Homologue series detection and management in LC-MS data with homologueDiscoverer | Zendy

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

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Homologue series detection and management in LC-MS data with homologueDiscoverer

Author(s) -

Kevin Mildau,

Justin J. J. van der Hooft,

Mira Flasch,

Benedikt Warth,

Yasin El Abiead,

Gunda Koellensperger,

Jürgen Zanghellini,

Christoph Büschl

Publication year - 2022

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btac647

Subject(s) - computer science , redundancy (engineering) , data mining , noise (video) , r package , series (stratigraphy) , data management , artificial intelligence , programming language , biology , operating system , paleontology , image (mathematics)

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.

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