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

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

doi:10.1093/bioinformatics/btac647

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

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	5139-5140
Journal	Bioinformatics
Volume	38
Issue number	22
DOIs	https://doi.org/10.1093/bioinformatics/btac647
Publication status	Published - 15 Nov 2022

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https://edepot.wur.nl/584324Licence: CC BY

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title = "Homologue series detection and management in LC-MS data with homologueDiscoverer",

abstract = "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.",

author = "Kevin Mildau and {van der Hooft}, {Justin J.J.} and Mira Flasch and Benedikt Warth and {El Abiead}, Yasin and Gunda Koellensperger and J{\"u}rgen Zanghellini and Christoph B{\"u}schl",

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AU - Mildau, Kevin

AU - van der Hooft, Justin J.J.

AU - Flasch, Mira

AU - Warth, Benedikt

AU - El Abiead, Yasin

AU - Koellensperger, Gunda

AU - Zanghellini, Jürgen

AU - Büschl, Christoph

PY - 2022/11/15

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N2 - 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.

AB - 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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DO - 10.1093/bioinformatics/btac647

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VL - 38

SP - 5139

EP - 5140

JO - Bioinformatics

JF - Bioinformatics

IS - 22

ER -

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

Abstract

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