Nature Chemical BiologyBioinformaticsPeer-reviewed journal article
Chemically-informed Analyses of Metabolomics Mass Spectrometry Data with Qemistree
Anupriya TripathiiD, Yoshiki Vázquez‐BaezaiD, Julia M. GauglitziD, Mingxun WangiD, Kai DührkopiD, Mélissa Nothias-EspositoiD, Deepa D. AcharyaiD, Madeleine ErnstiD, Justin J. J. van der HooftiD, Qiyun ZhuiD, Daniel T. McDonaldiD, Asker BrejnrodiD, Antonio GonzáleziD, Jo HandelsmaniD, Markus FleischaueriD, Marcus LudwigiD, Sebastian BöckeriD, Louis-Félix Nothias-ScagliaiD, Rob D. KnightiD, Pieter C. DorresteiniD
Abstract
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 hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.
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