TY - JOUR
T1 - Chemically informed analyses of metabolomics mass spectrometry data with Qemistree
AU - Tripathi, Anupriya
AU - Vázquez-Baeza, Yoshiki
AU - Gauglitz, Julia M.
AU - Wang, Mingxun
AU - Dührkop, Kai
AU - Nothias-Esposito, Mélissa
AU - Acharya, Deepa D.
AU - Ernst, Madeleine
AU - van der Hooft, Justin J.J.
AU - Zhu, Qiyun
AU - McDonald, Daniel
AU - Brejnrod, Asker D.
AU - Gonzalez, Antonio
AU - Handelsman, Jo
AU - Fleischauer, Markus
AU - Ludwig, Marcus
AU - Böcker, Sebastian
AU - Nothias, Louis Félix
AU - Knight, Rob
AU - Dorrestein, Pieter C.
N1 - Funding Information:
P.C.D. was supported by the Gordon and Betty Moore Foundation (grant no. GBMF7622), CCF foundation no. 675191, the US National Institutes of Health (grant nos. U19 AG063744 01, P41 GM103484, R03 CA211211, R01 GM107550, 1 DP1 AT010885, P30 DK120515) and the University of Wisconsin-Madison OVCRGE; L.F.N. was supported by the US National Institutes of Health (grant no. R01 GM107550), and the European Union’s Horizon 2020 program (MSCA-GF, 704786). J.J.J.v.d.H. was supported by an ASDI eScience grant no. ASDI.2017.030, from the Netherlands eScience Center—NLeSC. K.D., M.F., M.L. and S.B. were supported by Deutsche Forschungsgemeinschaft (BO 1910/20). Y.V.B. was funded by the Janssen Human Microbiome Initiative through the Center for Microbiome Innovation at UC San Diego.
Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/2
Y1 - 2021/2
N2 - Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow. [Figure not available: see fulltext.].
AB - Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow. [Figure not available: see fulltext.].
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U2 - 10.1038/s41589-020-00677-3
DO - 10.1038/s41589-020-00677-3
M3 - Article
C2 - 33199911
AN - SCOPUS:85096069035
SN - 1552-4450
VL - 17
SP - 146
EP - 151
JO - Nature Chemical Biology
JF - Nature Chemical Biology
IS - 2
ER -