The Everything Bagel Feature Finder: Ultra-fast automated feature finding for untargeted metabolomics
Year: 2026
Type: preprint
Abstract: Metabolomics studies are increasingly being applied with hundreds to thousands, even tens of thousands of samples that demand rapid, automated data processing while maintaining analytical sensitivity or quantitative accuracy. A major computational bottleneck is feature finding, which is the transformation of LC-MS and LC-MS/MS data into a set of analyte signals aligned and quantified across samples. Feature finding can be computationally intensive and often requires manual iterative parameter optimization. To accelerate this process, we present the Everything Bagel (EB) feature finder, an ultra-fast automated feature finding tool that integrates feature detection, retention-time alignment, and gap filling designed for run-time and memory efficiency. We benchmarked EB against two automated feature finding methods on eight benchmarking datasets. Specifically, we evaluated these three feature finding methods by measuring spike-in standard detection coverage, dilution series quantification accuracy, and yeast 12 C/ 13 C credentialed features. In this evaluation, the EB feature finder achieved performance comparable to, and often exceeding, existing methods while requiring up to 150-fold lower CPU hours and up to 113-fold lower wall time. We further demonstrated the bioanalytical validity of EB by reanalyzing published datasets used for biomarker discovery and reproduced biologically significant features that matched the published findings using manually tuned feature finding settings. Taken along with the speed improvements, we anticipate EB will enhance the ability to automatically analyze datasets with thousands to tens of thousands of samples for the community.
Authors: Yourae Shin, Yasin El Abiead, Alan K. Jarmusch, Michael Strobel, Paul E. Abraham, Sarah Thurmon, Deepa D. Acharya, Allegra Aron, Aivett Bilbao, Benjamin P. Bowen, Corey D. Broeckling, Chris J. Brown, Vincent Charron-Lamoureux, Xingyuan Chen, Tito Damiani, Alex Doty, Xiuxia Du, Neha Garg, Stilianos Papadopoulos Lambidis, Laura-Isobel McCall, Kaylie I. Kirkwood-Donelson, Trent Northen, Jessica Prenni, Emma E. Rennie, Oliver B. Vining, Crystal X. Wang, Quanbo Xiong, Haoqi Nina Zhao, Pieter C Dorrestein, Daniel Petras, Vanessa V Phelan, Mingxun Wang
Institutions: University of California, Riverside, BOKU University, National Institutes of Health, National Institute of Environmental Health Sciences, Oak Ridge National Laboratory, University of Tennessee at Knoxville, Corteva (United States), University of Denver, Pacific Northwest National Laboratory, Lawrence Berkeley National Laboratory, Joint Genome Institute, Colorado State University, University of California San Diego, Stanford University, Czech Academy of Sciences, Czech Academy of Sciences, Institute of Organic Chemistry and Biochemistry, Georgia Institute of Technology, University of North Carolina at Charlotte, Max Planck Institute for Biology, San Diego State University, Agilent Technologies (United States), California NanoSystems Institute, University of California, Santa Barbara, University of Colorado Anschutz
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: U.S. Department of Energy, U.S. Department of Health and Human Services, Österreichischen Akademie der Wissenschaften, National Institutes of Health, Office of Science, National Institute of Allergy and Infectious Diseases, Biological and Environmental Research, Center for Bioenergy Innovation, Lawrence Berkeley National Laboratory
Awards: G1079818582, G1143651532, G1286236842, G1337544421, Microbiota Metallophores: Probing Interactions and Nutrient Dynamics of the Human Gut, G223002938, G239262653, G3083819904, G3547926608, G382641700, G419881185, G42916606, G4501827968, G498139845, G5706655981, G6652958823, G6919849803, HIV Characterization, G7045985559, G7354628648, G7787287669, G8483344784, G9000342534, Characterizing Natural Product Mediated Microbial Interactions, G969889393
OpenAlex ID: W7203887395