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Design and analysis of quantitative differential proteomics investigations using LC-MS technology

Design and analysis of quantitative differential proteomics investigations using LC-MS technology
Design and analysis of quantitative differential proteomics investigations using LC-MS technology
Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.
0219-7200
107-123
Bukhman, Yury V.
dbad7bc0-aba4-4159-b818-538058dd1b31
Dharsee, Moyez
f06c7ce2-562c-4570-8dd7-2b57796a0999
Ewing, Rob
022c5b04-da20-4e55-8088-44d0dc9935ae
Chu, Peter
773e4c67-2b4d-4b17-9743-e522f65867c3
Topaloglou, Thodoros
2129aae5-4653-4c40-8c7a-cb25f406718a
Le Bihan, Thierry
0d59eb91-343b-44de-9f7a-a36dcf7ed564
Goh, Theo
87937e89-0b83-4e82-837e-02ca241237b9
Duewel, Henry
4b4fd2e2-5714-4cda-8375-7901cb0a5ba6
Stewart, Ian I.
d7bc1521-1e4f-4c63-89c6-7d1dafe9d068
Wisniewski, Jacek R.
f5258709-8329-496b-ad19-b14590a535cb
Ng, Nancy F.
16b6fa9e-31c0-4ea6-9ccd-231f07315e1d
Bukhman, Yury V.
dbad7bc0-aba4-4159-b818-538058dd1b31
Dharsee, Moyez
f06c7ce2-562c-4570-8dd7-2b57796a0999
Ewing, Rob
022c5b04-da20-4e55-8088-44d0dc9935ae
Chu, Peter
773e4c67-2b4d-4b17-9743-e522f65867c3
Topaloglou, Thodoros
2129aae5-4653-4c40-8c7a-cb25f406718a
Le Bihan, Thierry
0d59eb91-343b-44de-9f7a-a36dcf7ed564
Goh, Theo
87937e89-0b83-4e82-837e-02ca241237b9
Duewel, Henry
4b4fd2e2-5714-4cda-8375-7901cb0a5ba6
Stewart, Ian I.
d7bc1521-1e4f-4c63-89c6-7d1dafe9d068
Wisniewski, Jacek R.
f5258709-8329-496b-ad19-b14590a535cb
Ng, Nancy F.
16b6fa9e-31c0-4ea6-9ccd-231f07315e1d

Bukhman, Yury V., Dharsee, Moyez, Ewing, Rob, Chu, Peter, Topaloglou, Thodoros, Le Bihan, Thierry, Goh, Theo, Duewel, Henry, Stewart, Ian I., Wisniewski, Jacek R. and Ng, Nancy F. (2008) Design and analysis of quantitative differential proteomics investigations using LC-MS technology. Journal of Bioinformatics and Computational Biology, 6 (1), 107-123. (doi:10.1142/S0219720008003321). (PMID:18324749)

Record type: Article

Abstract

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.

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More information

Published date: February 2008
Organisations: Molecular and Cellular

Identifiers

Local EPrints ID: 355411
URI: https://eprints.soton.ac.uk/id/eprint/355411
ISSN: 0219-7200
PURE UUID: bf6dec32-cb52-4bb7-885f-047a3e67a318
ORCID for Rob Ewing: ORCID iD orcid.org/0000-0001-6510-4001

Catalogue record

Date deposited: 27 Aug 2013 13:55
Last modified: 20 Jul 2019 00:41

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