Computational workflow to study the seasonal variation of secondary metabolites in nine different bryophytes

Scientific Data
Kristian PetersSteffen Neumann

Abstract

In Eco-Metabolomics interactions are studied of non-model organisms in their natural environment and relations are made between biochemistry and ecological function. Current challenges when processing such metabolomics data involve complex experiment designs which are often carried out in large field campaigns involving multiple study factors, peak detection parameter settings, the high variation of metabolite profiles and the analysis of non-model species with scarcely characterised metabolomes. Here, we present a dataset generated from 108 samples of nine bryophyte species obtained in four seasons using an untargeted liquid chromatography coupled with mass spectrometry acquisition method (LC/MS). Using this dataset we address the current challenges when processing Eco-Metabolomics data. Here, we also present a reproducible and reusable computational workflow implemented in Galaxy focusing on standard formats, data import, technical validation, feature detection, diversity analysis and multivariate statistics. We expect that the representative dataset and the reusable processing pipeline will facilitate future studies in the research field of Eco-Metabolomics.

References

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Oct 3, 2018·Ecology and Evolution·Kristian PetersSteffen Neumann

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Citations

Dec 12, 2018·GigaScience·Kristian PetersChristoph Steinbeck
Apr 21, 2020·Drug Testing and Analysis·Liliana MuschiettiJerónimo Ulloa
May 8, 2018·International Journal of Molecular Sciences·Kristian PetersNicole M van Dam
Aug 18, 2020·Daru : Journal of Faculty of Pharmacy, Tehran University of Medical Sciences·Samapika Nandy, Abhijit Dey
Oct 3, 2018·Ecology and Evolution·Kristian PetersSteffen Neumann
Apr 4, 2021·International Journal of Molecular Sciences·Kristian PetersSteffen Neumann

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Software Mentioned

CompassXPort
Metabolights Eco - Metabolomics
APACHE
dendextend
phangorn
Bruker Compass IsotopePattern
Hmisc
MetaboLights
PhenoMeNal
gplots

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