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<identifier>oai:HAL:hal-01300043v1</identifier>
<datestamp>2018-01-11</datestamp>
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<title lang=en>Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution</title>
<creator>Corel, Eduardo</creator>
<creator>Lopez, Philippe</creator>
<creator>Méheust, Raphaël</creator>
<creator>Bapteste, Eric</creator>
<contributor>Evolution Paris Seine ; Université des Antilles et de la Guyane (UAG) - Université Pierre et Marie Curie - Paris 6 (UPMC) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>ISSN: 0966-842X</source>
<source>Trends in Microbiology</source>
<publisher>Elsevier</publisher>
<identifier>hal-01300043</identifier>
<identifier>http://hal.upmc.fr/hal-01300043</identifier>
<identifier>http://hal.upmc.fr/hal-01300043/document</identifier>
<identifier>http://hal.upmc.fr/hal-01300043/file/Network-Thinking.pdf</identifier>
<source>http://hal.upmc.fr/hal-01300043</source>
<source>Trends in Microbiology, Elsevier, 2016, 24 (3), pp.224-237. 〈10.1016/j.tim.2015.12.003〉</source>
<identifier>DOI : 10.1016/j.tim.2015.12.003</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.tim.2015.12.003</relation>
<language>en</language>
<subject lang=en>introgression</subject>
<subject lang=en>gene transfer</subject>
<subject lang=en>graph theory</subject>
<subject lang=en>bipartite graph</subject>
<subject lang=en>symbiosis</subject>
<subject lang=en>evolution</subject>
<subject>[SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>The tree model and tree-based methods have played a major, fruitful role in evolutionary studies. However, with the increasing realization of the quantitative and qualitative importance of reticulate evolutionary processes, affecting all levels of biological organization, complementary network-based models and methods are now flourishing, inviting evolutionary biology to experience a network-thinking era. We show how relatively recent comers in this field of study, that is, sequence-similarity networks, genome networks, and gene families–genomes bipartite graphs, already allow for a significantly enhanced usage of molecular datasets in comparative studies. Analyses of these networks provide tools for tackling a multitude of complex phenomena, including the evolution of gene transfer, composite genes and genomes, evolutionary transitions, and holobionts.</description>
<rights>http://creativecommons.org/licenses/by-nc-nd/</rights>
<date>2016-03</date>
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