@article{KrausMathewStephenSchapranow2021,
  author    = {Kraus, Sara Milena and Mathew-Stephen, Mariet and Schapranow, Matthieu-Patrick},
  title     = {Eatomics},
  series = {Journal of proteome research},
  volume    = {20},
  journal   = {Journal of proteome research},
  number    = {1},
  publisher = {American Chemical Society},
  address   = {Washington},
  issn      = {1535-3893},
  doi       = {10.1021/acs.jproteome.0c00398},
  pages     = {1070 -- 1078},
  year      = {2021},
  abstract  = {Quantitative proteomics data are becoming increasingly more available, and as a consequence are being analyzed and interpreted by a larger group of users. However, many of these users have less programming experience. Furthermore, experimental designs and setups are getting more complicated, especially when tissue biopsies are analyzed. Luckily, the proteomics community has already established some best practices on how to conduct quality control, differential abundance analysis and enrichment analysis. However, an easy-to-use application that wraps together all steps for the exploration and flexible analysis of quantitative proteomics data is not yet available. For Eatomics, we utilize the R Shiny framework to implement carefully chosen parts of established analysis workflows to (i) make them accessible in a user-friendly way, (ii) add a multitude of interactive exploration possibilities, and (iii) develop a unique experimental design setup module, which interactively translates a given research hypothesis into a differential abundance and enrichment analysis formula. In this, we aim to fulfill the needs of a growing group of inexperienced quantitative proteomics data analysts. Eatomics may be tested with demo data directly online via https://we.analyzegenomes.com/now/eatomics/or with the user's own data by installation from the Github repository at https://github.com/Millchmaedchen/Eatomics.},
  language  = {en}
}
@misc{PerkinsPernaAdrianetal.2019,
  author    = {Perkins, Daniel M. and Perna, Andrea and Adrian, Rita and Cerme{\~n}o, Pedro and Gaedke, Ursula and Huete-Ortega, Maria and White, Ethan P. and Yvon-Durocher, Gabriel},
  title     = {Energetic equivalence underpins the size structure of tree and phytoplankton communities},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {684},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42569},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-425695},
  pages     = {8},
  year      = {2019},
  abstract  = {The size structure of autotroph communities - the relative abundance of small vs. large individuals - shapes the functioning of ecosystems. Whether common mechanisms underpin the size structure of unicellular and multicellular autotrophs is, however, unknown. Using a global data compilation, we show that individual body masses in tree and phytoplankton communities follow power-law distributions and that the average exponents of these individual size distributions (ISD) differ. Phytoplankton communities are characterized by an average ISD exponent consistent with three-quarter-power scaling of metabolism with body mass and equivalence in energy use among mass classes. Tree communities deviate from this pattern in a manner consistent with equivalence in energy use among diameter size classes. Our findings suggest that whilst universal metabolic constraints ultimately underlie the emergent size structure of autotroph communities, divergent aspects of body size (volumetric vs. linear dimensions) shape the ecological outcome of metabolic scaling in forest vs. pelagic ecosystems.},
  language  = {en}
}