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Homologue series detection and management in LC-MS data with homologueDiscoverer.
Mildau, Kevin; van der Hooft, Justin J J; Flasch, Mira; Warth, Benedikt; El Abiead, Yasin; Koellensperger, Gunda; Zanghellini, Jürgen; Büschl, Christoph.
Affiliation
  • Mildau K; Department of Analytical Chemistry, University of Vienna, Vienna A-1090, Austria.
  • van der Hooft JJJ; Bioinformatics Group, Wageningen University, Wageningen 6708PB, the Netherlands.
  • Flasch M; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa.
  • Warth B; Department of Food Chemistry and Toxicology, University of Vienna, Vienna A-1090, Austria.
  • El Abiead Y; Department of Food Chemistry and Toxicology, University of Vienna, Vienna A-1090, Austria.
  • Koellensperger G; Department of Analytical Chemistry, University of Vienna, Vienna A-1090, Austria.
  • Zanghellini J; Department of Analytical Chemistry, University of Vienna, Vienna A-1090, Austria.
  • Büschl C; Department of Analytical Chemistry, University of Vienna, Vienna A-1090, Austria.
Bioinformatics ; 38(22): 5139-5140, 2022 11 15.
Article in En | MEDLINE | ID: mdl-36165687
ABSTRACT

SUMMARY:

Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. AVAILABILITY AND IMPLEMENTATION homologueDiscoverer is freely available at GitHub https//github.com/kevinmildau/homologueDiscoverer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Tandem Mass Spectrometry Type of study: Diagnostic_studies Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2022 Type: Article Affiliation country: Austria
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Tandem Mass Spectrometry Type of study: Diagnostic_studies Language: En Journal: Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2022 Type: Article Affiliation country: Austria