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Anal Chem. 2017 Sep 5;89(17):8696-8703. doi: 10.1021/acs.analchem.7b00947. Epub 2017 Aug 17.

One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks.

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University of North Carolina at Charlotte , Charlotte, North Carolina 28223, United States.
University of North Carolina at Chapel Hill , Chapel Hill, North Carolina 27514, United States.
Emory University , Atlanta, Georgia 30322, United States.
University of Alabama at Birmingham , Birmingham, Alabama 35294, United States.


False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography-mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in m/z should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks.

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