory elements can take advantage of comparative genomics to increase signal-to-noise levels. However, gene expression data are usually derived from only one species. Here we investigate tissue-specific cross-species gene expression profiling by high-throughput sequencing, combined with cross-species motif discovery. First, we compared different methods for expression level quantification and cross-species integration using Tag-seq data. Using the optimal pipeline, we derived a set of genes with conserved expression during retinal determination across Drosophila melanogaster, Drosophila yakuba, and Drosophila virilis. These genes are enriched for binding sites of eye-related transcription factors including the zincfinger Glass, a master regulator of photoreceptor differentiation. Validation of predicted Glass targets using RNA-seq in homozygous glass mutants confirms that the majority of our predictions are expressed downstream from Glass. Finally, we tested nine candidate enhancers by in vivo reporter assays and found eight of them to drive GFP in the eye disc, of which seven colocalize with the Glass protein, namely, scrt, chp, dpr10, CG6329, retn, Lim3, and dmrt99B. In conclusion, we show for the first time the combined use of cross-species expression profiling with cross-species motif discovery

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“…A better understanding of the role of VRGs during infection can be drawn from an analysis of their context in the interaction network in which they exist [34,35]. Application of some basic concepts of networks theory can elucidate which of two possible orthogonal scenarios better describe VRGs: on the one hand, VRGs are essential elements with a high number of links in the interactome (i.e., hub genes) or, on the other hand, VRGs are elements randomly and sparsely distributed across the interactome network and are thus poorly connected.…”

Section: Viruses Preferentially Alter Highly Connected Genesmentioning

confidence: 99%

Detection of food-borne pathogens with DNA arrays on disk

Arnandis-Chover

Morais

Tortajada-Genaro

et al. 2012

Talanta

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ElsevierElena Fito, SF.; Carrera, J.; Rodrigo, J. (2011). A systems biology approach to the evolution of plant-virus interactions. Current Opinion in Plant Biology. 14(4):372-377. doi:10.1016/j.pbi.2011.03.013. Omic approaches to the analysis of plant-virus interactions are becoming increasingly popular. These types of data, in combination with models of interaction networks, will aid in revealing not only host components that are important for the virus life cycle, but also general patterns about the way in which different viruses manipulate host regulation of gene expression for their own benefit and possible mechanisms by which viruses evade host defenses. Here, we review studies identifying host genes regulated by viruses and discuss how these genes integrate in host regulatory and interaction networks, with a particular focus on the physical properties of these networks. A systems biology approach to the evolution of plant-virus interactions The systems biology approachGenomic tools have allowed assessment of gene expression at a genome-wide scale, providing unprecedented views of the host-virus interaction. To make use of all of the information contained in these large data sets, however, it is necessary to use computational and mathematical tools to disentangle the interactions between the molecular components of both biological entities and to identify how these interactions determine the outcome of the infection [1,2••], which is known as the field of genomic systems biology (GSB). GSB is a top-down approach that takes advantage of the recent development of high-throughput experimental techniques for obtaining omic data, and constitutes the antithesis of the reductionist paradigm (with a bottom-up perspective) that has been dominating molecular biology. The GSB approach consists of cycling between the generation of experimental data and modeling by means of reverse-engineering techniques to propose testable hypotheses about biological systems, experimental validation of these hypotheses and quantification of the relevant model parameters, and then using the newly acquired quantitative description to refine the computational model and finally make predictions of the system behavior [3,4•].To complete its infectious cycle, a few components of a virus, including its nucleic acids and encoded proteins, must establish multiple and complex interactions not only among themselves [5,6•,7,8] but also with a myriad of components of the host cell [9,10•,11]. The outcome of all these interactions is that the plant controls the spread of viral infection or, alternatively, the virus overcomes the host defenses and establishes a productive infection that may or may not be associated with the development of symptoms. While the GSB approach is being extensively used in the analysis of animal virus interactions (e.g. hepatitis C, human immunodeficiency, yellow fever, influenza A and herpesviruses), plant virologyhas not yet benefitted to the same extent, and the most relevant studies in the field generally apply some tr...

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ExprEssence - Revealing the essence of differential experimental data in the context of an interaction/regulation net-work

Cited by 72 publications

References 61 publications

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera

Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions

Detection of food-borne pathogens with DNA arrays on disk

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