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  Interpretation of mass spectrometry data for high-throughput proteomics

Chamrad, D. C., Koerting, G., Gobom, J., Thiele, H., Klose, J., Meyer, H. E., et al. (2003). Interpretation of mass spectrometry data for high-throughput proteomics. Analytical and Bioanalytical Chemistry, 376(7), 1014-1022. doi:10.1007/s00216-003-1995-x.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-89E4-7 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-89E5-5
Genre: Journal Article
Alternative Title : Anal. Bioanal. Chem.

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 Creators:
Chamrad, Daniel C., Author
Koerting, Gerhard, Author
Gobom, Johan1, Author              
Thiele, Herbert, Author
Klose, Joachim, Author
Meyer, Helmut E., Author
Blueggel, Martin, Author
Affiliations:
1Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              

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Free keywords: bioinformatics; database; high throughput; mass spectrometry; protein identification; proteomics
 Abstract: Recent developments in proteomics have revealed a bottleneck in bioinformatics: high-quality interpretation of acquired MS data. The ability to generate thousands of MS spectra per day, and the demand for this, makes manual methods inadequate for analysis and underlines the need to transfer the advanced capabilities of an expert human user into sophisticated MS interpretation algorithms. The identification rate in current high-throughput proteomics studies is not only a matter of instrumentation. We present software for high-throughput PMF identification, which enables robust and confident protein identification at higher rates. This has been achieved by automated calibration, peak rejection, and use of a meta search approach which employs various PMF search engines. The automatic calibration consists of a dynamic, spectral information-dependent algorithm, which combines various known calibration methods and iteratively establishes an optimised calibration. The peak rejection algorithm filters signals that are unrelated to the analysed protein by use of automatically generated and dataset-dependent exclusion lists. In the "meta search" several known PMF search engines are triggered and their results are merged by use of a meta score. The significance of the meta score was assessed by simulation of PMF identification with 10,000 artificial spectra resembling a data situation close to the measured dataset. By means of this simulation the meta score is linked to expectation values as a statistical measure. The presented software is part of the proteome database ProteinScape which links the information derived from MS data to other relevant proteomics data. We demonstrate the performance of the presented system with MS data from 1891 PMF spectra. As a result of automatic calibration and peak rejection the identification rate increased from 6% to 44%.

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Language(s): eng - English
 Dates: 2003-08
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: eDoc: 194886
ISI: 000185155600009
DOI: 10.1007/s00216-003-1995-x
 Degree: -

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Title: Analytical and Bioanalytical Chemistry
  Alternative Title : Anal. Bioanal. Chem.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 376 (7) Sequence Number: - Start / End Page: 1014 - 1022 Identifier: ISSN: 1618-2642
ISSN: 1618-2650