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Anal Chem. 2017 Mar 21;89(6):3250-3255. doi: 10.1021/acs.analchem.6b04372. Epub 2017 Mar 6.

Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.

Author information

1
University of California, Davis , West Coast Metabolomics Center, 451 E. Health Sciences Drive, Rm 1300, Davis, California 95616, United States.
2
Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center , 6767 Bertner Avenue, Houston, Texas 77030-2603, United States.
3
Department of Biochemistry, Faculty of Sciences, King Abdulaziz University , Abdullah Sulayman, Jeddah 21589, Saudi Arabia.

Abstract

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .

PMID:
28225594
DOI:
10.1021/acs.analchem.6b04372
[Indexed for MEDLINE]

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