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Fast and Accurate Database Searches with MS-GF plus Percolator:  
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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2014 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 13, no 2, 890-897 p.Article in journal (Refereed) Published
Abstract [en]

One can interpret fragmentation spectra stemming from peptides in mass-spectrometry-based proteomics experiments using so-called database search engines. Frequently, one also runs post-processors such as Percolator to assess the confidence, infer unique peptides, and increase the number of identifications. A recent search engine, MS-GF+, has shown promising results, due to a new and efficient scoring algorithm. However, MS-GF+ provides few statistical estimates about the peptide-spectrum matches, hence limiting the biological interpretation. Here, we enabled Percolator processing for MS-GF+ output and observed an increased number of identified peptides for a wide variety of data sets. In addition, Percolator directly reports p values and false discovery rate estimates, such as q values and posterior error probabilities, for peptide-spectrum matches, peptides, and proteins, functions that are useful for the whole proteomics community.

Place, publisher, year, edition, pages
2014. Vol. 13, no 2, 890-897 p.
Keyword [en]
shotgun proteomics, bioinformatics, machine learning, confidence estimation
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-99447DOI: 10.1021/pr400937nISI: 000331164100049OAI: oai:DiVA.org:su-99447DiVA: diva2:686997
Funder
Swedish Research Council
Available from: 2014-01-13 Created: 2014-01-13 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
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Available from: 2014-03-13 Created: 2014-02-12 Last updated: 2014-02-14Bibliographically approved

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