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<titleInfo><title>Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose</title></titleInfo>





<name type="personal">
  <namePart type="given">Isabella</namePart>
  <namePart type="family">Tomanek</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">3981F020-F248-11E8-B48F-1D18A9856A87</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0001-6197-363X</description></name>
<name type="personal">
  <namePart type="given">Calin C</namePart>
  <namePart type="family">Guet</namePart>
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<abstract lang="eng">Copy-number and point mutations form the basis for most evolutionary novelty through the process of gene duplication and divergence. While a plethora of genomic sequence data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic system that allows us to distinguish copy-number and point mutations, we study their early and transient adaptive dynamics in real-time in Escherichia coli. We find two qualitatively different routes of adaptation depending on the level of functional improvement selected for: In conditions of high gene expression demand, the two types of mutations occur as a combination. Under low gene expression demand, negative epistasis between the two types of mutations renders them mutually exclusive. Thus, owing to their higher frequency, adaptation is dominated by copy-number mutations. Ultimately, due to high rates of reversal and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation but also constrain sequence divergence over evolutionary time scales.</abstract>

<originInfo><publisher>Dryad</publisher><dateIssued encoding="w3cdtf">2022</dateIssued>
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<relatedItem type="host"><identifier type="doi">10.5061/dryad.rfj6q57ds</identifier>
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<mla>Tomanek, Isabella, and Calin C. Guet. &lt;i&gt;Flow Cytometry YFP and CFP Data and Deep Sequencing Data of Populations Evolving in Galactose&lt;/i&gt;. Dryad, 2022, doi:&lt;a href=&quot;https://doi.org/10.5061/dryad.rfj6q57ds&quot;&gt;10.5061/dryad.rfj6q57ds&lt;/a&gt;.</mla>
<ieee>I. Tomanek and C. C. Guet, “Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose.” Dryad, 2022.</ieee>
<ama>Tomanek I, Guet CC. Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose. 2022. doi:&lt;a href=&quot;https://doi.org/10.5061/dryad.rfj6q57ds&quot;&gt;10.5061/dryad.rfj6q57ds&lt;/a&gt;</ama>
<short>I. Tomanek, C.C. Guet, (2022).</short>
<apa>Tomanek, I., &amp;#38; Guet, C. C. (2022). Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose. Dryad. &lt;a href=&quot;https://doi.org/10.5061/dryad.rfj6q57ds&quot;&gt;https://doi.org/10.5061/dryad.rfj6q57ds&lt;/a&gt;</apa>
<chicago>Tomanek, Isabella, and Calin C Guet. “Flow Cytometry YFP and CFP Data and Deep Sequencing Data of Populations Evolving in Galactose.” Dryad, 2022. &lt;a href=&quot;https://doi.org/10.5061/dryad.rfj6q57ds&quot;&gt;https://doi.org/10.5061/dryad.rfj6q57ds&lt;/a&gt;.</chicago>
<ista>Tomanek I, Guet CC. 2022. Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose, Dryad, &lt;a href=&quot;https://doi.org/10.5061/dryad.rfj6q57ds&quot;&gt;10.5061/dryad.rfj6q57ds&lt;/a&gt;.</ista>
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