MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Fraisier-Vannier, O.; Chervin, J.; Cabanac, G.; Puech-Pages, V.; Fournier, S.; Durand, V.; Amiel, A.; Andre, O.; Benamar, O. A.; Dumas, B.; Tsugawa, H.; Marti, G.

doi:10.1101/2020.04.09.033308

bioRxiv · 10.1101/2020.04.09.033308

MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics

Fraisier-Vannier, O.·Chervin, J.·Cabanac, G.·Puech-Pages, V.·Fournier, S.·Durand, V.·Amiel, A.·Andre, O.·Benamar, O. A.·Dumas, B.·Tsugawa, H.·Marti, G.

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Abstract

Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) is currently the gold-standard technique to determine the full chemical diversity in biological samples. This approach still has many limitations, however; notably, the difficulty of estimating accurately the number of unique metabolites being profiled among the thousands of MS ion signals arising from chromatograms. Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER suite, which tackles feature degeneracy and improves annotation rates. We show that implementation of MS-CleanR reduces the number of signals by nearly 80% while retaining 95% of unique metabolite features. Moreover, the annotation results from MS-FINDER can be ranked with respect to database chosen by the user, which improves identification accuracy. Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions improved class separation resulting from multivariate data analysis and lead to annotation of 75% of the final features. The full workflow was applied to metabolomic profiles from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches; a group of glycosylated triterpenoids overrepresented in resistant lines were identified as candidate compounds conferring pathogen resistance. MS-CleanR is implemented through a Shiny interface for intuitive use by end-users (available at: https://github.com/eMetaboHUB/MS-CleanR).

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Fraisier-Vannier, O., Chervin, J., Cabanac, G., Puech-Pages, V., Fournier, S., Durand, V., Amiel, A., Andre, O., Benamar, O. A., Dumas, B., Tsugawa, H., Marti, G.. 2020-04-09. MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics. https://doi.org/10.1101/2020.04.09.033308

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