TWiGS (two-dimensional wavelet transform with generalized cross validation
and soft thresholding) is a novel algorithm for denoising liquid
chromatography-mass spectrometry (LC-MS) data for use in "shot-gun"
proteomics. Proteomics, the study of all proteins in an organism, is an
emerging field that has already proven successful for drug and disease
discovery in humans. There are a number of constraints that limit the
effectiveness of liquid chromatography-mass spectrometry (LC-MS) for
shot-gun proteomics, where the chemical signals are typically weak, and data
sets are computationally large. Most algorithms suffer greatly from a
researcher driven bias, making the results irreproducible and unusable by
other laboratories. We thus introduce a new algorithm, TWiGS, that removes
electrical (additive white) and chemical noise from LC-MS data sets.
TWiGS is developed to be a true two-dimensional algorithm, which operates in
the time-frequency domain, and minimizes the amount of researcher bias. It
is based on the traditional discrete wavelet transform (DWT), which allows
for fast and reproducible analysis. The separable two-dimensional DWT
decomposition is paired with generalized cross validation and soft
thresholding. The Haar, Coiflet-6, Daubechie-4 and the number of
decomposition levels are determined based on observed experimental
results. Using a synthetic LC-MS data model, TWiGS accurately retains key
characteristics of the peaks in both the time and m/z domain, and can
detect peaks from noise of the same intensity. TWiGS is applied to
angiotensin I and II samples run on a LC-ESI-TOF-MS
(liquid-chromatography-electrospray-ionization) to demonstrate its
utility for the detection of low-lying peaks obscured by noise.
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