The Isotope Wavelet: A Signal Theoretic Framework for Analyzing Mass Spectrometry Data

Computational proteomics is one of today’s foremost challenges in bioinformatics with applications in fields as diverse as toxicology, diagnostics, or target identification and validation. The current de-facto standard for high-throughput proteomics studies, HPLC-MS, is capable of generating huge amounts of data for a single analysis: the LC-MS maps, which can be thought of as a collection of one-dimensional mass spectrometric scans for a possibly large number of retention times. Here, the signal of interest – the amount of certain proteins or peptides contained in the sample – is hidden in a complex mixture of baseline terms and chemical as well as instrument noise effects. Thus, most biologically or medically relevant applications require a certain, often manual, preprocessing of the raw instrument data, during which the mass spectrometric peaks in the sample are detected and those of potential interest are retained. We present a novel wavelet-based approach to detect regions of interest in mass spectrometric scans. To this end, we designed a tailored isotope wavelet that has been successfully tested in a myoglobin quantification study [3]. Here, we give more detailed information about the design and characteristics of the isotope wavelet.