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 implementation of our algorithm is available on our Web server.
Schwikowski, B (Reprint Author), Inst Syst Biol, Seattle, WA 98103 USA. Inst Syst Biol, Seattle, WA 98103 USA. Univ Washington, Dept Comp Sci, Seattle, WA 98195 USA. Fred Hutchinson Canc Res Ctr, Seattle, WA 98109 USA. ETH, Inst Mol Syst Biol, Zurich, Switzerland. Univ Zurich, Fac Nat Sci, Zurich, Switzerland. Wesleyan Univ, Dept Mol Biol & Biochem, Middletown, CT 06459 USA. Inst Pasteur, Syst Biol Grp, F-75015 Paris, France. Cedars Sinai Med Ctr, Los Angeles, CA 90048 USA.
%0 Journal Article
%1 Prakash2006
%A Prakash, A
%A Mallick, P
%A Whiteaker, J
%A Zhang, HD
%A Paulovich, A
%A Flory, M
%A Lee, H
%A Aebersold, R
%A Schwikowski, B
%C 9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USA
%D 2006
%I AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
%J Molecular & Cellular Proteomics
%K
%N 3
%P 423-432
%R 10.1074/mcp.M500133-MCP200
%T Signal maps for mass spectrometry-based comparative proteomics
%V 5
%X 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 implementation of our algorithm is available on our Web server.
@article{Prakash2006,
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 implementation of our algorithm is available on our Web server.}},
added-at = {2011-01-17T12:52:53.000+0100},
address = {{9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USA}},
affiliation = {{Schwikowski, B (Reprint Author), Inst Syst Biol, Seattle, WA 98103 USA. Inst Syst Biol, Seattle, WA 98103 USA. Univ Washington, Dept Comp Sci, Seattle, WA 98195 USA. Fred Hutchinson Canc Res Ctr, Seattle, WA 98109 USA. ETH, Inst Mol Syst Biol, Zurich, Switzerland. Univ Zurich, Fac Nat Sci, Zurich, Switzerland. Wesleyan Univ, Dept Mol Biol \& Biochem, Middletown, CT 06459 USA. Inst Pasteur, Syst Biol Grp, F-75015 Paris, France. Cedars Sinai Med Ctr, Los Angeles, CA 90048 USA.}},
author = {Prakash, A and Mallick, P and Whiteaker, J and Zhang, HD and Paulovich, A and Flory, M and Lee, H and Aebersold, R and Schwikowski, B},
author-email = {{benno@pasteur.fr}},
biburl = {https://www.bibsonomy.org/bibtex/2ff87c00615fc61ba77701ef380d0c3ca/hkayabilisim},
doc-delivery-number = {{023RB}},
doi = {{10.1074/mcp.M500133-MCP200}},
file = {:/home/hkaya/Projeler/diagnus/Screener/doc/literature/Prakash2006.pdf:PDF},
interhash = {3c0e256366e29ce3340695b135f94b9e},
intrahash = {ff87c00615fc61ba77701ef380d0c3ca},
issn = {{1535-9476}},
journal = {{Molecular \& Cellular Proteomics}},
journal-iso = {{Mol. Cell. Proteomics}},
keywords = {},
keywords-plus = {{SEARCH; OPTIMIZATION; SEQUENCES; PROTEINS; GENOMICS; DATABASE; MS/MS}},
language = {{English}},
month = {{Mar}},
number = {{3}},
number-of-cited-references = {{23}},
pages = {{423-432}},
publisher = {{AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC}},
review = {* Yazılımların webden deniyorsun: http://www.pasteur.fr/recherche/unites/Biolsys/chams/index.htm},
subject-category = {{Biochemical Research Methods}},
times-cited = {{35}},
timestamp = {2011-01-17T12:52:53.000+0100},
title = {{Signal maps for mass spectrometry-based comparative proteomics}},
type = {{Article}},
unique-id = {{ISI:000236142800001}},
volume = {{5}},
year = {{2006}}
}