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Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2010 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 9, no 10, 5209-5216 p.Article in journal (Refereed) Published
Abstract [en]

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Most notably such predictions are used to weed out incorrect peptide-spectrum matches, and to design targeted proteomics experiments. In this study, we describe a RT predictor, ELUDE, which can be employed in both applications. ELUDE's predictions are based on 60 features derived from the peptide's amino acid composition and optimally combined using kernel regression. When sufficient data is available, ELUDE derives a retention time index for the condition at hand making it fully portable to new chromatographic conditions. In cases when little training data is available, as often is the case in targeted proteomics experiments, ELUDE selects and calibrates a model from a library of pretrained predictors. Both model selection and calibration are carried out via robust statistical methods and thus ELUDE can handle situations where the calibration data contains erroneous data points. We benchmarked our method against two state-of-the-art predictors and showed that ELUDE outperforms these methods and tracked up to 34% more peptides in a theoretical SRM method creation experiment. ELUDE is freely available under Apache License from http://per-colator.com.

Place, publisher, year, edition, pages
2010. Vol. 9, no 10, 5209-5216 p.
Keyword [en]
retention time prediction, support vector regression, targeted proteomics, peptide identification, bioinformatics
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:su:diva-49432DOI: 10.1021/pr1005058ISI: 000282257800031OAI: oai:DiVA.org:su-49432DiVA: diva2:379407
Note
authorCount :3Available from: 2010-12-17 Created: 2010-12-14 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
Open this publication in new window or tab >>Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mass spectrometry-based methods are among the most commonly used techniques to characterize proteins in biological samples. With rapid technological developments allowing increasing throughput, thousands of proteins can now be monitored in a matter of hours. However, these advances brought a whole new set of analytical challenges. At the moment, it is no longer possible to rely on human experts to process the data. Instead, accurate computational tools are required.

In line with these observations, my research work has involved development of computational methods to facilitate the analysis of mass spectrometry-based experiments. In particular, the projects included in this thesis revolve around the chromatography step of such experiments, where peptides are separated according to their hydrophobicity.

The first part of the thesis describes an algorithm to predict retention time from peptide sequences. The method provides more accurate predictions compared to previous approaches, while being easily transferable to other chromatography setups. In addition, it gives equally good predictions for peptides carrying arbitrary posttranslational modifications as for unmodified peptides.

The second part of the thesis includes two applications of retention time predictions in the context of mass spectrometry-based proteomics experiments. First, we show how theoretical calculations of masses and retention times can be used to infer proteins in shotgun proteomics experiments. Secondly, we illustrate the use of retention time predictions to calculate optimized gradient functions for reversed-phase liquid chromatography.

Place, publisher, year, edition, pages
Department of Biochemistry and Biophysics. Stockholm University, 2013. 58 p.
Keyword
mass spectrometry; reversed-phase liquid chromatography; retention time prediction; bioinfomatics; computational mass spectrometry
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-94800 (URN)978-91-7447-775-7 (ISBN)
Public defence
2013-11-29, Magnelisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

A the time of the doctoral defence the paper nr. 4 had a status Epubl. ahead of  print.

Available from: 2013-11-07 Created: 2013-10-14 Last updated: 2013-10-23Bibliographically approved

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