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Homologue series detection and management in LC-MS data with <i>homologueDiscoverer</i>

Kevin Mildau, Justin J. J. van der Hooft, Mira Flasch, Benedikt Warth, Yasin El Abiead, Gunda Koellensperger, Jürgen Zanghellini, Christoph Büschl

2022Bioinformatics11 citationsDOIOpen Access PDF

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.

Topics & Concepts

Computer scienceRedundancy (engineering)Data miningNoise (video)R packageSeries (stratigraphy)Data managementArtificial intelligenceProgramming languageBiologyOperating systemImage (mathematics)PaleontologyMetabolomics and Mass Spectrometry StudiesAdvanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and Applications
Homologue series detection and management in LC-MS data with <i>homologueDiscoverer</i> | Litcius