The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
TOPP—the OpenMS proteomics pipeline
Abstract
Motivation: 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. Results: 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. Availability: 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 Contact: oliver.kohlbacher@uni-tuebingen.de
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LIBSVM: A library for support vector machines
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Cited by
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031591 · Cited by 5,180
ProteoWizard: open source software for rapid proteomics tools development
Kessner, D., Chambers, M., Burke, R., Agus, D., & Mallick, P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn323 · Cited by 2,024
Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present
Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-012-6203-4 · Cited by 801
Deutsch, E. W., Mendoza, L., Shteynberg, D., Slagel, J., Sun, Z., & Moritz, R. L. (2015) · PROTEOMICS – Clinical Applications · doi:10.1002/prca.201400164 · Cited by 342
Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0
Carvalho, P. C., Lima, D. B., Leprevost, F. V., Santos, M. D. M., Fischer, J. S. G., Aquino, P. F., Moresco, J. J., Yates, J. R., & Barbosa, V. C. (2016) · Nature Protocols · doi:10.1038/nprot.2015.133 · Cited by 283
Metadata
- Authors
- Kohlbacher, O., Reinert, K., Gröpl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O., & Sturm, M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-01-15
- Volume
- 23
- Issue
- 2
- Pages
- e191-e197
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2126297361
- Cited by
- 281
- References
- 2
- FWCI
- 11.47
- Type
- journal article
- Language
- en
- Open access
- closed
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