• H. L. Rost, 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. Malmstrom, R. Aebersold, K. Reinert, and O. Kohlbacher, “OpenMS: a flexible open-source software platform for mass spectrometry data analysis,” Nat Meth, vol. 13, iss. 9, pp. 741-748, 2016.
    [Bibtex]
    @article{Rost2016,
    author = {Rost, Hannes L and Sachsenberg, Timo and Aiche, Stephan and Bielow, Chris and Weisser, Hendrik and Aicheler, Fabian and Andreotti, Sandro and Ehrlich, Hans-Christian and Gutenbrunner, Petra and Kenar, Erhan and Liang, Xiao and Nahnsen, Sven and Nilse, Lars and Pfeuffer, Julianus and Rosenberger, George and Rurik, Marc and Schmitt, Uwe and Veit, Johannes and Walzer, Mathias and Wojnar, David and Wolski, Witold E and Schilling, Oliver and Choudhary, Jyoti S and Malmstrom, Lars and Aebersold, Ruedi and Reinert, Knut and Kohlbacher, Oliver},
    title = {OpenMS: a flexible open-source software platform for mass spectrometry data analysis},
    year = {2016},
    URL = {http://dx.doi.org/10.1038/nmeth.3959},
    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.},
    journal = {Nat Meth},
    volume = {13},
    number = {9},
    pages = {741-748},
    note = {Perspective}
    }
  • J. Veit, T. Sachsenberg, A. Chernev, F. Aicheler, H. Urlaub, and O. Kohlbacher, “LFQProfiler and RNPxl: Open-Source Tools for Label-Free Quantification and Protein–RNA Cross-Linking Integrated into Proteome Discoverer,” Journal of Proteome Research, vol. 15, iss. 9, pp. 3441-3448, 2016.
    [Bibtex]
    @article{veit2016lfqprofiler,
    title={LFQProfiler and RNPxl: Open-Source Tools for Label-Free Quantification and Protein--RNA Cross-Linking Integrated into Proteome Discoverer},
    author={Veit, Johannes and Sachsenberg, Timo and Chernev, Aleksandar and Aicheler, Fabian and Urlaub, Henning and Kohlbacher, Oliver},
    journal={Journal of Proteome Research},
    volume={15},
    number={9},
    pages={3441--3448},
    year={2016},
    publisher={ACS Publications}
    }
  • P. Navarro, J. Kuharev, L. C. Gillet, O. M. Bernhardt, B. MacLean, H. L. Röst, S. A. Tate, C. Tsou, L. Reiter, U. Distler, and others, “A multi-center study benchmarks software tools for label-free proteome quantification,” Nature biotechnology, vol. 34, iss. 11, p. 1130, 2016.
    [Bibtex]
    @article{navarro2016multi,
    title={A multi-center study benchmarks software tools for label-free proteome quantification},
    author={Navarro, Pedro and Kuharev, J{\"o}rg and Gillet, Ludovic C and Bernhardt, Oliver M and MacLean, Brendan and R{\"o}st, Hannes L and Tate, Stephen A and Tsou, Chih-Chiang and Reiter, Lukas and Distler, Ute and others},
    journal={Nature biotechnology},
    volume={34},
    number={11},
    pages={1130},
    year={2016},
    publisher={Europe PMC Funders}
    }
  • [DOI] H. Weisser, J. C. Wright, J. M. Mudge, P. Gutenbrunner, and J. S. Choudhary, “Flexible Data Analysis Pipeline for High-Confidence Proteogenomics,” Journal of Proteome Research, vol. 15, iss. 12, pp. 4686-4695, 2016.
    [Bibtex]
    @article{weisser2016flexible,
    title={Flexible Data Analysis Pipeline for High-Confidence Proteogenomics},
    author={Weisser, Hendrik and Wright, James C and Mudge, Jonathan M and Gutenbrunner, Petra and Choudhary, Jyoti S},
    journal={Journal of Proteome Research},
    volume={15},
    number={12},
    pages={4686--4695},
    year={2016},
    publisher={ACS Publications},
    doi={10.1021/acs.jproteome.6b00765}
    }