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Signal Processing in Proteomics

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Book cover Proteome Bioinformatics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 604))

Abstract

Computational proteomics applications are often imagined as a pipeline, where information is processed in each stage before it flows to the next one. Independent of the type of application, the first stage invariably consists of obtaining the raw mass spectrometric data from the spectrometer and preparing it for use in the later stages by enhancing the signal of interest while suppressing spurious components. Numerous approaches for preprocessing MS data have been described in the literature. In this chapter, we will describe both, standard techniques originating from classical signal and image processing, and novel computational approaches specifically tailored to the analysis of MS data sets. We will focus on low level signal processing tasks such as baseline reduction, denoising, and feature detection.

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Notes

  1. 1.

     Of course, this picture is changing as soon as we take posttranslational modifications or labelling techniques into account.

  2. 2.

     Please note that peptides will usually elute over several subsequent time points and will therefore appear in several neighbouring scans.

  3. 3.

     This whole “de-isotoping” step is often seen as part of a later stage of the proteomics pipeline - the identification stage - since it usually operates not on the raw data, but on the list of sticks. However, as we will show in a later section, integrating de-isotoping (feature detection) into the signal processing can improve prediction performance by extracting further valuable information from the data that would otherwise be neglected.

  4. 4.

    In reality, the peak intensities rather follow a binomial distribution, but can be approximated by a Poisson distribution.

  5. 5.

     The adaptive Wavelet transform is a slight generalization of the classical Wavelet transform in that the Wavelet kernel can vary with position; hence, the transform does not correspond to a simple convolution, but rather to a more complicated integral transform.

  6. 6.

     The sinc-function is defined by sinc(x):  =  sin(x)/x.

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Acknowledgments

The authors would like to express their gratitude to Mrs. Anna Katharina Dehof, Mrs. Sophie Weggler, and Mrs. Linda Wolters for critical reading of the manuscript.

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Authors and Affiliations

  1. Center for Bioinformatics, Saarland University, Saarbrücken, Germany

    Rene Hussong & Andreas Hildebrandt

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  1. Rene Hussong
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  2. Andreas Hildebrandt
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Correspondence to Andreas Hildebrandt .

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Editors and Affiliations

  1. Fac. Life Sciences, University of Manchester, Oxford Rd., Manchester, M13 9PT, United Kingdom

    Simon J. Hubbard

  2. Fac. Veterinary Science, University of Liverpool, Liverpool, L69 7ZJ, United Kingdom

    Andrew R. Jones

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Hussong, R., Hildebrandt, A. (2010). Signal Processing in Proteomics. In: Hubbard, S., Jones, A. (eds) Proteome Bioinformatics. Methods in Molecular Biology™, vol 604. Humana Press. https://doi.org/10.1007/978-1-60761-444-9_11

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  • DOI: https://doi.org/10.1007/978-1-60761-444-9_11

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