title: MetCirc: navigating mass spectral similarity in high-resolution MS/MS metabolomics data.
abstract: CONCLUSIONS
  Among the main challenges in metabolomics are the rapid dereplication of previously characterized metabolites across a range of biological samples and the structural prediction of unknowns from MS/MS data. Here, we developed MetCirc to comprehensively align and calculate pairwise similarity scores among MS/MS spectral data and visualize these across a range of biological samples. MetCirc comprises functionalities to interactively organize these data according to compound familial groupings and to accelerate the discovery of shared metabolites and hypothesis formulation for unknowns. As such, MetCirc provides a significant advance to address biological questions in areas where chemodiversity plays a role.
  
  
  METHODS
  MetCirc , implemented in the open-source R language, together with its vignette are available in the Bioconductor project and at https://github.com/PlantDefenseMetabolism/MetCirc .
  
  
  BACKGROUND
  thomasnaake@googlemail.com or emmanuel.gaquerel@cos.uni-heidelberg.de.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Centre for Organismal Studies, University of Heidelberg, Heidelberg 69120, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Naake
authors.affiliation: Centre for Organismal Studies, University of Heidelberg, Heidelberg 69120, Germany.
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Gaquerel
mesh_terms: D035264:Flowers / Q000378:metabolism; D055432:Metabolomics / Q000379:methods*; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods*; D014026:Nicotiana / Q000378:metabolism