title: Signal maps for mass spectrometry-based comparative proteomics.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Washington, Seattle, Washington 98195, USA.
authors.forename: Amol
authors.initials: A
authors.lastname: Prakash
authors.forename: Parag
authors.initials: P
authors.lastname: Mallick
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Whiteaker
authors.forename: Heidi
authors.initials: H
authors.lastname: Zhang
authors.forename: Amanda
authors.initials: A
authors.lastname: Paulovich
authors.forename: Mark
authors.initials: M
authors.lastname: Flory
authors.forename: Hookeun
authors.initials: H
authors.lastname: Lee
authors.forename: Ruedi
authors.initials: R
authors.lastname: Aebersold
authors.forename: Benno
authors.initials: B
authors.lastname: Schwikowski
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D013058:Mass Spectrometry / Q000379:methods*; D010455:Peptides / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D029701:Saccharomyces cerevisiae Proteins / Q000032:analysis / Q000737:chemistry
chemical_list: D015415:Biomarkers; D010455:Peptides; D029701:Saccharomyces cerevisiae Proteins