Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12-06

AUTHORS

David P. A. Kilgour, Sam Hughes, Samantha L. Kilgour, C. Logan Mackay, Magnus Palmblad, Bao Quoc Tran, Young Ah Goo, Robert K. Ernst, David J. Clarke, David R. Goodlett

ABSTRACT

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 Abstractᅟ More... »

PAGES

253-262

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13361-016-1549-z

DOI

http://dx.doi.org/10.1007/s13361-016-1549-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011547319

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27924495


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