Author

Robert A. Quinn

Michigan State UniversityORCID 0000-0002-9829-3256Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:database / resource· 4 Assay:metabolomics· 4 Approach:statistical model· 3 Research Focus:clustering· 2 Approach:experimental assay· 2 Assay:metagenomics· 2 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Alexander A. AksenoviD· 4 shared Louis-Félix Nothias-ScagliaiD· 4 shared Mingxun WangiD· 4 shared Pieter C. DorresteiniD· 4 shared Daniel PetrasiD· 3 shared Justin J. J. van der HooftiD· 3 shared Madeleine ErnstiD· 3 shared Mélissa Nothias-EspositoiD· 3 shared

Activity by Year

2019-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersBioinformatics

Alexander A. AksenovORCID

Mutual co-authors in cluster

Author profile →

6 papers

Recent·Most cited

From SCUBA to spectra: Broadly applicable methods for coral metabolomics research

Sabrina L. Rosset, Thomas J.B. Cline, Kiran‐Kumar Shivaiah, Immy A. Ashley, Khalil Smith, Giada Tortorelli, Lauren Kezia Walling, John Everett Parkinson, Mark J. A. Vermeij, Mehdi Bouhaddou, Crawford Drury, Ty N. F. Roach, Robert A. Quinn

This study develops methods for coral metabolomics, evaluating sample fixation and storage effects, and introduces tools for metabolite annotation and multi-omics analysis.

bioRxiv · Jan 19, 2026 · PreprintBiochemistry

ClusterApp to visualize, organize, and navigate metabolomics data

Vinicius Hansel, Pothuvilage Karunarathne, Tiago Cabral Borelli, Robert A. Quinn, Ricardo Silva

ClusterApp provides a web tool for metabolomics data analysis using PCoA, offering preprocessing, visualization, and clustering features to enhance data interpretation.

bioRxiv · Feb 12, 2025 · PreprintBioinformatics

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Alexander A. Aksenov, Ivan Laponogov, Zheng Zhang, Sophie L. F. Doran, Ilaria Belluomo, Dennis A. Veselkov, Wout Bittremieux, Louis-Félix Nothias-Scaglia, Mélissa Nothias-Esposito, Katherine N. Maloney, Biswapriya B. Misra, Alexey V. Melnik, Aleksandr Alekseevich Smirnov, Xiuxia Du, Kenneth Lyons Jones, , Kirill Veselkov

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

Nature Biotechnology · Nov 9, 2020Peer-reviewedBioinformatics

Feature-based Molecular Networking in the GNPS Analysis Environment

Louis-Félix Nothias-Scaglia, Daniel Petras, Robin Schmid, Kai Dührkop, Johannes Rainer, Abinesh Sarvepalli, Ivan Protsyuk, Madeleine Ernst, Hiroshi Tsugawa, Markus Fleischauer, Fabian Aicheler, Alexander A. Aksenov, Oliver Alka, Pierre‐Marie Allard, Aiko Barsch, , Pieter C. Dorrestein

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.

Nature Methods · Aug 24, 2020Peer-reviewedBioinformatics

Repository-scale Co- and Re-analysis of Tandem Mass Spectrometry Data

Alan K. Jarmusch, Mingxun Wang, Christine M. Aceves, Rohit S. Advani, Shaden Aguirre, Alexander A. Aksenov, Gajender Aleti, Allegra T. Aron, Anelize Bauermeister, Sanjana Bolleddu, Amina Bouslimani, Andrés Mauricio Caraballo‐Rodríguez, Rama Chaar, Roxana Coras, Emmanuel O. Elijah, , Pieter C. Dorrestein

The paper develops ReDU, a tool for discovering and re-analyzing metabolomics data in GNPS, following FAIR principles.

Nature Methods · Aug 17, 2020Peer-reviewedBioinformatics

Revisiting microbe-metabolite interactions: doing better than random

James T. Morton, Daniel T. McDonald, Alexander A. Aksenov, Louis-Félix Nothias-Scaglia, James R. Foulds, Robert A. Quinn, Michelle H. Badri, Tami L. Swenson, Marc W. Van Goethem, Trent R. Northen, Yoshiki Vázquez‐Baeza, Mingxun Wang, Nicholas A. Bokulich, Aaron Watters, Se Jin Song, , Rob D. Knight

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

bioRxiv · Dec 10, 2019 · PreprintBioinformatics