OpenMS

An open-source framework for mass spectrometry and TOPP – The OpenMS Proteomics Pipeline

Publications

Here you find Publications about algorithmic components included in OpenMS as well as data analysis project conducted by members of the OpenMS core team.

If you want to cite OpenMS please cite the following paper:

Marc Sturm, Andreas Bertsch, Clemens Gröpl, Andreas Hildebrandt, Rene Hussong, Eva Lange, Nico Pfeifer, Ole Schulz-Trieglaff, Alexandra Zerck, Knut Reinert, and Oliver Kohlbacher, 2008.
“OpenMS – an Open-Source Software Framework for Mass Spectrometry”
BMC Bioinformatics 9: 163. doi:10.1186/1471-2105-9-163.

2015

  • H. Röst, G. Rosenberger, R. Aebersold, and L. Malmström, “Efficient visualization of high-throughput targeted proteomics experiments: TAPIR,” Bioinformatics, pp. btv152, 2015.
    [Bibtex]
    @article{rost2015efficient,
      title={Efficient visualization of high-throughput targeted proteomics experiments: TAPIR},
      author={R{\"o}st, Hannes L and Rosenberger, George and Aebersold, Ruedi and Malmstr{\"o}m, Lars},
      journal={Bioinformatics},
      pages={btv152},
      year={2015},
      publisher={Oxford Univ Press}
    }
  • C. Ranninger, M. Rurik, A. Limonciel, S. Ruzek, R. Reischl, A. Wilmes, P. Jennings, P. Hewitt, W. Dekant, O. Kohlbacher, and others, “Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High Performance Liquid Chromatography-Mass Spectrometry-based Experimental and Computational Pipeline,” Journal of Biological Chemistry, vol. 290, iss. 31, pp. 19121-19132, 2015.
    [Bibtex]
    @article{ranninger2015nephron,
      title={Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High Performance Liquid Chromatography-Mass Spectrometry-based Experimental and Computational Pipeline},
      author={Ranninger, Christina and Rurik, Marc and Limonciel, Alice and Ruzek, Silke and Reischl, Roland and Wilmes, Anja and Jennings, Paul and Hewitt, Philip and Dekant, Wolfgang and Kohlbacher, Oliver and others},
      journal={Journal of Biological Chemistry},
      volume={290},
      number={31},
      pages={19121--19132},
      year={2015},
      publisher={ASBMB}
    }
  • F. Aicheler, J. Li, M. Hoene, R. Lehmann, G. Xu, and O. Kohlbacher, “Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches,” Analytical chemistry, vol. 87, iss. 15, pp. 7698-7704, 2015.
    [Bibtex]
    @article{aicheler2015retention,
      title={Retention Time Prediction Improves Identification in Nontargeted Lipidomics Approaches},
      author={Aicheler, Fabian and Li, Jia and Hoene, Miriam and Lehmann, Rainer and Xu, Guowang and Kohlbacher, Oliver},
      journal={Analytical chemistry},
      volume={87},
      number={15},
      pages={7698--7704},
      year={2015},
      publisher={American Chemical Society}
    }
  • L. Nilse, F. C. Sigloch, M. Biniossek, and O. Schilling, “Towards improved peptide feature detection in quantitative proteomics using stable isotope labelling,” PROTEOMICS-Clinical Applications, 2015.
    [Bibtex]
    @article{nilse2015towards,
      title={Towards improved peptide feature detection in quantitative proteomics using stable isotope labelling},
      author={Nilse, Lars and Sigloch, Florian Christoph and Biniossek, Martin L and Schilling, Oliver},
      journal={PROTEOMICS-Clinical Applications},
      year={2015},
      publisher={Wiley Online Library}
    }
  • K. Bartkowiak, M. Kwiatkowski, F. Buck, T. Gorges, L. Nilse, V. Assmann, A. Andreas, V. Müller, H. Wikman, S. Riethdorf, and others, “Disseminated tumor cells persist in the bone marrow of breast cancer patients through sustained activation of the unfolded protein response,” Cancer research, vol. 75, iss. 24, pp. 5367-5377, 2015.
    [Bibtex]
    @article{bartkowiak2015disseminated,
      title={Disseminated tumor cells persist in the bone marrow of breast cancer patients through sustained activation of the unfolded protein response},
      author={Bartkowiak, Kai and Kwiatkowski, Marcel and Buck, Friedrich and Gorges, Tobias M and Nilse, Lars and Assmann, Volker and Andreas, Antje and M{\"u}ller, Volkmar and Wikman, Harriet and Riethdorf, Sabine and others},
      journal={Cancer research},
      volume={75},
      number={24},
      pages={5367--5377},
      year={2015},
      publisher={AACR}
    }
  • H. Röst, U. Schmitt, R. Aebersold, and L. Malmström, “Fast and Efficient XML Data Access for Next-Generation Mass Spectrometry,” , 2015.
    [Bibtex]
    @article{rost2015fast,
      title={Fast and Efficient XML Data Access for Next-Generation Mass Spectrometry},
      author={R{\"o}st, Hannes L and Schmitt, Uwe and Aebersold, Ruedi and Malmstr{\"o}m, Lars},
      year={2015}
    }
  • S. Aiche, T. Sachsenberg, E. Kenar, M. Walzer, B. Wiswedel, T. Kristl, M. Boyles, A. Duschl, C. Huber, M. Berthold, and others, “Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry,” Proteomics, vol. 15, iss. 8, pp. 1443-1447, 2015.
    [Bibtex]
    @article{aiche2015workflows,
      title={Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry},
      author={Aiche, Stephan and Sachsenberg, Timo and Kenar, Erhan and Walzer, Mathias and Wiswedel, Bernd and Kristl, Theresa and Boyles, Matthew and Duschl, Albert and Huber, Christian G and Berthold, Michael R and others},
      journal={Proteomics},
      volume={15},
      number={8},
      pages={1443--1447},
      year={2015}
    }

2014

  • T. Sachsenberg, F. Herbst, M. Taubert, R. Kermer, N. Jehmlich, M. von Bergen, J. Seifert, and O. Kohlbacher, “MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics,” Journal of proteome research, vol. 14, iss. 2, pp. 619-627, 2014.
    [Bibtex]
    @article{sachsenberg2014metaprosip,
      title={MetaProSIP: automated inference of stable isotope incorporation rates in proteins for functional metaproteomics},
      author={Sachsenberg, Timo and Herbst, Florian-Alexander and Taubert, Martin and Kermer, Ren{\'e} and Jehmlich, Nico and von Bergen, Martin and Seifert, Jana and Kohlbacher, Oliver},
      journal={Journal of proteome research},
      volume={14},
      number={2},
      pages={619--627},
      year={2014},
      publisher={American Chemical Society}
    }
  • K. Kramer, T. Sachsenberg, B. Beckmann, S. Qamar, K. Boon, M. Hentze, O. Kohlbacher, and H. Urlaub, “Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins,” Nature methods, vol. 11, iss. 10, pp. 1064-1070, 2014.
    [Bibtex]
    @article{kramer2014photo,
      title={Photo-cross-linking and high-resolution mass spectrometry for assignment of RNA-binding sites in RNA-binding proteins},
      author={Kramer, Katharina and Sachsenberg, Timo and Beckmann, Benedikt M and Qamar, Saadia and Boon, Kum-Loong and Hentze, Matthias W and Kohlbacher, Oliver and Urlaub, Henning},
      journal={Nature methods},
      volume={11},
      number={10},
      pages={1064--1070},
      year={2014},
      publisher={Nature Publishing Group}
    }
  • H. Röst, G. Rosenberger, P. Navarro, L. Gillet, S. Miladinovi’c, O. Schubert, W. Wolski, B. Collins, J. Malmström, L. Malmström, and others, “OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data,” Nature biotechnology, vol. 32, iss. 3, pp. 219-223, 2014.
    [Bibtex]
    @article{rost2014openswath,
      title={OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data},
      author={R{\"o}st, Hannes L and Rosenberger, George and Navarro, Pedro and Gillet, Ludovic and Miladinovi{\'c}, Sa{\v{s}}a M and Schubert, Olga T and Wolski, Witold and Collins, Ben C and Malmstr{\"o}m, Johan and Malmstr{\"o}m, Lars and others},
      journal={Nature biotechnology},
      volume={32},
      number={3},
      pages={219--223},
      year={2014},
      publisher={Nature Publishing Group}
    }
  • Go to document J. Griss, A. Jones, T. Sachsenberg, M. Walzer, L. Gatto, J. Hartler, G. Thallinger, R. Salek, C. Steinbeck, N. Neuhauser, J. Cox, S. Neumann, J. Fan, F. Reisinger, Q. Xu, N. del Toro, Y. Perez-Riverol, F. Ghali, N. Bandeira, I. Xenarios, O. Kohlbacher, J. A. Vizcaino, and H. Hermjakob, “The mzTab Data Exchange Format: communicating MS-based proteomics and metabolomics experimental results to a wider audience,” Molecular & Cellular Proteomics, 2014.
    [Bibtex]
    @article{Griss30062014,
    author = {Griss, Johannes and Jones, Andrew R. and Sachsenberg, Timo and Walzer, Mathias and Gatto, Laurent and Hartler, Jurgen and Thallinger, Gerhard G. and Salek, Reza M. and Steinbeck, Christoph and Neuhauser, Nadin and Cox, Jurgen and Neumann, Steffen and Fan, Jun and Reisinger, Florian and Xu, Qing-Wei and del Toro, Noemi and Perez-Riverol, Yasset and Ghali, Fawaz and Bandeira, Nuno and Xenarios, Ioannis and Kohlbacher, Oliver and Vizcaino, Juan Antonio and Hermjakob, Henning},
    title = {The {mzTab} {D}ata {E}xchange {F}ormat: communicating MS-based proteomics and metabolomics experimental results to a wider audience},
    year = {2014},
    doi = {10.1074/mcp.O113.036681},
    abstract ={The HUPO Proteomics Standards Initiative (PSI) has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS) based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools like Microsoft Excel or R.
    We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplementary material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. This ranges from a simple summary of the final results up to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found at http://mztab.googlecode.com.},
    URL = {http://www.mcponline.org/content/early/2014/07/08/mcp.O113.036681.abstract},
    eprint = {http://www.mcponline.org/content/early/2014/07/08/mcp.O113.036681.full.pdf+html},
    journal = {Molecular \& Cellular Proteomics}
    }
  • Go to document M. Walzer, L. E. Pernas, S. Nasso, W. Bittremieux, S. Nahnsen, P. Kelchtermans, P. Pichler, H. van den Toorn, A. Staes, J. Vandenbussche, M. Mazanek, T. Taus, R. Scheltema, C. Kelstrup, L. Gatto, B. van Breukelen, S. Aiche, D. Valkenborg, K. Laukens, K. Lilley, J. Olsen, A. Heck, K. Mechtler, R. Aebersold, K. Gevaert, J. A. Vizcaíno, H. Hermjakob, O. Kohlbacher, and L. Martens, “qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments,” Molecular & Cellular Proteomics, vol. 13, iss. 8, pp. 1905-1913, 2014.
    [Bibtex]
    @article{Walzer01082014,
    author = {Walzer, Mathias and Pernas, Lucia Espona and Nasso, Sara and Bittremieux, Wout and Nahnsen, Sven and Kelchtermans, Pieter and Pichler, Peter and van den Toorn, Henk W. P. and Staes, An and Vandenbussche, Jonathan and Mazanek, Michael and Taus, Thomas and Scheltema, Richard A. and Kelstrup, Christian D. and Gatto, Laurent and van Breukelen, Bas and Aiche, Stephan and Valkenborg, Dirk and Laukens, Kris and Lilley, Kathryn S. and Olsen, Jesper V. and Heck, Albert J. R. and Mechtler, Karl and Aebersold, Ruedi and Gevaert, Kris and Vizcaíno, Juan Antonio and Hermjakob, Henning and Kohlbacher, Oliver and Martens, Lennart},
    title = {{qcML}: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments},
    volume = {13},
    number = {8},
    pages = {1905-1913},
    year = {2014},
    doi = {10.1074/mcp.M113.035907},
    abstract ={Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml.},
    URL = {http://www.mcponline.org/content/13/8/1905.abstract},
    eprint = {http://www.mcponline.org/content/13/8/1905.full.pdf+html},
    journal = {Molecular \& Cellular Proteomics}
    }
  • Go to document E. Kenar, H. Franken, S. Forcisi, H. Wörmann Kilia nand Häring, R. Lehmann, S. Philippe, A. Zell, and O. Kohlbacher, “Automated Label-free Quantification of Metabolites from Liquid Chromatography-Mass Spectrometry Data.,” Mol Cell Proteomics, vol. 13, iss. 1, pp. 348-359, 2014.
    [Bibtex]
    @ARTICLE{Kenar2014,
      author = {Kenar, Erhan and Franken, Holger and Forcisi, Sara and W{\"o}rmann, Kilia nand H{\"a}ring, Hans-Ulrich and Lehmann, Rainer and Schmitt-Koppli Philippe and Zell, Andreas and Kohlbacher, Oliver},
      title = {Automated Label-free Quantification of Metabolites from Liquid Chromatography-Mass Spectrometry Data.},
      journal = {Mol Cell Proteomics},
      year = {2014},
      volume = {13},
      pages = {348--359},
      number = {1},
      month = {Jan},
      abstract = {Liquid chromatography coupled to mass spectrometry (LC-MS) has become
      a standard technology in metabolomics. In particular, label-free
      quantification based on LC-MS is easily amenable to large-scale studies
      and thus well suited to clinical metabolomics. Large-scale studies,
      however, require automated processing of the large and complex LC-MS
      datasets. We present a novel algorithm for the detection of mass
      traces and their aggregation into features (i.e. all signals caused
      by the same analyte species) that is computationally efficient and
      sensitive and that leads to reproducible quantification results.
      The algorithm is based on a sensitive detection of mass traces, which
      are then assembled into features based on mass-to-charge spacing,
      co-elution information, and a support vector machine-based classifier
      able to identify potential metabolite isotope patterns. The algorithm
      is not limited to metabolites but is applicable to a wide range of
      small molecules (e.g. lipidomics, peptidomics), as well as to other
      separation technologies. We assessed the algorithm's robustness with
      regard to varying noise levels on synthetic data and then validated
      the approach on experimental data investigating human plasma samples.
      We obtained excellent results in a fully automated data-processing
      pipeline with respect to both accuracy and reproducibility. Relative
      to state-of-the art algorithms, ours demonstrated increased precision
      and recall of the method. The algorithm is available as part of the
      open-source software package OpenMS and runs on all major operating
      systems.},
      doi = {10.1074/mcp.M113.031278},
      institution = {Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology
      Center, and Department of Computer Science, University of Tuebingen,
      Sand 14, 72076 Tuebingen, Germany;},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pii = {M113.031278},
      pmid = {24176773},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1074/mcp.M113.031278}
    }
  • Go to document H. Röst, U. Schmitt, R. Aebersold, and L. Malmström, “pyOpenMS: A Python-based interface to the OpenMS mass-spectrometry algorithm library.,” Proteomics, vol. 14, iss. 1, pp. 74-77, 2014.
    [Bibtex]
    @ARTICLE{Roest2014,
      author = {R{\"o}st, Hannes L. and Schmitt, Uwe and Aebersold, Ruedi and Malmstr{\"o}m, Lars},
      title = {pyOpenMS: A Python-based interface to the OpenMS mass-spectrometry algorithm library.},
      journal = {Proteomics},
      year = {2014},
      volume = {14},
      pages = {74--77},
      number = {1},
      month = {Jan},
      abstract = {pyOpenMS is an open-source, Python-based interface to the C++ OpenMS
      library, providing facile access to a feature-rich, open-source algorithm
      library for MS-based proteomics analysis. It contains Python bindings
      that allow raw access to the data structures and algorithms implemented
      in OpenMS, specifically those for file access (mzXML, mzML, TraML,
      mzIdentML among others), basic signal processing (smoothing, filtering,
      de-isotoping, and peak-picking) and complex data analysis (including
      label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus
      allows fast prototyping and efficient workflow development in a fully
      interactive manner (using the interactive Python interpreter) and
      is also ideally suited for researchers not proficient in C++. In
      addition, our code to wrap a complex C++ library is completely open-source,
      allowing other projects to create similar bindings with ease. The
      pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms
      while the autowrap tool to create Cython code automatically is available
      at https://pypi.python.org/pypi/autowrap (both released under the
      3-clause BSD licence).},
      doi = {10.1002/pmic.201300246},
      institution = {Department of Biology, Institute of Molecular Systems Biology, ETH
      Zurich, Zurich, Switzerland; Ph.D. Program in Systems Biology, University
      of Zurich and ETH Zurich, Zurich, Switzerland.},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pmid = {24420968},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1002/pmic.201300246}
    }

2013

  • Go to document A. K. Hildebrandt, E. Althaus, H. Lenhof, C. Hung, A. Tholey, and A. Hildebrandt, “Efficient Interpretation of Tandem Mass Tags in Top-Down Proteomics,” in German Conference on Bioinformatics 2013, Dagstuhl, Germany, 2013, pp. 56-67.
    [Bibtex]
    @INPROCEEDINGS{Hildebrandt2013,
      author = {Anna Katharina Hildebrandt and Ernst Althaus and Hans-Peter Lenhof and Chien-Wen Hung and Andreas Tholey and Andreas Hildebrandt},
      title = {Efficient Interpretation of Tandem Mass Tags in Top-Down Proteomics},
      booktitle = {German Conference on Bioinformatics 2013},
      year = {2013},
      editor = {Tim Beißbarth and Martin Kollmar and Andreas Leha and Burkhard Morgenstern
      and Anne-Kathrin Schultz and Stephan Waack and Edgar Wingender},
      volume = {34},
      series = {OpenAccess Series in Informatics (OASIcs)},
      pages = {56--67},
      address = {Dagstuhl, Germany},
      publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
      abstract = {Mass spectrometry is the major analytical tool for the identification
      and quantification of proteins in biological samples. In so-called
      top-down proteomics, separation and mass spectrometric analysis is
      performed at the level of intact proteins, without preparatory digestion
      steps. It has been shown that the tandem mass tag (TMT) labeling
      technology, which is often used for quantification based on digested
      proteins (bottom-up studies), can be applied in top-down proteomics
      as well. This, however, leads to a complex interpretation problem,
      where we need to annotate measured peaks with their respective generating
      protein, the number of charges, and the a priori unknown number of
      TMT-groups attached to this protein. In this work, we give an algorithm
      for the efficient enumeration of all valid annotations that fulfill
      available experimental constraints. Applying the algorithm to real-world
      data, we show that the annotation problem can indeed be efficiently
      solved. However, our experiments also demonstrate that reliable annotation
      in complex mixtures requires at least partial sequence information
      and high mass accuracy and resolution to go beyond the proof-of-concept
      stage.},
      annote = {Keywords: Mass spectrometry, TMT labeling, Top-down Proteomics},
      doi = {http://dx.doi.org/10.4230/OASIcs.GCB.2013.56},
      isbn = {978-3-939897-59-0},
      issn = {2190-6807},
      owner = {hw5},
      timestamp = {2014.01.16},
      url = {http://drops.dagstuhl.de/opus/volltexte/2013/4230},
      urn = {urn:nbn:de:0030-drops-42304}
    }
  • Go to document S. Nahnsen, C. Bielow, K. Reinert, and O. Kohlbacher, “Tools for label-free peptide quantification.,” Mol Cell Proteomics, vol. 12, iss. 3, pp. 549-556, 2013.
    [Bibtex]
    @ARTICLE{Nahnsen2013a,
      author = {Nahnsen, Sven and Bielow, Chris and Reinert, Knut and Kohlbacher, Oliver},
      title = {Tools for label-free peptide quantification.},
      journal = {Mol Cell Proteomics},
      year = {2013},
      volume = {12},
      pages = {549--556},
      number = {3},
      month = {Mar},
      abstract = {The increasing scale and complexity of quantitative proteomics studies
      complicate subsequent analysis of the acquired data. Untargeted label-free
      quantification, based either on feature intensities or on spectral
      counting, is a method that scales particularly well with respect
      to the number of samples. It is thus an excellent alternative to
      labeling techniques. In order to profit from this scalability, however,
      data analysis has to cope with large amounts of data, process them
      automatically, and do a thorough statistical analysis in order to
      achieve reliable results. We review the state of the art with respect
      to computational tools for label-free quantification in untargeted
      proteomics. The two fundamental approaches are feature-based quantification,
      relying on the summed-up mass spectrometric intensity of peptides,
      and spectral counting, which relies on the number of MS/MS spectra
      acquired for a certain protein. We review the current algorithmic
      approaches underlying some widely used software packages and briefly
      discuss the statistical strategies for analyzing the data.},
      doi = {10.1074/mcp.R112.025163},
      institution = {Center for Bioinformatics, Quantitative Biology Center and Department
      of Computer Science, University of Tübingen, Tübingen, Germany.},
      keywords = {Algorithms; Animals; Humans; Peptides, analysis; Proteome, analysis;
      Proteomics, methods; Reproducibility of Results; Software; Tandem
      Mass Spectrometry, methods},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pii = {R112.025163},
      pmid = {23250051},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1074/mcp.R112.025163}
    }
  • Go to document S. Nahnsen, T. Sachsenberg, and O. Kohlbacher, “PTMeta: increasing identification rates of modified peptides using modification prescanning and meta-analysis.,” Proteomics, vol. 13, iss. 6, pp. 1042-1051, 2013.
    [Bibtex]
    @ARTICLE{Nahnsen2013,
      author = {Nahnsen, Sven and Sachsenberg, Timo and Kohlbacher, Oliver},
      title = {PTMeta: increasing identification rates of modified peptides using modification prescanning and meta-analysis.},
      journal = {Proteomics},
      year = {2013},
      volume = {13},
      pages = {1042--1051},
      number = {6},
      month = {Mar},
      abstract = {The analysis of peptides and proteins in complex biological systems
      has greatly improved over the last decade. State-of-the-art mass
      spectrometric instruments combined with adequate software tools allow
      for more and more comprehensive proteome analyses. Most proteome-wide
      studies focus on the analysis of unmodified proteins or look at selected
      modifications only. However, spectral information of protein modifications,
      chemically induced through sample preparation or post-translationally
      attached in biological pathways is acquired as a significant, yet
      disregarded, fraction of tandem spectra in most discovery studies.
      We present a new computational pipeline, PTMeta, to uncover information
      of modifications attached to peptides. We use modification prescanning
      to pinpoint the most abundant potential modifications, followed by
      extensive database search and a statistical framework to combine
      results from database search runs with different modification settings.},
      doi = {10.1002/pmic.201200315},
      institution = {Quantitative Biology Center, University of Tuebingen, Tuebingen,
      Germany. svendotnahnsenatuni-tuebingendotde},
      keywords = {Algorithms; Databases, Protein; Escherichia coli Proteins, chemistry;
      Humans; Meta-Analysis as Topic; Molecular Weight; Peptide Fragments,
      chemistry; Peptide Mapping, methods; Protein Processing, Post-Translational;
      Proteome, chemistry; Search Engine; Software},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pmid = {23335442},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1002/pmic.201200315}
    }
  • Go to document V. Neu, C. Bielow, P. Schneider, K. Reinert, H. Stuppner, and C. Huber, “Investigation of reaction mechanisms of drug degradation in the solid state: a kinetic study implementing ultrahigh-performance liquid chromatography and high-resolution mass spectrometry for thermally stressed thyroxine.,” Anal Chem, vol. 85, iss. 4, pp. 2385-2390, 2013.
    [Bibtex]
    @ARTICLE{Neu2013a,
      author = {Neu, Volker and Bielow, Chris and Schneider, Peter and Reinert, Knut and Stuppner, Hermann and Huber, Christian G.},
      title = {Investigation of reaction mechanisms of drug degradation in the solid state: a kinetic study implementing ultrahigh-performance liquid chromatography and high-resolution mass spectrometry for thermally stressed thyroxine.},
      journal = {Anal Chem},
      year = {2013},
      volume = {85},
      pages = {2385--2390},
      number = {4},
      month = {Feb},
      __markedentry = {[hw5:]},
      abstract = {A reaction scheme was derived for the thermal degradation of thyroxine
      in the solid state, using data obtained from ultrahigh-performance
      liquid chromatography and high-resolution mass spectrometry (UHPLC-HRMS).
      To study the reaction mechanism and kinetics of the thermal degradation
      of the pharmaceutical in the solid state, a workflow was developed
      by generating compound-specific, time-dependent degradation or formation
      curves of at least 13 different degradation products. Such curves
      allowed one to distinguish between first- and second-generation degradation
      products, as well as impurities resulting from chemical synthesis.
      The structures of the degradation products were derived from accurate
      molecular masses and multistage mass spectrometry. Deiodination and
      oxidative side chain degradation were found to be the major degradation
      reactions, resulting in the formation of deiodinated thyroxines,
      as well as acetic acid, benzoic acid, formaldehyde, acetamide, hydroxyacetic
      acid, oxoacetic acid, hydroxyacetamide, or oxoacetamide derivatives
      of thyroxine or deiodinated thyroxine. Upon additional structural
      verification of mass spectrometric data using nuclear magnetic resonance
      spectroscopy, this comprehensive body of data sheds light on an elaborate,
      radical-driven reaction scheme, explaining the presence or formation
      of impurities in thermally stressed thyroxine.},
      doi = {10.1021/ac303404e},
      institution = {Department of Molecular Biology, Division of Chemistry and Bioanalytics,
      University of Salzburg, Hellbrunner Strasse 34, 5020 Salzburg, Austria.},
      keywords = {Chromatography, High Pressure Liquid; Drug Stability; Hydrolysis;
      Kinetics; Magnetic Resonance Spectroscopy; Oxidation-Reduction; Spectrometry,
      Mass, Electrospray Ionization; Temperature; Thyroxine, analysis/metabolism;
      Time Factors},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pmid = {23311729},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1021/ac303404e}
    }
  • Go to document J. Novak, T. Sachsenberg, D. Hoksza, T. Skopal, and O. Kohlbacher, “On comparison of SimTandem with state-of-the-art peptide identification tools, efficiency of precursor mass filter and dealing with variable modifications.,” J Integr Bioinform, vol. 10, iss. 3, pp. 228, 2013.
    [Bibtex]
    @ARTICLE{Novak2013,
      author = {Novak, Jiri and Sachsenberg, Timo and Hoksza, David and Skopal, Tomas and Kohlbacher, Oliver},
      title = {On comparison of SimTandem with state-of-the-art peptide identification tools, efficiency of precursor mass filter and dealing with variable modifications.},
      journal = {J Integr Bioinform},
      year = {2013},
      volume = {10},
      pages = {228},
      number = {3},
      abstract = {The similarity search in theoretical mass spectra generated from protein
      sequence databases is a widely accepted approach for identification
      of peptides from query mass spectra produced by shotgun proteomics.
      Growing protein sequence databases and noisy query spectra demand
      database indexing techniques and better similarity measures for the
      comparison of theoretical spectra against query spectra. We employ
      a modification of previously proposed parameterized Hausdorff distance
      for comparisons of mass spectra. The new distance outperforms the
      original distance, the angle distance and state-of-the-art peptide
      identification tools OMSSA and X!Tandem in the number of identified
      peptides even though the q-value is only 0.001. When a precursor
      mass filter is used as a database indexing technique, our method
      outperforms OMSSA in the speed of search. When variable modifications
      are not searched, the search time is similar to X!Tandem. We show
      that the precursor mass filter is an efficient database indexing
      technique for high-accuracy data even though many variable modifications
      are being searched. We demonstrate that the number of identified
      peptides is bigger when variable modifications are searched separately
      by more search runs of a peptide identification engine. Otherwise,
      the false discovery rates are affected by mixing unmodified and modified
      spectra together resulting in a lower number of identified peptides.
      Our method is implemented in the freely available application SimTandem
      which can be used in the framework TOPP based on OpenMS.},
      doi = {10.2390/biecoll-jib-2013-228},
      institution = {Charles University in Prague, Faculty of Mathematics and Physics,
      Malostranske nam. 25, 118 00 Prague 1, Czech Republic.},
      language = {eng},
      medline-pst = {epublish},
      owner = {hw5},
      pii = {752},
      pmid = {24231142},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.2390/biecoll-jib-2013-228}
    }
  • Go to document M. Walzer, D. Qi, G. Mayer, J. Uszkoreit, M. Eisenacher, T. Sachsenberg, F. Gonzalez-Galarza, J. Fan, C. Bessant, E. Deutsch, F. Reisinger, J. A. Vizcaíno, A. Medina-Aunon, J. P. Albar, O. Kohlbacher, and A. Jones, “The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics.,” Mol Cell Proteomics, vol. 12, iss. 8, pp. 2332-2340, 2013.
    [Bibtex]
    @ARTICLE{Walzer2013,
      author = {Walzer, Mathias and Qi, Da and Mayer, Gerhard and Uszkoreit, Julian and Eisenacher, Martin and Sachsenberg, Timo and Gonzalez-Galarza, Faviel F. and Fan, Jun and Bessant, Conrad and Deutsch, Eric W. and Reisinger, Florian and Vizcaíno, Juan Antonio and Medina-Aunon, J Alberto and Albar, Juan Pablo and Kohlbacher, Oliver and Jones, Andrew R.},
      title = {The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics.},
      journal = {Mol Cell Proteomics},
      year = {2013},
      volume = {12},
      pages = {2332--2340},
      number = {8},
      month = {Aug},
      abstract = {The range of heterogeneous approaches available for quantifying protein
      abundance via mass spectrometry (MS)(1) leads to considerable challenges
      in modeling, archiving, exchanging, or submitting experimental data
      sets as supplemental material to journals. To date, there has been
      no widely accepted format for capturing the evidence trail of how
      quantitative analysis has been performed by software, for transferring
      data between software packages, or for submitting to public databases.
      In the context of the Proteomics Standards Initiative, we have developed
      the mzQuantML data standard. The standard can represent quantitative
      data about regions in two-dimensional retention time versus mass/charge
      space (called features), peptides, and proteins and protein groups
      (where there is ambiguity regarding peptide-to-protein inference),
      and it offers limited support for small molecule (metabolomic) data.
      The format has structures for representing replicate MS runs, grouping
      of replicates (for example, as study variables), and capturing the
      parameters used by software packages to arrive at these values. The
      format has the capability to reference other standards such as mzML
      and mzIdentML, and thus the evidence trail for the MS workflow as
      a whole can now be described. Several software implementations are
      available, and we encourage other bioinformatics groups to use mzQuantML
      as an input, internal, or output format for quantitative software
      and for structuring local repositories. All project resources are
      available in the public domain from the HUPO Proteomics Standards
      Initiative http://www.psidev.info/mzquantml.},
      doi = {10.1074/mcp.O113.028506},
      institution = {Quantitative Biology Center and Department of Computer Science, Center
      for Bioinformatics, University of Tübingen, Sand 14, 72076 Tübingen,
      Germany.},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pii = {O113.028506},
      pmid = {23599424},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1074/mcp.O113.028506}
    }
  • Go to document H. Weisser, S. Nahnsen, J. Grossmann, L. Nilse, A. Quandt, H. Brauer, M. Sturm, E. Kenar, O. Kohlbacher, R. Aebersold, and L. Malmström, “An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics.,” J Proteome Res, 2013.
    [Bibtex]
    @ARTICLE{Weisser2013,
      author = {Weisser, Hendrik and Nahnsen, Sven and Grossmann, Jonas and Nilse, Lars and Quandt, Andreas and Brauer, Hendrik and Sturm, Marc and Kenar, Erhan and Kohlbacher, Oliver and Aebersold, Ruedi and Malmstr{\"o}m,
      Lars},
      title = {An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics.},
      journal = {J Proteome Res},
      year = {2013},
      month = {Feb},
      abstract = {We present a computational pipeline for the quantification of peptides
      and proteins in label-free LC-MS/MS data sets. The pipeline is composed
      of tools from the OpenMS software framework and is applicable to
      the processing of large experiments (50+ samples). We describe several
      enhancements that we have introduced to OpenMS to realize the implementation
      of this pipeline. They include new algorithms for centroiding of
      raw data, for feature detection, for the alignment of multiple related
      measurements, and a new tool for the calculation of peptide and protein
      abundances. Where possible, we compare the performance of the new
      algorithms to that of their established counterparts in OpenMS. We
      validate the pipeline on the basis of two small data sets that provide
      ground truths for the quantification. There, we also compare our
      results to those of MaxQuant and Progenesis LC-MS, two popular alternatives
      for the analysis of label-free data. We then show how our software
      can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.},
      doi = {10.1021/pr300992u},
      institution = {Department of Biology, Institute of Molecular Systems Biology, ETH
      Zürich , 8093 Zürich, Switzerland.},
      language = {eng},
      medline-pst = {aheadofprint},
      owner = {hw5},
      pmid = {23391308},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1021/pr300992u}
    }
  • Go to document A. Zerck, E. Nordhoff, H. Lehrach, and K. Reinert, “Optimal precursor ion selection for LC-MALDI MS/MS.,” BMC Bioinformatics, vol. 14, pp. 56, 2013.
    [Bibtex]
    @ARTICLE{Zerck2013,
      author = {Zerck, Alexandra and Nordhoff, Eckhard and Lehrach, Hans and Reinert, Knut},
      title = {Optimal precursor ion selection for LC-MALDI MS/MS.},
      journal = {BMC Bioinformatics},
      year = {2013},
      volume = {14},
      pages = {56},
      abstract = {Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics
      are often too complex to select every detected peptide signal for
      fragmentation by tandem mass spectrometry (MS/MS). Standard methods
      for precursor ion selection, commonly based on data dependent acquisition,
      select highly abundant peptide signals in each spectrum. However,
      these approaches produce redundant information and are biased towards
      high-abundance proteins.We present two algorithms for inclusion list
      creation that formulate precursor ion selection as an optimization
      problem. Given an LC-MS map, the first approach maximizes the number
      of selected precursors given constraints such as a limited number
      of acquisitions per RT fraction. Second, we introduce a protein sequence-based
      inclusion list that can be used to monitor proteins of interest.
      Given only the protein sequences, we create an inclusion list that
      optimally covers the whole protein set. Additionally, we propose
      an iterative precursor ion selection that aims at reducing the redundancy
      obtained with data dependent LC-MS/MS. We overcome the risk of erroneous
      assignments by including methods for retention time and proteotypicity
      predictions. We show that our method identifies a set of proteins
      requiring fewer precursors than standard approaches. Thus, it is
      well suited for precursor ion selection in experiments with limited
      sample amount or analysis time.We present three approaches to precursor
      ion selection with LC-MALDI MS/MS. Using a well-defined protein standard
      and a complex human cell lysate, we demonstrate that our methods
      outperform standard approaches. Our algorithms are implemented as
      part of OpenMS and are available under http://www.openms.de.},
      doi = {10.1186/1471-2105-14-56},
      institution = {Department of Vertebrate Genomics, Max Planck Institute for Molecular
      Genetics, Berlin, Germany. zerckatmolgendotmpgdotde},
      keywords = {Algorithms; Chromatography, Liquid, methods; Humans; Ions, chemistry;
      Peptides, analysis/chemistry; Proteins, analysis/chemistry; Proteomics,
      methods; Sequence Analysis, Protein; Spectrometry, Mass, Matrix-Assisted
      Laser Desorption-Ionization, methods; Tandem Mass Spectrometry, methods},
      language = {eng},
      medline-pst = {epublish},
      owner = {hw5},
      pii = {1471-2105-14-56},
      pmid = {23418672},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1186/1471-2105-14-56}
    }

2012

  • Go to document S. Nahnsen and O. Kohlbacher, “In silico design of targeted SRM-based experiments.,” BMC Bioinformatics, vol. 13 Suppl 16, pp. S8, 2012.
    [Bibtex]
    @ARTICLE{Nahnsen2012,
      author = {Nahnsen, Sven and Kohlbacher, Oliver},
      title = {In silico design of targeted SRM-based experiments.},
      journal = {BMC Bioinformatics},
      year = {2012},
      volume = {13 Suppl 16},
      pages = {S8},
      __markedentry = {[hw5:6]},
      abstract = {Selected reaction monitoring (SRM)-based proteomics approaches enable
      highly sensitive and reproducible assays for profiling of thousands
      of peptides in one experiment. The development of such assays involves
      the determination of retention time, detectability and fragmentation
      properties of peptides, followed by an optimal selection of transitions.
      If those properties have to be identified experimentally, the assay
      development becomes a time-consuming task. We introduce a computational
      framework for the optimal selection of transitions for a given set
      of proteins based on their sequence information alone or in conjunction
      with already existing transition databases. The presented method
      enables the rapid and fully automated initial development of assays
      for targeted proteomics. We introduce the relevant methods, report
      and discuss a step-wise and generic protocol and we also show that
      we can reach an ad hoc coverage of 80 \% of the targeted proteins.
      The presented algorithmic procedure is implemented in the open-source
      software package OpenMS/TOPP.},
      doi = {10.1186/1471-2105-13-S16-S8},
      institution = {Center for Bioinformatics, Quantitative Biology Center, and Department
      of Computer Science, University of Tübingen, Germany. svendotnahnsenatuni-tuebingendotde},
      keywords = {Algorithms; Computer Simulation; Humans; Mass Spectrometry, methods;
      Peptides, chemistry; Proteins, chemistry; Proteomics, methods; Research
      Design, statistics /&/ numerical data; Software},
      language = {eng},
      medline-pst = {ppublish},
      owner = {hw5},
      pii = {1471-2105-13-S16-S8},
      pmid = {23176520},
      timestamp = {2014.01.16},
      url = {http://dx.doi.org/10.1186/1471-2105-13-S16-S8}
    }
  • Go to document J. Junker, C. Bielow, A. Bertsch, M. Sturm, K. Reinert, and O. Kohlbacher, “TOPPAS: A graphical workflow editor for the analysis of high-throughput proteomics data,” J. Proteome Res., 2012.
    [Bibtex]
    @Article{TOPPAS-JPR,
      author = {Junker, Johannes and Bielow, Chris and Bertsch, Andreas and Sturm, Marc and Reinert, Knut and Kohlbacher, Oliver},
      title = {TOPPAS: A graphical workflow editor for the analysis of high-throughput proteomics data},
      year = {2012},
      abstract = {Mass spectrometry coupled to high-performance liquid chromatography (HPLC-MS) is evolving more quickly than ever. A wide range of different instrument types and experimental setups are commonly used. Modern instruments acquire huge amounts of data, thus requiring tools for an efficient and automated data analysis. Most existing software for analyzing HPLC-MS data is monolithic and tailored towards a specific application. A more flexible alternative consists in pipeline-based tool kits allowing the construction of custom analysis workflows from small building blocks, e.g., the Trans Proteomics Pipeline (TPP) or The OpenMS Proteomics Pipeline (TOPP). One drawback, however, is the hurdle of setting up complex workflows using command line tools. We present TOPPAS, The OpenMS Proteomics Pipeline ASsistant, a graphical user interface (GUI) for rapid composition of HPLC-MS analysis workflows. Workflow construction reduces to simple drag-and-drop of analysis tools and adding connections in between. Integration of external tools into these workflows is possible as well. Once workflows have been developed, they can be deployed in other workflow management systems or batch processing systems in a fully automated fashion. The implementation is portable and has been tested under Windows, Mac OS X, and Linux. TOPPAS is open-source software and available free of charge at http://www.OpenMS.de/TOPPAS.},
      journal = {J. Proteome Res.},
      pmid = {22583024},
      doi = {10.1021/pr300187f}
    }
  • Go to document J. Malmström, C. Karlsson, P. Nordenfelt, R. Ossola, H. Weisser, A. Quandt, K. Hansson, R. Aebersold, L. Malmström, and L. Björck, “Streptococcus pyogenes in human plasma: adaptive mechanisms analyzed by mass spectrometry-based proteomics.,” J. Biol. Chem., vol. 287, iss. 2, pp. 1415-1425, 2012.
    [Bibtex]
    @ARTICLE{Malmstroem2011,
     author = {Johan Malmstr{\"o}m and Christofer Karlsson and Pontus Nordenfelt and Reto Ossola and Hendrik Weisser and Andreas Quandt and Karin Hansson and Ruedi Aebersold and Lars Malmstr{\"o}m and Lars Bj{\"o}rck},
     title = {Streptococcus pyogenes in human plasma: adaptive mechanisms analyzed by mass spectrometry-based proteomics.},
     journal = {J. Biol. Chem.},
     year = {2012},
     volume = {287},
     pages = {1415--1425},
     number = {2},
     month = {Jan},
    abstract = {Streptococcus pyogenes is a major bacterial pathogen and a potent
      inducer of inflammation causing plasma leakage at the site of infection.
      A combination of label-free quantitative mass spectrometry-based
      proteomics strategies were used to measure how the intracellular
      proteome homeostasis of S. pyogenes is influenced by the presence
      of human plasma, identifying and quantifying 842 proteins. In plasma
      the bacterium modifies its production of 213 proteins, and the most
      pronounced change was the complete down-regulation of proteins required
      for fatty acid biosynthesis. Fatty acids are transported by albumin
      (HSA) in plasma. S. pyogenes expresses HSA-binding surface proteins,
      and HSA carrying fatty acids reduced the amount of fatty acid biosynthesis
      proteins to the same extent as plasma. The results clarify the function
      of HSA-binding proteins in S. pyogenes and underline the power of
      the quantitative mass spectrometry strategy used here to investigate
      bacterial adaptation to a given environment.},
     doi = {10.1074/jbc.M111.267674},
     file = {Malmstroem2011.pdf:Malmstroem2011.pdf:PDF},
     institution = {From the Department of Immunotechnology and.},
     language = {eng},
     medline-pst = {ppublish},
     owner = {hendrik},
     pii = {M111.267674},
     pmid = {22117078},
     timestamp = {2012.01.31},
     url = {http://dx.doi.org/10.1074/jbc.M111.267674}
    }
  • M. Trusch, K. Tillack, M. Kwiatkowski, A. Bertsch, R. Ahrends, O. Kohlbacher, R. Martin, M. Sospedra, and H. Schlüter, “Displacement chromatography as first separating step in online two-dimensional liquid chromatography coupled to mass spectrometry analysis of a complex protein sample – The proteome of neutrophils,” J. Chromatogr. A, vol. 1232, pp. 288-94, 2012.
    [Bibtex]
    @Article{DCNeutrophils,
      author = {Trusch, Maria and Tillack, Kati and Kwiatkowski, Marcel and Bertsch, Andreas and Ahrends, Robert and Kohlbacher, Oliver and Martin, Roland and Sospedra, Mirieia and Schl{\"u}ter, Hartmut},
      title = {Displacement chromatography as first separating step in online two-dimensional liquid chromatography coupled to mass spectrometry analysis of a complex protein sample - The proteome of neutrophils},
      year = {2012},
      abstract = {Displacement chromatography provides some advantages over elution chromatography such as the opportunity to enrich trace amounts of molecules and to elute molecules in highest concentrations achievable with liquid chromatography. In a previous study we demonstrated that displacement chromatography is a well-suited alternative to gradient elution in an offline two-dimensional (2D-)LC-MS approach for the analysis of proteomes. In this study we present a method for applying displacement chromatography in an online 2D-LC-MS system including a cation exchange (CEX) column and a reversed phase column. We circumvented the problem of determining the sample capacity of the CEX column by repeated injection (pulses) of sample aliquots monitored by an LC-MS analysis of each flow-through fraction of the CEX column. Elution of tryptic peptides from the CEX column was achieved by repeated injection (pulses) of the displacer spermine. Pulsed displacer injections offer the advantage through physical separation of preventing post-column mixing of already separated compounds. As a proof of principle we analyzed the cytosolic proteome of human neutrophils.},
      journal = {J. Chromatogr. A},
      volume = {1232},
      pages = {288-94},
      pmid = {22391494}
    }
  • A. Jones, M. Eisenacher, G. Mayer, O. Kohlbacher, J. Siepen, S. Hubbard, J. Selley, B. Searle, J. Shofstahl, S. Seymour, R. Julian, P. Binz, E. Deutsch, H. Hermjakob, F. Reisinger, J. Griss, J. A. Vizcaino, M. Chambers, A. Pizarro, and D. Creasy, “The mzIdentML data standard for mass spectrometry-based proteomics results,” Mol. Cell. Prot., 2012.
    [Bibtex]
    @Article{mzIdentML,
      author = {Jones, Andrew R and Eisenacher, Martin and Mayer, Gerhard and Kohlbacher, Oliver and Siepen, Jennifer and Hubbard, Simon J and Selley, Julian N and Searle, Brian C and Shofstahl, James and Seymour, Sean L and Julian, Randall and Binz, Pierre-Alain and Deutsch, Eric W and Hermjakob, Henning and Reisinger, Florian and Griss, Johannes and Vizcaino, Juan Antonio and Chambers, Matthew and Pizarro, Angel and Creasy, David},
      title = {The mzIdentML data standard for mass spectrometry-based proteomics results},
      year = {2012},
      URL = {http://www.mcponline.org/content/early/2012/02/27/mcp.M111.014381},
      abstract = {We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative (PSI). The format was developed by the PSI in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.},
      journal = {Mol. Cell. Prot.},
      pmid = {22375074}
    }
  • Go to document S. Aiche, K. Reinert, C. Schütte, D. Hildebrand, H. Schlüter, and T. Conrad, “Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs,” PLoS ONE, vol. 7, iss. 7, pp. e40656, 2012.
    [Bibtex]
    @article{Aiche:2012gt,
      author = {Aiche, Stephan and Reinert, Knut and Sch{\"u}tte, Christof and Hildebrand, Diana and Schl{\"u}ter, Hartmut and Conrad, Tim O F},
      title = {{Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs}},
      journal = {PLoS ONE},
      year = {2012},
      volume = {7},
      number = {7},
      pages = {e40656},
      month = jul,
      doi = {doi:10.1371/journal.pone.0040656}
    
    }

2011

  • A. Bertsch, C. Gröpl, K. Reinert, and O. Kohlbacher, “OpenMS and TOPP: Open Source Software for LC-MS Data Analysis,” Methods Mol. Biol., vol. 696, pp. 353-67, 2011.
    [Bibtex]
    @Article{MMB-TOPP-2011,
      author = {Bertsch, Andreas and Gr{\"o}pl, Clemens and Reinert, Knut and Kohlbacher, Oliver},
      title = {OpenMS and TOPP: Open Source Software for LC-MS Data Analysis},
      year = {2011},
      abstract = {Proteomics experiments based on state-of-the-art mass spectrometry produce vast amounts of data, which cannot be analyzed manually. Hence, software is needed which is able to analyze the data in an automated fashion. The need for robust and reusable software tools triggered the development of libraries implementing different algorithms for the various analysis steps. OpenMS is such a software library and provides a wealth of data structures and algorithms for the analysis of mass spectrometric data. For users unfamiliar with programming, TOPP ("The OpenMS Proteomics Pipeline") offers a wide range of already implemented tools sharing the same interface and designed for a specific analysis task each. TOPP thus makes the sophisticated algorithms of OpenMS accessible to nonprogrammers. The individual TOPP tools can be strung together into pipelines for analyzing mass spectrometry-based experiments starting from the raw output of the mass spectrometer. These analysis pipelines can be constructed using a graphical editor. Even complex analytical workflows can thus be analyzed with ease.},
      journal = {Methods Mol. Biol.},
      volume = {696},
      pages = {353-67},
      pmid = {21063960}
    }
  • C. Bielow, C. Gröpl, O. Kohlbacher, and K. Reinert, “Bioinformatics for Qualitative and Quantitative Proteomics,” , B. Mayer, Ed., Springer, 2011, pp. 1-19.
    [Bibtex]
    @Inbook{BookChapter-BIoinformaticsForOmics,
      author = {Bielow, Chris and Gr{\"o}pl, Clemens and Kohlbacher, Oliver and Reinert, Knut},
      title = {Bioinformatics for Qualitative and Quantitative Proteomics},
      year = {2011},
      URL = {http://www.springer.com/life+sciences/bioinformatics/book/978-1-61779-026-3},
      abstract = {Mass spectrometry is today a key analytical technique to elucidate the amount and content of proteins expressed in a certain cellular context. The degree of automation in proteomics has yet to reach that of genomic techniques, but even current technologies make a manual inspection of the data infeasible. This article addresses the key algorithmic problems bioinformaticians face when handling modern proteomic samples and shows common solutions to them. We provide examples on how algorithms can be combined to build relatively complex analysis pipelines, point out certain pitfalls and aspects worth considering and give a list of current state-of-the-art tools.},
      booktitle = {Bioinformatics for Omics Data: Methods and Protocols},
      editor = {Bernd Mayer},
      publisher = {Springer},
      series = {Methods in Molecular Biology, vol.719},
      chapter = {15},
      pages = {1-19}
    }
  • Go to document S. Nahnsen, A. Bertsch, J. Rahnenführer, A. Nordheim, and O. Kohlbacher, “Probabilistic Consensus Scoring Improves Tandem Mass Spectrometry Peptide Identification,” Journal of Proteome Research, vol. 10, iss. 8, pp. 3332-3343, 2011.
    [Bibtex]
    @article{doi:10.1021/pr2002879,
      author = {Nahnsen, Sven and Bertsch, Andreas and Rahnenf{\"u}hrer, J{\"o}rg and Nordheim, Alfred and Kohlbacher, Oliver},
      title = {Probabilistic Consensus Scoring Improves Tandem Mass Spectrometry Peptide Identification},
      journal = {Journal of Proteome Research},
      volume = {10},
      number = {8},
      pages = {3332-3343},
      year = {2011},
      doi = {10.1021/pr2002879},
    
      URL = {http://pubs.acs.org/doi/abs/10.1021/pr2002879},
      eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr2002879}
    }
  • Go to document S. Andreotti, G. Klau, and K. Reinert, “Antilope – A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, iss. 99, 2011.
    [Bibtex]
    @ARTICLE{Andreotti2011,
      author = {Sandro Andreotti and Gunnar W. Klau and Knut Reinert},
      title = {Antilope - A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem},
      journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
      year = {2011},
      number = {99},
      month = { },
      doi = {10.1109/TCBB.2011.59},
      issn = {1545-5963},
      note = {In Press}
    }
  • Go to document A. Bertsch, C. Gröpl, K. Reinert, and O. Kohlbacher, “OpenMS and TOPP: Open Source Software for LC-MS Data Analysis,” , J. Walker, M. Hamacher, M. Eisenacher, and Christian
    Stephan, Ed., Humana Press, 2011, vol. 696, pp. 353-367.
    [Bibtex]
    @INCOLLECTION{Bertsch2011,
      author = {Andreas Bertsch and Clemens Gr{\"o}pl and Knut Reinert and Oliver Kohlbacher},
      title = {OpenMS and TOPP: Open Source Software for LC-MS Data Analysis},
      booktitle = {Data Mining in Proteomics},
      publisher = {Humana Press},
      year = {2011},
      editor = {John M. Walker and Michael Hamacher and Martin Eisenacher and Christian
      Stephan},
      volume = {696},
      series = {Methods in Molecular Biology™},
      pages = {353-367},
      abstract = {Proteomics experiments based on state-of-the-art mass spectrometry
      produce vast amounts of data, which cannot be analyzed manually.
      Hence, software is needed which is able to analyze the data in an
      automated fashion. The need for robust and reusable software tools
      triggered the development of libraries implementing different algorithms
      for the various analysis steps. OpenMS is such a software library
      and provides a wealth of data structures and algorithms for the analysis
      of mass spectrometric data. For users unfamiliar with programming,
      TOPP ( The OpenMS Proteomics Pipeline ) offers a wide range of already
      implemented tools sharing the same interface and designed for a specific
      analysis task each. TOPP thus makes the sophisticated algorithms
      of OpenMS accessible to nonprogrammers. The individual TOPP tools
      can be strung together into pipelines for analyzing mass spectrometry-based
      experiments starting from the raw output of the mass spectrometer.
      These analysis pipelines can be constructed using a graphical editor.
      Even complex analytical workflows can thus be analyzed with ease.},
      added-at = {2011-02-21T12:55:05.000+0100},
      affiliation = {Division for Simulation of Biological Systems, WSI/ZBIT, Eberhard-Karls-Universität
      Tübingen, Tübingen, Germany},
      biburl = {http://www.bibsonomy.org/bibtex/2314c892b223eb2bafd6880fb2d9d6fde/openms},
      description = {SpringerLink - Abstract},
      doi = {10.1007/978-1-60761-987-1_23},
      interhash = {981b5768786efd021fa4bab55336a495},
      intrahash = {314c892b223eb2bafd6880fb2d9d6fde},
      isbn = {978-1-60761-987-1},
      keyword = {Life Sciences},
      keywords = {LC-MS OpenMS TOPP},
      url = {http://dx.doi.org/10.1007/978-1-60761-987-1_23}
    }
  • Go to document C. Bielow, S. Aiche, S. Andreotti, and K. Reinert, “MSSimulator: Simulation of Mass Spectrometry Data.,” Journal of Proteome Research, vol. 10, iss. 7, pp. 2922-2929, 2011.
    [Bibtex]
    @ARTICLE{Bielow2011b,
      author = {Chris Bielow and Stephan Aiche and Sandro Andreotti and Knut Reinert},
      title = {{MSSimulator}: Simulation of Mass Spectrometry Data.},
      year = {2011},
      volume = {10},
      pages = {2922--2929},
      number = {7},
      month = {Jul},
      abstract = {Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS)
      is commonly used to analyze the protein content of biological samples
      in large scale studies, enabling quantitation and identification
      of proteins and peptides using a wide range of experimental protocols,
      algorithms, and statistical models to analyze the data. Currently
      it is difficult to compare the plethora of algorithms for these tasks.
      So far, curated benchmark data exists for peptide identification
      algorithms but data that represents a ground truth for the evaluation
      of LC-MS data is limited. Hence there have been attempts to simulate
      such data in a controlled fashion to evaluate and compare algorithms.
      We present MSSimulator, a simulation software for LC-MS and LC-MS/MS
      experiments. Starting from a list of proteins from a FASTA file,
      the simulation will perform in-silico digestion, retention time prediction,
      ionization filtering, and raw signal simulation (including MS/MS),
      while providing many options to change the properties of the resulting
      data like elution profile shape, resolution and sampling rate. Several
      protocols for SILAC, iTRAQ or MS(E) are available, in addition to
      the usual label-free approach, making MSSimulator the most comprehensive
      simulator for LC-MS and LC-MS/MS data.},
      institution = {Institute of Computer Science, Department of Mathematics and Computer
      Science, Freie Universität Berlin , Berlin, Germany.},
      medline-pst = {ppublish},
      owner = {aiche},
      pmid = {21526843},
      journal = {Journal of Proteome Research},
      doi = {10.1021/pr200155f},
      eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr200155f},
      url = {http://pubs.acs.org/doi/abs/10.1021/pr200155f},
    }
  • C. Bielow, C. Gröpl, O. Kohlbacher, and K. Reinert, “Bioinformatics for Qualitative and Quantitative Proteomics,” , 1 ed., B. Mayer, Ed., Springer, 2011, pp. 1-19.
    [Bibtex]
    @INCOLLECTION{Bielow2011a,
      author = {Chris Bielow and Clemens Gr\"{o}pl and Oliver Kohlbacher and Knut Reinert},
      title = {Bioinformatics for Qualitative and Quantitative Proteomics},
      booktitle = {Bioinformatics for Omics Data: Methods and Protocols},
      publisher = {Springer},
      year = {2011},
      editor = {Bernd Mayer},
      series = {Methods in Molecular Biology, vol.719},
      chapter = {15},
      pages = {1-19},
      edition = {1},
      abstract = {Mass spectrometry is today a key analytical technique to elucidate
      the amount and content of proteins expressed in a certain cellular
      context. The degree of automation in proteomics has yet to reach
      that of genomic techniques, but even current technologies make a
      manual inspection of the data infeasible. This article addresses
      the key algorithmic problems bioinformaticians face when handling
      modern proteomic samples and shows common solutions to them. We provide
      examples on how algorithms can be combined to build relatively complex
      analysis pipelines, point out certain pitfalls and aspects worth
      considering and give a list of current state-of-the-art tools.},
      added-at = {2011-02-23T14:09:02.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/25f3db2b725e95c91cc31c38b0a4a9c7a/openms},
      interhash = {5b62ed21622b10bafba7bcd5b425e035},
      intrahash = {5f3db2b725e95c91cc31c38b0a4a9c7a},
      keywords = {Proteomics Qualitative Quantitative},
      url = {http://www.springer.com/life+sciences/bioinformatics/book/978-1-61779-026-3}
    }

2010

  • L. Nilse, H. Plattfaut, S. Sass, D. Trudgian, and O. Kohlbacher, “SILACAnalyzer-a tool for quantitation of stable isotope labelled proteomics data,” , 2010.
    [Bibtex]
    @article{nilsesilacanalyzer,
      title={SILACAnalyzer-a tool for quantitation of stable isotope labelled proteomics data},
      author={Nilse, Lars and Plattfaut, Holger and Sass, Steffen and Trudgian, David and Kohlbacher, Oliver},
      year = {2010}
    }
  • Go to document A. Bertsch, S. Jung, A. Zerck, N. Pfeifer, S. Nahnsen, C. Henneges, A. Nordheim, and O. Kohlbacher, “Optimal de novo Design of MRM Experiments for Rapid Assay Development in Targeted Proteomics,” Journal of Proteome Research, vol. 9, iss. 5, pp. 2696-2704, 2010.
    [Bibtex]
    @ARTICLE{Bertsch2010,
      author = {Andreas Bertsch and Stephan Jung and Alexandra Zerck and Nico Pfeifer and Sven Nahnsen and Carsten Henneges and Alfred Nordheim and Oliver Kohlbacher},
      title = {Optimal de novo Design of MRM Experiments for Rapid Assay Development in Targeted Proteomics},
      journal = {Journal of Proteome Research},
      year = {2010},
      volume = {9},
      pages = {2696-2704},
      number = {5},
      note = {PMID: 20201589},
      abstract = { Targeted proteomic approaches such as multiple reaction monitoring
      (MRM) overcome problems associated with classical shotgun mass spectrometry
      experiments. Developing MRM quantitation assays can be time consuming,
      because relevant peptide representatives of the proteins must be
      found and their retention time and the product ions must be determined.
      Given the transitions, hundreds to thousands of them can be scheduled
      into one experiment run. However, it is difficult to select which
      of the transitions should be included into a measurement. We present
      a novel algorithm that allows the construction of MRM assays from
      the sequence of the targeted proteins alone. This enables the rapid
      development of targeted MRM experiments without large libraries of
      transitions or peptide spectra. The approach relies on combinatorial
      optimization in combination with machine learning techniques to predict
      proteotypicity, retention time, and fragmentation of peptides. The
      resulting potential transitions are scheduled optimally by solving
      an integer linear program. We demonstrate that fully automated construction
      of MRM experiments from protein sequences alone is possible and over
      80% coverage of the targeted proteins can be achieved without further
      optimization of the assay. },
      added-at = {2011-02-23T13:51:53.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/29a602e681e778892bb15a4c38a1ea1ec/openms},
      description = {Optimal de novo Design of MRM Experiments for Rapid Assay Development
      in Targeted Proteomics - Journal of Proteome Research (ACS Publications)},
      doi = {10.1021/pr1001803},
      eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr1001803},
      interhash = {0b925c52a50084195ba5517eeae50e5e},
      intrahash = {9a602e681e778892bb15a4c38a1ea1ec},
      keywords = {ILP MRM OpenMS SRM prediction},
      url = {http://pubs.acs.org/doi/abs/10.1021/pr1001803}
    }
  • Go to document C. Bielow, S. Ruzek, C. Huber, and K. Reinert, “Optimal Decharging and Clustering of Charge Ladders Generated in ESI-MS,” Journal of Proteome Research, vol. 9, iss. 5, pp. 2688-2695, 2010.
    [Bibtex]
    @ARTICLE{Bielow2010,
      author = {Chris Bielow and Silke Ruzek and Christian G. Huber and Knut Reinert},
      title = {Optimal Decharging and Clustering of Charge Ladders Generated in ESI-MS},
      journal = {Journal of Proteome Research},
      year = {2010},
      volume = {9},
      pages = {2688-2695},
      number = {5},
      note = {PMID: 20201597},
      abstract = { In electrospray ionization mass spectrometry (ESI-MS), peptide
      and protein ions are usually observed in multiple charge states.
      Moreover, adduction of the multiply charged species with other ions
      frequently results in quite complex signal patterns for a single
      analyte, which significantly complicates the derivation of quantitative
      information from the mass spectra. Labeling strategies targeting
      the MS1 level further aggravate this situation, as multiple biological
      states such as healthy or diseased must be represented simultaneously.
      We developed an integer linear programming (ILP) approach, which
      can cluster signals belonging to the same peptide or protein. The
      algorithm is general in that it models all possible shifts of signals
      along the m/z axis. These shifts can be induced by different charge
      states of the compound, the presence of adducts (e.g., potassium
      or sodium), and/or a fixed mass label (e.g., from ICAT or nicotinic
      acid labeling), or any combination of the above. We show that our
      approach can be used to infer more features in labeled data sets,
      correct wrong charge assignments even in high-resolution MS, improve
      mass precision, and cluster charged species in different charge states
      and several adduct types. },
      added-at = {2011-02-22T11:13:58.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2b506619f53981eff01ff2553698ba23d/openms},
      description = {Optimal Decharging and Clustering of Charge Ladders Generated in ESIMS
      - Journal of Proteome Research (ACS Publications)},
      doi = {10.1021/pr100177k},
      eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr100177k},
      interhash = {1deb9545a59f3b5a3341fe36bd2c4e16},
      intrahash = {b506619f53981eff01ff2553698ba23d},
      keywords = {ESI decharging mass spectrometry},
      url = {http://pubs.acs.org/doi/abs/10.1021/pr100177k}
    }
  • Go to document S. Gesing, J. van Hemert, J. Koetsier, A. Bertsch, and O. Kohlbacher, “TOPP goes Rapid,” IEEE International Symposium on Cluster Computing and the Grid, pp. 598-599, 2010.
    [Bibtex]
    @ARTICLE{Gesing2010,
      author = {Sandra Gesing and Jano van Hemert and Jos Koetsier and Andreas Bertsch and Oliver Kohlbacher},
      title = {TOPP goes Rapid},
      journal = {IEEE International Symposium on Cluster Computing and the Grid},
      year = {2010},
      volume = {0},
      pages = {598-599},
      added-at = {2011-02-23T13:49:47.000+0100},
      address = {Los Alamitos, CA, USA},
      biburl = {http://www.bibsonomy.org/bibtex/2d10044fe02289d5fa66991af2d892bd0/openms},
      description = {TOPP goes Rapid},
      doi = {10.1109/CCGRID.2010.39,},
      interhash = {43470057665f3d94b32d33fde1e4ede7},
      intrahash = {d10044fe02289d5fa66991af2d892bd0},
      isbn = {978-0-7695-4039-9},
      keywords = {computing grid mass portal spectrometry},
      publisher = {IEEE Computer Society},
      url = {http://www.computer.org/portal/web/csdl/doi/10.1109/CCGRID.2010.39}
    }
  • Go to document R. Hussong and A. Hildebrandt, “Signal Processing in Proteomics,” , J. Walker, S. Hubbard, and A. Jones, Ed., Humana Press, 2010, vol. 604, pp. 145-161.
    [Bibtex]
    @INCOLLECTION{Hussong2010,
      author = {Rene Hussong and Andreas Hildebrandt},
      title = {Signal Processing in Proteomics},
      booktitle = {Proteome Bioinformatics},
      publisher = {Humana Press},
      year = {2010},
      editor = {John M. Walker and Simon J. Hubbard and Andrew R. Jones},
      volume = {604},
      series = {Methods in Molecular Biology™},
      pages = {145-161},
      abstract = {Computational proteomics applications are often imagined as a pipeline,
      where information is processed in each stage before it flows to the
      next one. Independent of the type of application, the first stage
      invariably consists of obtaining the raw mass spectrometric data
      from the spectrometer and preparing it for use in the later stages
      by enhancing the signal of interest while suppressing spurious components.
      Numerous approaches for preprocessing MS data have been described
      in the literature. In this chapter, we will describe both, standard
      techniques originating from classical signal and image processing,
      and novel computational approaches specifically tailored to the analysis
      of MS data sets. We will focus on low level signal processing tasks
      such as baseline reduction, denoising, and feature detection.},
      added-at = {2011-02-21T12:55:25.000+0100},
      affiliation = {Center for Bioinformatics, Saarland University, Saarbrücken, Germany},
      biburl = {http://www.bibsonomy.org/bibtex/28e4a953302f1c093e60d44c415878d23/openms},
      description = {SpringerLink - Abstract},
      doi = {10.1007/978-1-60761-444-9_11},
      interhash = {af425c0c59a837b67757dc160f0df2ab},
      intrahash = {8e4a953302f1c093e60d44c415878d23},
      isbn = {978-1-60761-444-9},
      keyword = {Life Sciences},
      keywords = {"Signal Processing" Proteomics},
      url = {http://dx.doi.org/10.1007/978-1-60761-444-9_11}
    }
  • Go to document K. Reinert and O. Kohlbacher, “OpenMS and TOPP: Open Source Software for LC-MS Data Analysis,” , J. Walker, S. Hubbard, and A. Jones, Ed., Humana Press, 2010, vol. 604, pp. 201-211.
    [Bibtex]
    @INCOLLECTION{Reinert2010,
      author = {Knut Reinert and Oliver Kohlbacher},
      title = {OpenMS and TOPP: Open Source Software for LC-MS Data Analysis},
      booktitle = {Proteome Bioinformatics},
      publisher = {Humana Press},
      year = {2010},
      editor = {John M. Walker and Simon J. Hubbard and Andrew R. Jones},
      volume = {604},
      series = {Methods in Molecular Biology™},
      pages = {201-211},
      abstract = {The automatic analysis of mass spectrometry data is becoming more
      and more important since increasingly larger datasets are readily
      available that cannot be evaluated manually. This has triggered the
      development of several open-source software libraries for the automatic
      analysis of such data. Among those is OpenMS together with TOPP (The
      OpenMS Proteomics Pipeline). OpenMS is a C++ library for rapid prototyping
      of complex algorithms for the analysis of mass spectrometry data.
      Based on the OpenMS library, TOPP provides a collection of tools
      for the most important tasks in proteomics analysis. The tight coupling
      of OpenMS and TOPP makes it easy to extend TOPP by adding new tools
      to the OpenMS library. We describe the overall concepts behind the
      software and illustrate its use with several examples.},
      added-at = {2011-02-21T12:55:40.000+0100},
      affiliation = {Department Computer Science and Mathematics, Free University of Berlin,
      Berlin, Germany},
      biburl = {http://www.bibsonomy.org/bibtex/2aa337e8c016cd1773d5cfb6ec9a11e47/openms},
      description = {SpringerLink - Abstract},
      doi = {10.1007/978-1-60761-444-9_14},
      interhash = {2b7cafec7e3869cc4911421b27e3c68c},
      intrahash = {aa337e8c016cd1773d5cfb6ec9a11e47},
      isbn = {978-1-60761-444-9},
      keyword = {Life Sciences},
      keywords = {LC-MS OpenMS Prote TOPP},
      url = {http://dx.doi.org/10.1007/978-1-60761-444-9_14}
    }
  • Go to document M. Trusch, A. Böhlick, D. Hildebrand, B. Lichtner, A. Bertsch, O. Kohlbacher, S. Bachmann, and H. Schlüter, “Application of displacement chromatography for the analysis of a lipid raft proteome,” Journal of Chromatography B, vol. 878, iss. 3-4, pp. 309-314, 2010.
    [Bibtex]
    @ARTICLE{Trusch2010,
      author = {Maria Trusch and Alexandra B{\"o}hlick and Diana Hildebrand and Bj{\"o}rn Lichtner and Andreas Bertsch and Oliver Kohlbacher and Sebastian Bachmann and Hartmut Schl{\"u}ter},
      title = {Application of displacement chromatography for the analysis of a lipid raft proteome},
      journal = {Journal of Chromatography B},
      year = {2010},
      volume = {878},
      pages = {309 - 314},
      number = {3-4},
      abstract = {Defining membrane proteomes is fundamental to understand the role
      of membrane proteins in biological processes and to find new targets
      for drug development. Usually multidimensional chromatography using
      step or gradient elution is applied for the separation of tryptic
      peptides of membrane proteins prior to their mass spectrometric analysis.
      Displacement chromatography (DC) offers several advantages that are
      helpful for proteome analysis. However, DC has so far been applied
      for proteomic investigations only in few cases. In this study we
      therefore applied DC in a multidimensional LC-MS approach for the
      separation and identification of membrane proteins located in cholesterol-enriched
      membrane microdomains (lipid rafts) obtained from rat kidney by density
      gradient centrifugation. The tryptic peptides were separated on a
      cation-exchange column in the displacement mode with spermine used
      as displacer. Fractions obtained from DC were analyzed using an HPLC-chip
      system coupled to an electrospray-ionization ion-trap mass spectrometer.
      This procedure yielded more than 400 highly significant peptide spectrum
      matches and led to the identification of more than 140 reliable protein
      hits within an established rat kidney lipid raft proteome. The majority
      of identified proteins were membrane proteins. In sum, our results
      demonstrate that DC is a suitable alternative to gradient elution
      separations for the identification of proteins via a multidimensional
      LC-MS approach.},
      added-at = {2011-02-16T17:14:26.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2cbc9c0ced5d9af75ae39664e8fafaa6e/openms},
      description = {ScienceDirect - Journal of Chromatography B : Application of displacement
      chromatography for the analysis of a lipid raft proteome},
      doi = {10.1016/j.jchromb.2009.11.035},
      interhash = {6a8ad8bfb94c6c7c292e721e8dec1947},
      intrahash = {cbc9c0ced5d9af75ae39664e8fafaa6e},
      issn = {1570-0232},
      keywords = {chromatography displacement},
      url = {http://www.sciencedirect.com/science/article/B6X0P-4XT3HR1-1/2/a4ec591b07e202e74b63bd457f1aea7c}
    }

2009

  • Go to document A. Bertsch, A. Leinenbach, A. Pervukhin, M. Lubeck, R. Hartmer, C. Baessmann, Y. A. Elnakady, R. Müller, S. Böcker, C. Huber, and O. Kohlbacher, “De novo peptide sequencing by tandem MS using complementary CID and electron transfer dissociation,” ELECTROPHORESIS, vol. 30, iss. 21, pp. 3736-3747, 2009.
    [Bibtex]
    @ARTICLE{Bertsch2009,
      author = {Andreas Bertsch and Andreas Leinenbach and Anton Pervukhin and Markus Lubeck and Ralf Hartmer and Carsten Baessmann and Yasser Abbas Elnakady and Rolf M{\"u}ller and Sebastian B{\"o}cker and Christian G. Huber and Oliver Kohlbacher},
      title = {De novo peptide sequencing by tandem MS using complementary CID and electron transfer dissociation},
      journal = {ELECTROPHORESIS},
      year = {2009},
      volume = {30},
      pages = {3736--3747},
      number = {21},
      abstract = {Abstract 10.1002/elps.200900332.abs De novo sequencing of peptides
      using tandem MS is difficult due to missing fragment ions in the
      spectra commonly obtained after CID of peptide precursor ions. Complementing
      CID spectra with spectra obtained in an ion-trap mass spectrometer
      upon electron transfer dissociation (ETD) significantly increases
      the sequence coverage with diagnostic ions. In the de novo sequencing
      algorithm CompNovo presented here, a divide-and-conquer approach
      was combined with an efficient mass decomposition algorithm to exploit
      the complementary information contained in CID and ETD spectra. After
      optimizing the parameters for the algorithm on a well-defined training
      data set obtained for peptides from nine known proteins, the CompNovo
      algorithm was applied to the de novo sequencing of peptides derived
      from a whole protein extract of Sorangium cellulosum bacteria. To
      2406 pairs of CID and ETD spectra contained in this data set, 675
      fully correct sequences were assigned, which represent a success
      rate of 28.1%. It is shown that the CompNovo algorithm yields significantly
      improved sequencing accuracy as compared with published approaches
      using only CID spectra or combined CID and ETD spectra.},
      added-at = {2011-02-21T11:32:33.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/22b7fe8c1a1d1701d65bcd548ab9e5cb6/openms},
      description = {De novo peptide sequencing by tandem MS using complementary CID and
      electron transfer dissociation - Bertsch - 2009 - ELECTROPHORESIS
      - Wiley Online Library},
      doi = {10.1002/elps.200900332},
      interhash = {5cfaa5223d6eaec105a9f708596746ed},
      intrahash = {2b7fe8c1a1d1701d65bcd548ab9e5cb6},
      issn = {15222683},
      keywords = {MS OpenMS TOPP denovo tandem},
      publisher = {WILEY-VCH Verlag},
      url = {http://dx.doi.org/10.1002/elps.200900332}
    }
  • Go to document R. Hussong, B. Gregorius, A. Tholey, and A. Hildebrandt, “Highly accelerated feature detection in proteomics data sets using modern graphics processing units,” Bioinformatics, vol. 25, iss. 15, pp. 1937-1943, 2009.
    [Bibtex]
    @ARTICLE{Hussong2009,
      author = {Rene Hussong and Barbara Gregorius and Andreas Tholey and Andreas Hildebrandt},
      title = {Highly accelerated feature detection in proteomics data sets using modern graphics processing units},
      journal = {Bioinformatics},
      year = {2009},
      volume = {25},
      pages = {1937-1943},
      number = {15},
      month = aug,
      abstract = {Mass spectrometry (MS) is one of the most important techniques for
      high-throughput analysis in proteomics research. Due to the large
      number of different proteins and their post-translationally modified
      variants, the amount of data generated by a single wet-lab MS experiment
      can easily exceed several gigabytes. Hence, the time necessary to
      analyze and interpret the measured data is often significantly larger
      than the time spent on sample preparation and the wet-lab experiment
      itself. Since the automated analysis of this data is hampered by
      noise and baseline artifacts, more sophisticated computational techniques
      are required to handle the recorded mass spectra. Obviously, there
      is a clear tradeoff between performance and quality of the analysis,
      which is currently one of the most challenging problems in computational
      proteomics.Using modern graphics processing units (GPUs), we implemented
      a feature finding algorithm based on a hand-tailored adaptive wavelet
      transform that drastically reduces the computation time. A further
      speedup can be achieved exploiting the multi-core architecture of
      current computing devices, which leads to up to an approximately
      200-fold speed-up in our computational experiments. In addition,
      we will demonstrate that several approximations necessary on the
      CPU to keep run times bearable, become obsolete on the GPU, yielding
      not only faster, but also improved results.An open source implementation
      of the CUDA-based algorithm is available via the software framework
      OpenMS (http://www.openms.de).Supplementary data are available at
      Bioinformatics online.},
      added-at = {2011-02-21T11:30:22.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2532e0011520a028de2d0ddf0f2d60dae/openms},
      description = {Highly accelerated feature detection in proteomics... [Bioinformatics.
      2009] - PubMed result},
      doi = {10.1093/bioinformatics/btp294},
      interhash = {b0f3a42cc234baed2e01e61b24d97dc4},
      intrahash = {532e0011520a028de2d0ddf0f2d60dae},
      keywords = {OpenMS TOPP feature finder},
      pmid = {19447788},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/19447788?dopt=Abstract}
    }
  • S. Jung, S. Pengelley, A. Bertsch, A. Velic, K. Krug, O. Kohlbacher, and B. Macek, “SILAC-labeled cell/tissue lysates as a generic source of proteotypic peptides in multiple reaction monitoring analyses,” in n proceedings of 57th ASMS Conference on Mass Spectrometry, 2009.
    [Bibtex]
    @INPROCEEDINGS{Jung2009,
      author = {Stephan Jung and Stuart Pengelley and Andreas Bertsch and Ana Velic and Karsten Krug and Oliver Kohlbacher and Boris Macek},
      title = {SILAC-labeled cell/tissue lysates as a generic source of proteotypic peptides in multiple reaction monitoring analyses},
      booktitle = {n proceedings of 57th ASMS Conference on Mass Spectrometry},
      year = {2009},
      added-at = {2011-02-21T11:09:58.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2944d6b7b251cdfdd28aa19dcdc1069d7/openms},
      interhash = {5fb30227e418cfc9d3b6bfce6b6f1cfa},
      intrahash = {944d6b7b251cdfdd28aa19dcdc1069d7},
      keywords = {MRM OpenMS SILAC}
    }
  • S. Nahnsen, A. Bertsch, A. Nordheim, and O. Kohlbacher, “Novel probability-based consensus scoring improves identification rates in Tandem Mass spectrometry-based peptide identification ,” in In proceedings of 57th ASMS Conference on Mass Spectrometry, Philadelphia, PA, 2009.
    [Bibtex]
    @INPROCEEDINGS{Nahnsen2009,
      author = {Sven Nahnsen and Andreas Bertsch and Alfred Nordheim and Oliver Kohlbacher},
      title = {Novel probability-based consensus scoring improves identification rates in Tandem Mass spectrometry-based peptide identification },
      booktitle = {In proceedings of 57th ASMS Conference on Mass Spectrometry},
      year = {2009},
      address = {Philadelphia, PA},
      added-at = {2011-02-21T11:08:15.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/20cd8685fc642208a91a54550dd891d69/openms},
      interhash = {afd97dfd5a48a52d413fec9336861607},
      intrahash = {0cd8685fc642208a91a54550dd891d69},
      keywords = {consensus id}
    }
  • Go to document M. Sturm and O. Kohlbacher, “TOPPView: An Open-Source Viewer for Mass Spectrometry Data,” Journal of Proteome Research, vol. 8, iss. 7, pp. 3760-3763, 2009.
    [Bibtex]
    @ARTICLE{Sturm2009,
      author = {Marc Sturm and Oliver Kohlbacher},
      title = {TOPPView: An Open-Source Viewer for Mass Spectrometry Data},
      journal = {Journal of Proteome Research},
      year = {2009},
      volume = {8},
      pages = {3760-3763},
      number = {7},
      note = {PMID: 19425593},
      abstract = { Visualization of complex mass spectrometric data sets is becoming
      increasingly important in proteomics and metabolomics. We present
      TOPPView, an integrated data visualization and analysis tool for
      mass spectrometric data sets. TOPPView allows the visualization and
      comparison of individual mass spectra, two-dimensional LC-MS data
      sets and their accompanying metadata. By supporting standardized
      XML-based data exchange formats, data import is possible from any
      type of mass spectrometer. The integrated analysis tools of the OpenMS
      Proteomics Pipeline (TOPP) allow efficient data analysis from within
      TOPPView through a convenient graphical user interface. TOPPView
      runs on all major operating systems and is available free of charge
      under an open-source license at http://www.openms.de . },
      added-at = {2011-02-21T11:32:23.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2113906b33b9978a9930507c2e1a87771/openms},
      description = {TOPPView: An Open-Source Viewer for Mass Spectrometry Data - Journal
      of Proteome Research (ACS Publications)},
      doi = {10.1021/pr900171m},
      eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr900171m},
      interhash = {97587e840dd442e6cf54604df45d46c6},
      intrahash = {113906b33b9978a9930507c2e1a87771},
      keywords = {OpenMS TOPP TOPPView},
      url = {http://pubs.acs.org/doi/abs/10.1021/pr900171m}
    }

2008

  • H. Schlüter, M. Trusch, D. Hildebrand, A. Schulz, W. Shun, A. Böhlick, S. Bachmann, and A. Bertsch, “Identifizierung von Proteinen mit der HPLC-Chip-Massenspektrometrie. Hochauflösende LC-MS mit dem HPLC-Chip-System,” GIT, iss. 03, pp. 254, 2008.
    [Bibtex]
    @ARTICLE{Schlueter2008,
      author = {Hartmut Schl{\"u}ter and Maria Trusch and Diana Hildebrand and Anna Schulz and Wang Shun and Alexandra B{\"o}hlick and Sebastian Bachmann and Andreas Bertsch},
      title = {Identifizierung von Proteinen mit der HPLC-Chip-Massenspektrometrie. Hochauflösende LC-MS mit dem HPLC-Chip-System},
      journal = {GIT},
      year = {2008},
      pages = {254},
      number = {03},
      added-at = {2011-02-21T11:15:13.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2bb4fa5414589c57bceffa6ad6267c920/openms},
      interhash = {02ee535a9771eea8e836e5cb7002055f},
      intrahash = {bb4fa5414589c57bceffa6ad6267c920},
      keywords = {HPLC}
    }
  • Go to document O. Schulz-Trieglaff, N. Pfeifer, C. Gröpl, O. Kohlbacher, and K. Reinert, “LC-MSsim–a simulation software for liquid chromatography mass spectrometry data,” BMC Bioinformatics, vol. 9, pp. 423-423, 2008.
    [Bibtex]
    @ARTICLE{Schulz-Trieglaff2008,
      author = {Ole Schulz-Trieglaff and Nico Pfeifer and Clemens Gr{\"o}pl and Oliver Kohlbacher and Knut Reinert},
      title = {LC-MSsim--a simulation software for liquid chromatography mass spectrometry data},
      journal = {BMC Bioinformatics},
      year = {2008},
      volume = {9},
      pages = {423-423},
      abstract = {Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly
      used to analyze the protein content of biological samples in large
      scale studies. The data resulting from an LC-MS experiment is huge,
      highly complex and noisy. Accordingly, it has sparked new developments
      in Bioinformatics, especially in the fields of algorithm development,
      statistics and software engineering. In a quantitative label-free
      mass spectrometry experiment, crucial steps are the detection of
      peptide features in the mass spectra and the alignment of samples
      by correcting for shifts in retention time. At the moment, it is
      difficult to compare the plethora of algorithms for these tasks.
      So far, curated benchmark data exists only for peptide identification
      algorithms but no data that represents a ground truth for the evaluation
      of feature detection, alignment and filtering algorithms.We present
      LC-MSsim, a simulation software for LC-ESI-MS experiments. It simulates
      ESI spectra on the MS level. It reads a list of proteins from a FASTA
      file and digests the protein mixture using a user-defined enzyme.
      The software creates an LC-MS data set using a predictor for the
      retention time of the peptides and a model for peak shapes and elution
      profiles of the mass spectral peaks. Our software also offers the
      possibility to add contaminants, to change the background noise level
      and includes a model for the detectability of peptides in mass spectra.
      After the simulation, LC-MSsim writes the simulated data to mzData,
      a public XML format. The software also stores the positions (monoisotopic
      m/z and retention time) and ion counts of the simulated ions in separate
      files.LC-MSsim generates simulated LC-MS data sets and incorporates
      models for peak shapes and contaminations. Algorithm developers can
      match the results of feature detection and alignment algorithms against
      the simulated ion lists and meaningful error rates can be computed.
      We anticipate that LC-MSsim will be useful to the wider community
      to perform benchmark studies and comparisons between computational
      tools.},
      added-at = {2011-02-23T13:56:11.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2f3f611ece863518667fc27df56ad881e/openms},
      description = {LC-MSsim--a simulation software for liquid chromat... [BMC Bioinformatics.
      2008] - PubMed result},
      doi = {10.1186/1471-2105-9-423},
      interhash = {ddb7fbc1e914d99d0a10a3a1443f5b27},
      intrahash = {f3f611ece863518667fc27df56ad881e},
      keywords = {LC-MS OpenMS simulation},
      pmid = {18842122},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/18842122}
    }
  • Go to document M. Sturm, A. Bertsch, C. Gröpl, A. Hildebrand, R. Hussong, E. Lange, N. Pfeifer, O. Schulz-Trieglaf, A. Zerck, K. Reinert, and O. Kohlbacher, “OpenMS — An open-source software framework for mass spectrometry,” BMC Bioinformatics, vol. 9, pp. 1-11, 2008.
    [Bibtex]
    @ARTICLE{Sturm2008,
      author = {Marc Sturm and Andreas Bertsch and Clemens Gr{\"o}pl and Andreas Hildebrand and Rene Hussong and Eva Lange and Nico Pfeifer and Ole Schulz-Trieglaf and Alexandra Zerck and Knut Reinert and Oliver Kohlbacher},
      title = {OpenMS -- An open-source software framework for mass spectrometry},
      journal = {BMC Bioinformatics},
      year = {2008},
      volume = {9},
      pages = {1-11},
      abstract = {Background  Mass spectrometry is an essential analytical technique
      for high-throughput analysis in proteomics and metabolomics. The
      development of new separation techniques, precise mass analyzers
      and experimental protocols is a very active field of research. This
      leads to more complex experimental setups yielding ever increasing
      amounts of data. Consequently, analysis of the data is currently
      often the bottleneck for experimental studies. Although software
      tools for many data analysis tasks are available today, they are
      often hard to combine with each other or not flexible enough to allow
      for rapid prototyping of a new analysis workflow. Results  We present
      OpenMS, a software framework for rapid application development in
      mass spectrometry. OpenMS has been designed to be portable, easy-to-use
      and robust while offering a rich functionality ranging from basic
      data structures to sophisticated algorithms for data analysis. This
      has already been demonstrated in several studies. Conclusion  OpenMS
      is available under the Lesser GNU Public License (LGPL) from the
      project website at http://www.openms.de.},
      added-at = {2011-02-21T11:34:37.000+0100},
      affiliation = {Center for Bioinformatics, Eberhard Karls University Tübingen, Sand
      14, 72076 Tübingen, Germany},
      biburl = {http://www.bibsonomy.org/bibtex/28e0b930b4cc876822d8c5e4d5bde6379/openms},
      description = {SpringerLink - BMC Bioinformatics, Volume 9, Number 1},
      doi = {10.1186/1471-2105-9-163},
      interhash = {07e73cf8c4121d92089d6cf33c7f94f8},
      intrahash = {8e0b930b4cc876822d8c5e4d5bde6379},
      issue = {1},
      keyword = {Algorithms},
      keywords = {LC-MS OpenMS Proteomics},
      publisher = {BioMed Central},
      url = {http://dx.doi.org/10.1186/1471-2105-9-163}
    }

2007

  • Go to document R. Hussong, A. Tholey, and A. Hildebrandt, “Efficient Analysis of Mass Spectrometry Data Using the Isotope Wavelet,” AIP Conference Proceedings, vol. 940, iss. 1, pp. 139-149, 2007.
    [Bibtex]
    @ARTICLE{Hussong2007,
      author = {Rene Hussong and Andreas Tholey and Andreas Hildebrandt},
      title = {Efficient Analysis of Mass Spectrometry Data Using the Isotope Wavelet},
      journal = {AIP Conference Proceedings},
      year = {2007},
      volume = {940},
      pages = {139-149},
      number = {1},
      added-at = {2011-02-21T11:34:02.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/27e7eedf8c113e5e3bade150b4406313a/openms},
      description = {Efficient Analysis of Mass Spectrometry Data Using the Isotope Wavelet},
      doi = {10.1063/1.2793396},
      editor = {Arno P. J. M. Siebes and Michael R. Berthold and Robert C. Glen and
      Ad J. Feelders},
      interhash = {3c6d0e75d6e4f3cf5f683be1aac586ff},
      intrahash = {7e7eedf8c113e5e3bade150b4406313a},
      keywords = {mass spec},
      publisher = {AIP},
      url = {http://link.aip.org/link/?APC/940/139/1}
    }
  • Go to document O. Kohlbacher, K. Reinert, C. Gröpl, E. Lange, N. Pfeifer, O. Schulz-Trieglaff, and M. Sturm, “TOPP–the OpenMS proteomics pipeline,” Bioinformatics, vol. 23, iss. 2, pp. 191-197, 2007.
    [Bibtex]
    @ARTICLE{Kohlbacher2007,
      author = {Oliver Kohlbacher and Knut Reinert and Clemens Gr{\"o}pl and Eva Lange and Nico Pfeifer and Ole Schulz-Trieglaff and Marc Sturm},
      title = {TOPP--the OpenMS proteomics pipeline},
      journal = {Bioinformatics},
      year = {2007},
      volume = {23},
      pages = {191-197},
      number = {2},
      month = jan,
      abstract = {Experimental techniques in proteomics have seen rapid development
      over the last few years. Volume and complexity of the data have both
      been growing at a similar rate. Accordingly, data management and
      analysis are one of the major challenges in proteomics. Flexible
      algorithms are required to handle changing experimental setups and
      to assist in developing and validating new methods. In order to facilitate
      these studies, it would be desirable to have a flexible 'toolbox'
      of versatile and user-friendly applications allowing for rapid construction
      of computational workflows in proteomics.We describe a set of tools
      for proteomics data analysis-TOPP, The OpenMS Proteomics Pipeline.
      TOPP provides a set of computational tools which can be easily combined
      into analysis pipelines even by non-experts and can be used in proteomics
      workflows. These applications range from useful utilities (file format
      conversion, peak picking) over wrapper applications for known applications
      (e.g. Mascot) to completely new algorithmic techniques for data reduction
      and data analysis. We anticipate that TOPP will greatly facilitate
      rapid prototyping of proteomics data evaluation pipelines. As such,
      we describe the basic concepts and the current abilities of TOPP
      and illustrate these concepts in the context of two example applications:
      the identification of peptides from a raw dataset through database
      search and the complex analysis of a standard addition experiment
      for the absolute quantitation of biomarkers. The latter example demonstrates
      TOPP's ability to construct flexible analysis pipelines in support
      of complex experimental setups.The TOPP components are available
      as open-source software under the lesser GNU public license (LGPL).
      Source code is available from the project website at www.OpenMS.de},
      added-at = {2011-02-21T11:31:07.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/20f9c94322a3f2ccecd2748c82d3ec252/openms},
      description = {TOPP--the OpenMS proteomics pipeline. [Bioinformatics. 2007] - PubMed
      result},
      doi = {10.1093/bioinformatics/btl299},
      interhash = {b4313c84b75e3aa3f1147ff7d251ee2f},
      intrahash = {0f9c94322a3f2ccecd2748c82d3ec252},
      keywords = {TOPP mass spectrometry},
      pmid = {17237091},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/17237091}
    }
  • Go to document E. Lange, C. Gröpl, O. Schulz-Trieglaff, A. Leinenbach, C. Huber, and K. Reinert, “A geometric approach for the alignment of liquid chromatography-mass spectrometry data,” Bioinformatics, vol. 23, iss. 13, pp. 273-281, 2007.
    [Bibtex]
    @ARTICLE{Lange2007,
      author = {Eva Lange and Clemens Gr{\"o}pl and Ole Schulz-Trieglaff and Andreas Leinenbach and Christian G. Huber and Knut Reinert},
      title = {A geometric approach for the alignment of liquid chromatography-mass spectrometry data},
      journal = {Bioinformatics},
      year = {2007},
      volume = {23},
      pages = {273-281},
      number = {13},
      month = jul,
      abstract = {Liquid chromatography coupled to mass spectrometry (LC-MS) and combined
      with tandem mass spectrometry (LC-MS/MS) have become a prominent
      tool for the analysis of complex proteomic samples. An important
      step in a typical workflow is the combination of results from multiple
      LC-MS experiments to improve confidence in the obtained measurements
      or to compare results from different samples. To do so, a suitable
      mapping or alignment between the data sets needs to be estimated.
      The alignment has to correct for variations in mass and elution time
      which are present in all mass spectrometry experiments.We propose
      a novel algorithm to align LC-MS samples and to match corresponding
      ion species across samples. Our algorithm matches landmark signals
      between two data sets using a geometric technique based on pose clustering.
      Variations in mass and retention time are corrected by an affine
      dewarping function estimated from matched landmarks. We use the pairwise
      dewarping in an algorithm for aligning multiple samples. We show
      that our pose clustering approach is fast and reliable as compared
      to previous approaches. It is robust in the presence of noise and
      able to accurately align samples with only few common ion species.
      In addition, we can easily handle different kinds of LC-MS data and
      adopt our algorithm to new mass spectrometry technologies.This algorithm
      is implemented as part of the OpenMS software library for shotgun
      proteomics and available under the Lesser GNU Public License (LGPL)
      at www.openms.de.},
      added-at = {2011-02-21T11:32:11.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2f12cf8af023fef6a80656d51381a90da/openms},
      description = {A geometric approach for the alignment of liquid c... [Bioinformatics.
      2007] - PubMed result},
      doi = {10.1093/bioinformatics/btm209},
      interhash = {1cdc4f7cf885d1d02a5c028b9708fe62},
      intrahash = {f12cf8af023fef6a80656d51381a90da},
      keywords = {alignment chromatography lc mass spectrometry},
      pmid = {17646306},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/17646306?dopt=Abstract}
    }
  • Go to document N. Pfeifer, A. Leinenbach, C. Huber, and O. Kohlbacher, “Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics,” BMC Bioinformatics, vol. 8, pp. 468-468, 2007.
    [Bibtex]
    @ARTICLE{Pfeifer2007,
      author = {Nico Pfeifer and Andreas Leinenbach and Christian G. Huber and Oliver Kohlbacher},
      title = {Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics},
      journal = {BMC Bioinformatics},
      year = {2007},
      volume = {8},
      pages = {468-468},
      abstract = {High-throughput peptide and protein identification technologies have
      benefited tremendously from strategies based on tandem mass spectrometry
      (MS/MS) in combination with database searching algorithms. A major
      problem with existing methods lies within the significant number
      of false positive and false negative annotations. So far, standard
      algorithms for protein identification do not use the information
      gained from separation processes usually involved in peptide analysis,
      such as retention time information, which are readily available from
      chromatographic separation of the sample. Identification can thus
      be improved by comparing measured retention times to predicted retention
      times. Current prediction models are derived from a set of measured
      test analytes but they usually require large amounts of training
      data.We introduce a new kernel function which can be applied in combination
      with support vector machines to a wide range of computational proteomics
      problems. We show the performance of this new approach by applying
      it to the prediction of peptide adsorption/elution behavior in strong
      anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase
      high-performance liquid chromatography (IP-RP-HPLC). Furthermore,
      the predicted retention times are used to improve spectrum identifications
      by a p-value-based filtering approach. The approach was tested on
      a number of different datasets and shows excellent performance while
      requiring only very small training sets (about 40 peptides instead
      of thousands). Using the retention time predictor in our retention
      time filter improves the fraction of correctly identified peptide
      mass spectra significantly.The proposed kernel function is well-suited
      for the prediction of chromatographic separation in computational
      proteomics and requires only a limited amount of training data. The
      performance of this new method is demonstrated by applying it to
      peptide retention time prediction in IP-RP-HPLC and prediction of
      peptide sample fractionation in SAX-SPE. Finally, we incorporate
      the predicted chromatographic behavior in a p-value based filter
      to improve peptide identifications based on liquid chromatography-tandem
      mass spectrometry.},
      added-at = {2011-02-21T11:16:48.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2bdce053d4ebabb6f6380cba4badf1fc3/openms},
      description = {Statistical learning of peptide retention behavior... [BMC Bioinformatics.
      2007] - PubMed result},
      doi = {10.1186/1471-2105-8-468},
      interhash = {272f11754af92d1ca6a28a684f5234b8},
      intrahash = {bdce053d4ebabb6f6380cba4badf1fc3},
      keywords = {chromatography prediciton rt},
      pmid = {18053132},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/18053132?dopt=AbstractPlus&holding=f1000,f1000m,isrctn}
    }
  • Go to document O. Schulz-Trieglaff, R. Hussong, C. Gröpl, A. Hildebrandt, and K. Reinert, “A Fast and Accurate Algorithm for the Quantification of Peptides from Mass Spectrometry Data,” , T. Speed and H. Huang, Ed., Berlin / Heidelberg: Springer, 2007, vol. 4453, pp. 473-487.
    [Bibtex]
    @INCOLLECTION{Schulz-Trieglaff2007,
      author = {Ole Schulz-Trieglaff and Rene Hussong and Clemens Gr{\"o}pl and Andrea Hildebrandt and Knut Reinert},
      title = {A Fast and Accurate Algorithm for the Quantification of Peptides from Mass Spectrometry Data},
      booktitle = {Research in Computational Molecular Biology},
      publisher = {Springer},
      year = {2007},
      editor = {Terry Speed and Haiyan Huang},
      volume = {4453},
      series = {Lecture Notes in Computer Science},
      pages = {473-487},
      address = {Berlin / Heidelberg},
      abstract = {Liquid chromatography combined with mass spectrometry (LC-MS) has
      become the prevalent technology in high-throughput proteomics research.
      One of the aims of this discipline is to obtain accurate quantitative
      information about all proteins and peptides in a biological sample.
      Due to size and complexity of the data generated in these experiments,
      this problem remains a challenging task requiring sophisticated and
      efficient computational tools. We propose an algorithm that can quantify
      even low abundance peptides from LC-MS data. Our approach is flexible
      and can be applied to preprocessed and raw instrument data. It is
      based on a combination of the sweep line paradigm with a novel wavelet
      function tailored to detect isotopic patterns. We evaluate our technique
      on several data sets of varying complexity and show that we are able
      to rapidly quantify peptides with high accuracy in a sound algorithmic
      framework.},
      added-at = {2011-02-21T11:31:25.000+0100},
      affiliation = {Max Planck Research School, Berlin Germany},
      biburl = {http://www.bibsonomy.org/bibtex/23aac65078abced5a5d034760674cf696/openms},
      description = {SpringerLink - Abstract},
      doi = {10.1007/978-3-540-71681-5_33},
      interhash = {fa4137e1ee2102b25c12097500ed06fa},
      intrahash = {3aac65078abced5a5d034760674cf696},
      keywords = {mass quantification spectrometry},
      url = {http://dx.doi.org/10.1007/978-3-540-71681-5_33}
    }

2006

  • E. Lange, C. Gröpl, K. Reinert, O. Kohlbacher, and A. Hildebrandt, “High-accuracy peak picking of proteomics data using wavelet techniques,” Pac Symp Biocomput, pp. 243-254, 2006.
    [Bibtex]
    @ARTICLE{Lange2006,
      author = {Eva Lange and Clemens Gr{\"o}pl and Knut Reinert and Oliver Kohlbacher and Andreas Hildebrandt},
      title = {High-accuracy peak picking of proteomics data using wavelet techniques},
      journal = {Pac Symp Biocomput},
      year = {2006},
      pages = {243-254},
      abstract = {A new peak picking algorithm for the analysis of mass spectrometric
      (MS) data is presented. It is independent of the underlying machine
      or ionization method, and is able to resolve highly convoluted and
      asymmetric signals. The method uses the multiscale nature of spectrometric
      data by first detecting the mass peaks in the wavelet-transformed
      signal before a given asymmetric peak function is fitted to the raw
      data. In an optional third stage, the resulting fit can be further
      improved using techniques from nonlinear optimization. In contrast
      to currently established techniques (e.g. SNAP, Apex) our algorithm
      is able to separate overlapping peaks of multiply charged peptides
      in ESI-MS data of low resolution. Its improved accuracy with respect
      to peak positions makes it a valuable preprocessing method for MS-based
      identification and quantification experiments. The method has been
      validated on a number of different annotated test cases, where it
      compares favorably in both runtime and accuracy with currently established
      techniques. An implementation of the algorithm is freely available
      in our open source framework OpenMS.},
      added-at = {2011-02-21T11:30:37.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/24d31fc0c085e7c9142a005f55c820be5/openms},
      description = {High-accuracy peak picking of proteomics data usin... [Pac Symp Biocomput.
      2006] - PubMed result},
      interhash = {0d1dce1eb823e1fd6da0a3db41a2b27b},
      intrahash = {4d31fc0c085e7c9142a005f55c820be5},
      keywords = {peak picking proteomics},
      pmid = {17094243},
      url = {http://www.ncbi.nlm.nih.gov/pubmed/17094243}
    }
  • Go to document B. Mayr, O. Kohlbacher, K. Reinert, M. Sturm, C. Gröpl, E. Lange, C. Klein, and C. Huber, “Absolute Myoglobin Quantitation in Serum by Combining Two-Dimensional Liquid Chromatography-Electrospray Ionization Mass Spectrometry and Novel Data Analysis Algorithms,” Journal of Proteome Research, vol. 5, iss. 2, pp. 414-421, 2006.
    [Bibtex]
    @ARTICLE{Mayr2006,
      author = {Bettina M. Mayr and Oliver Kohlbacher and Knut Reinert and Marc Sturm and Clemens Gr{\"o}pl and Eva Lange and Christoph Klein and Christian G. Huber},
      title = {Absolute Myoglobin Quantitation in Serum by Combining Two-Dimensional Liquid Chromatography-Electrospray Ionization Mass Spectrometry and Novel Data Analysis Algorithms},
      journal = {Journal of Proteome Research},
      year = {2006},
      volume = {5},
      pages = {414-421},
      number = {2},
      abstract = { To measure myoglobin, a marker for myocardial infarction, directly
      in human serum, two-dimensional liquid chromatography in combination
      with electrospray ionization mass spectrometry was applied as an
      analytical method. High-abundant serum proteins were depleted by
      strong anion-exchange chromatography. The myoglobin fraction was
      digested and injected onto a 60 mm × 0.2 mm i.d. monolithic capillary
      column for quantitation of selected peptides upon mass spectrometric
      detection. The addition of known amounts of myoglobin to the serum
      sample was utilized for calibration, and horse myoglobin was added
      as an internal standard to improve reproducibility. Calibration graphs
      were linear and facilitated the reproducible and accurate determination
      of the myoglobin amount present in serum. Manual data evaluation
      using integrated peak areas and an automated multistage algorithm
      fitting two-dimensional models of peptide elution profiles and isotope
      patterns to the mass spectrometric raw data were compared. When the
      automated method was applied, a myoglobin concentration of 460 pg/μL
      serum was determined with a maximum relative deviation from the theoretical
      value of 10.1% and a maximum relative standard deviation of 13.4%.
      Keywords: absolute quantitation • serum • myoglobin • high-performance
      liquid chromatography • electrospray ionization mass spectrometry
      • two-dimensional HPLC • standard addition • monoliths •
      computational proteomics • algorithms },
      added-at = {2011-02-21T11:30:52.000+0100},
      biburl = {http://www.bibsonomy.org/bibtex/2b92d40790016f0c911e18ff7a38cc96d/openms},
      description = {Absolute Myoglobin Quantitation in Serum by Combining Two-Dimensional
      Liquid Chromatography−Electrospray Ionization Mass Spectrometry and
      Novel Data Analysis Algorithms - Journal of Proteome Research (ACS
      Publications)},
      doi = {10.1021/pr050344u},
      eprint = {http://pubs.acs.org/doi/pdf/10.1021/pr050344u},
      interhash = {2f3ae626d4bbe9dfdaf43c666af4ef0f},
      intrahash = {b92d40790016f0c911e18ff7a38cc96d},
      keywords = {chromatography},
      url = {http://pubs.acs.org/doi/abs/10.1021/pr050344u}
    }

2005

  • Go to document C. Gröpl, E. Lange, K. Reinert, O. Kohlbacher, M. Sturm, C. Huber, B. Mayr, and C. Klein, “Algorithms for the Automated Absolute Quantification of Diagnostic Markers in Complex Proteomics Samples,” , M. Berthold, R. Glen, K. Diederichs, O. Kohlbacher, and I. Fischer, Ed., Berlin / Heidelberg: Springer, 2005, vol. 3695, pp. 151-162.
    [Bibtex]
    @INCOLLECTION{Gröpl2005,
      author = {Clemens Gr{\"o}pl and Eva Lange and Knut Reinert and Oliver Kohlbacher and Marc Sturm and Christian G. Huber and Bettina M. Mayr and Christoph L. Klein},
      title = {Algorithms for the Automated Absolute Quantification of Diagnostic Markers in Complex Proteomics Samples},
      booktitle = {Computational Life Sciences},
      publisher = {Springer},
      year = {2005},
      editor = {Michael R. Berthold and Robert Glen and Kay Diederichs and Oliver Kohlbacher and Ingrid Fischer},
      volume = {3695},
      series = {Lecture Notes in Computer Science},
      pages = {151-162},
      address = {Berlin / Heidelberg},
      abstract = {HPLC-ESI-MS is rapidly becoming an established standard method for
      shotgun proteomics. Currently, its major drawbacks are two-fold:
      quantification is mostly limited to relative quantification and the
      large amount of data produced by every individual experiment can
      make manual analysis quite difficult. Here we present a new, combined
      experimental and algorithmic approach to absolutely quantify proteins
      from samples with unprecedented precision. We apply the method to
      the analysis of myoglobin in human blood serum, which is an important
      diagnostic marker for myocardial infarction. Our approach was able
      to determine the absolute amount of myoglobin in a serum sample through
      a series of standard addition experiments with a relative error of
      2.5%. Compared to a manual analysis of the same dataset we could
      improve the precision and conduct it in a fraction of the time needed
      for the manual analysis. We anticipate that our automatic quantitation
      method will facilitate further absolute or relative quantitation
      of even more complex peptide samples. The algorithm was developed
      using our publically available software framework OpenMS (www.openms.de).},
      added-at = {2011-02-21T11:26:59.000+0100},
      affiliation = {Free University Berlin, Algorithmic Bioinformatics, D-14195 Berlin Germany},
      biburl = {http://www.bibsonomy.org/bibtex/2503de52130a24d7417dca79cabe50d96/openms},
      description = {SpringerLink - Abstract},
      doi = {10.1007/11560500_14},
      interhash = {d8e83bb4f35d029202f1d48cd78f6326},
      intrahash = {503de52130a24d7417dca79cabe50d96},
      keywords = {OpenMS quantification},
      url = {http://dx.doi.org/10.1007/11560500_14}
    }