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.
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Acknowledgments
The authors would like to thank Johannes Junker for most of the implementation work of TOPPAS and the whole OpenMS team for their contributions to this project. We would also like to thank the Proteome Center Tübingen for providing the BSA measurements of the example dataset.
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Bertsch, A., Gröpl, C., Reinert, K., Kohlbacher, O. (2011). OpenMS and TOPP: Open Source Software for LC-MS Data Analysis. In: Hamacher, M., Eisenacher, M., Stephan, C. (eds) Data Mining in Proteomics. Methods in Molecular Biology, vol 696. Humana Press. https://doi.org/10.1007/978-1-60761-987-1_23
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DOI: https://doi.org/10.1007/978-1-60761-987-1_23
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