Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching
Author(s) -
Pan Du,
Warren A. Kibbe,
Simon Lin
Publication year - 2006
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btl355
Subject(s) - smoothing , wavelet , pattern recognition (psychology) , noise (video) , false positive paradox , preprocessor , algorithm , computer science , wavelet transform , artificial intelligence , amplitude , mathematics , robustness (evolution) , physics , computer vision , optics , biochemistry , chemistry , image (mathematics) , gene
A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom