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Molecular & Cellular Proteomics 5:423-432, 2006.
© 2006 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Signal Maps for Mass Spectrometry-based Comparative Proteomics^*

Amol Prakash^,,¶, Parag Mallick^||,**, Jeffrey Whiteaker^¶, Heidi Zhang^¶, Amanda Paulovich^¶, Mark Flory^**,, Hookeun Lee, Ruedi Aebersold^**, and Benno Schwikowski^,,**,¶¶

From the Department of Computer Science, University of Washington, Seattle, Washington 98195 ^** Institute for Systems Biology, Seattle, Washington 98103 ^¶ Fred Hutchinson Cancer Research Center, Seattle, Washington 98109 Institute for Molecular Systems Biology, Eidgenössische Technische Hochschule and Faculty of Natural Sciences, University of Zurich, Zurich, Switzerland Department of Molecular Biology and Biochemistry, Wesleyan University, Middletown, Connecticut 06459 Systems Biology Group, Institut Pasteur, 25-28 Rue du Dr. Roux, 75015 Paris, France ^|| Cedars-Sinai Medical Center, Los Angeles, California 90048

Mass spectrometry-based proteomic experiments, in combination with liquid chromatography-based separation, can be used to compare complex biological samples across multiple conditions. These comparisons are usually performed on the level of protein lists generated from individual experiments. Unfortunately given the current technologies, these lists typically cover only a small fraction of the total protein content, making global comparisons extremely limited. Recently approaches have been suggested that are built on the comparison of computationally built feature lists instead of protein identifications. Although these approaches promise to capture a bigger spectrum of the proteins present in a complex mixture, their success is strongly dependent on the correctness of the identified features and the aligned retention times of these features across multiple experiments. In this experimental-computational study, we went one step further and performed the comparisons directly on the signal level. First signal maps were constructed that associate the experimental signals across multiple experiments. Then a feature detection algorithm used this integrated information to identify those features that are discriminating or common across multiple experiments. At the core of our approach is a score function that faithfully recognizes mass spectra from similar peptide mixtures and an algorithm that produces an optimal alignment (time warping) of the liquid chromatography experiments on the basis of raw MS signal, making minimal assumptions on the underlying data. We provide experimental evidence that suggests uniqueness and correctness of the resulting signal maps even on low accuracy mass spectrometers. These maps can be used for a variety of proteomic analyses. Here we illustrate the use of signal maps for the discovery of diagnostic biomarkers. An imple-mentation of our algorithm is available on our Web server.

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