Discrepancies in Biomarker Identification in Peak Picking Strategies in Untargeted Metabolomics Analyses of Cells, Tissues, and Biofluids

Different software and algorithms are available for peak picking in nontargeted metabolomics, and each may have its own strengths and limitations. The choice of the peak picking method can significantly influence the results obtained, including the number and identity of metabolites detected, their...

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Bibliographic Details
Main Author:	Iqbal Mahmud (1530976) (author)
Other Authors:	Taylor A. Harmon (8676855) (author), Laurel E. Meke (10834926) (author), Timothy J. Garrett (7418819) (author)
Published:	2025
Subjects:	Biochemistry Medicine Cell Biology Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified solvent gradient phases found significant discrepancy charge ratio (< careful noise filtering blank feature filtering >) tolerances used subsequent biomarker analysis source software tools based biomarker study peak picking strategies peak picking method peak picking tools untargeted metabolomics research untargeted metabolomics analyses peak picking algorithms biofluids different software peak picking untargeted metabolomics different tools biomarker identification nontargeted metabolomics significantly influence sample types retention time results obtained mzmine 2 metabolites detected largely understated investigation evaluates ensure reliable carefully evaluating accurate results
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Discrepancies in Biomarker Identification in Peak Picking Strategies in Untargeted Metabolomics Analyses of Cells, Tissues, and Biofluids

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