Proteome analysis using HPLC/MS

Overview

In the context of HPLC-MS we worked during the report period in several applied projects on pipelines for quantitative HPLC-MS analysis. In the next years, we will work on more basic research questions, namely on the problem of protein identification resp. protein inference from a set of HPLC-MS measurements. The traditional algorithmic approach for protein identification disregards the inherent information wealth available in a set of mass spectra. By blindly splitting the identification of fragment spectra into several parts, it is not possible that peptide identifcations re-enforce each other and evidence from one spectrum supports the identi cation of a similar one. Further, the MS1 spectra carry more information that is of relevance for identification than just the parent mass of the fragmentation. This information such as further supporting mass positions or retention time information regularly remains either unobserved or is only used out of context and thereby lost for further steps. For more informations about OpenMS click here or read [1, 2].

People currently working mainly on this topic:

Julianus Pfeuffer: Protein inference with bayesian models

Relevant publications:

[1] K. Reinert, O. Kohlbacher, C. Gröpl, E. Lange, O. Schulz-Trieglaff, M. Sturm, and N. Pfeifer, “OpenMS – A Framework for Quantitative HPLC/MS-Based Proteomics,” in Computational Proteomics, 2006.
[Bibtex]
@inproceedings{fu_mi_publications397,
editor = {C. G. Huber and O. Kohlbacher and K. Reinert},
note = {{\ensuremath{<}}span class='mathrm'{\ensuremath{>}}\<{\ensuremath{<}}/span{\ensuremath{>}}http://drops.dagstuhl.de/opus/volltexte/2006/546{\ensuremath{<}}span class='mathrm'{\ensuremath{>}}\>{\ensuremath{<}}/span{\ensuremath{>}} [date of citation:  2006-01-01]},
booktitle = {Computational Proteomics},
publisher = {Internationales Begegnungs- und Forschungszentrum f{\~A}?r Informatik  (IBFI), Schloss Dagstuhl, Germany},
series = {Dagstuhl Seminar Proceedings},
author = {K. Reinert and O. Kohlbacher and C. Gr{\"o}pl and E. Lange and O. Schulz-Trieglaff and M. Sturm and N. Pfeifer},
number = {05471},
title = {OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics},
year = {2006},
url = {http://publications.imp.fu-berlin.de/397/}
}
[2] H. L. Röst, T. Sachsenberg, S. Aiche, C. Bielow, H. Weisser, F. Aicheler, S. Andreotti, H. Ehrlich, P. Gutenbrunner, E. Kenar, X. Liang, S. Nahnsen, L. Nilse, J. Pfeuffer, G. Rosenberger, M. Rurik, U. Schmitt, J. Veit, M. Walzer, D. Wojnar, W. E. Wolski, O. Schilling, J. S. Choudhary, L. Malmström, R. Aebersold, K. Reinert, and O. Kohlbacher, “OpenMS: a flexible open-source software platform for mass spectrometry data analysis,” Nature Methods, vol. 13, iss. 9, p. 741–748, 2016.
[Bibtex]
@article{fu_mi_publications2129,
month = {August},
pages = {741--748},
year = {2016},
title = {OpenMS: a flexible open-source software platform for mass spectrometry data analysis},
number = {9},
author = {Hannes L R{\"o}st and Timo Sachsenberg and Stephan Aiche and Chris Bielow and Hendrik Weisser and Fabian Aicheler and Sandro Andreotti and Hans-Christian Ehrlich and Petra Gutenbrunner and Erhan Kenar and Xiao Liang and Sven Nahnsen and Lars Nilse and Julianus Pfeuffer and George Rosenberger and Marc Rurik and Uwe Schmitt and Johannes Veit and Mathias Walzer and David Wojnar and Witold E Wolski and Oliver Schilling and Jyoti S Choudhary and Lars Malmstr{\"o}m and Ruedi Aebersold and Knut Reinert and Oliver Kohlbacher},
journal = {Nature Methods},
publisher = {Springer Nature/Macmillan Publishers Limited},
volume = {13},
abstract = {High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.},
url = {http://publications.imp.fu-berlin.de/2129/}
}