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J Am Soc Mass Spectrom. 2017 Feb;28(2):253-262. doi: 10.1007/s13361-016-1549-z. Epub 2016 Dec 6.

Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry.

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Department of Chemistry and Forensics, Nottingham Trent University, Nottingham, NG11 8NS, UK.
EastCHEM School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, UK.
Department of Microbial Pathogenesis, School of Dentistry, University of Maryland, Baltimore, MD, 21201, USA.
Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands.
Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD, 21201, USA.


We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks. Graphical Abstract ᅟ.


Mass spectrometry; Peak detection; Threshold


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