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Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
2013 (English)In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, Vol. 80, 123-131 p.Article in journal (Refereed) Published
Abstract [en]

The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.

Place, publisher, year, edition, pages
2013. Vol. 80, 123-131 p.
Keyword [en]
Shotgun proteomics, Peptides, Statistics
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-89873DOI: 10.1016/j.jprot.2012.12.007ISI: 000317544700010OAI: oai:DiVA.org:su-89873DiVA: diva2:621263
Note

AuthorCount:4;

Available from: 2013-05-14 Created: 2013-05-14 Last updated: 2017-12-06Bibliographically approved
In thesis
1. The accuracy of statistical confidence estimates in shotgun proteomics
Open this publication in new window or tab >>The accuracy of statistical confidence estimates in shotgun proteomics
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High-throughput techniques are currently some of the most promising methods to study molecular biology, with the potential to improve medicine and enable new biological applications. In proteomics, the large scale study of proteins, the leading method is mass spectrometry. At present researchers can routinely identify and quantify thousands of proteins in a single experiment with the technique called shotgun proteomics.

A challenge of these experiments is the computational analysis and the interpretation of the mass spectra. A shotgun proteomics experiment easily generates tens of thousands of spectra, each thought to represent a peptide from a protein. Due to the immense biological and technical complexity, however, our computational tools often misinterpret these spectra and derive incorrect peptides. As a consequence, the biological interpretation of the experiment relies heavily on the statistical confidence that we estimate for the identifications.

In this thesis, I have included four articles from my research on the accuracy of the statistical confidence estimates in shotgun proteomics, how to accomplish and evaluate it. In the first two papers a new method to use pre-characterized protein samples to evaluate this accuracy is presented. The third paper deals with how to avoid statistical inaccuracies when using machine learning techniques to analyze the data. In the fourth paper, we present a new tool for analyzing shotgun proteomics results, and evaluate the accuracy of  its statistical estimates using the method from the first papers.

The work I have included here can facilitate the development of new and accurate computational tools in mass spectrometry-based proteomics. Such tools will help making the interpretation of the spectra and the downstream biological conclusions more reliable.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2014. 40 p.
Keyword
Proteomics, Peptides, Statistics, Mass spectrometry, Tandem mass spectrometry
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-100769 (URN)978-91-7447-787-0 (ISBN)
Public defence
2014-04-04, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 09:30 (English)
Opponent
Supervisors
Available from: 2014-03-13 Created: 2014-02-12 Last updated: 2014-02-14Bibliographically approved

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