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      Is Open Access

      OpenMS – An open-source software framework for mass spectrometry

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          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.

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          Most cited references27

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          Generalizing the Hough transform to detect arbitrary shapes

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            Open mass spectrometry search algorithm.

            Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.
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              MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.

              New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                26 March 2008
                : 9
                : 163
                Affiliations
                [1 ]Center for Bioinformatics, Eberhard Karls University Tübingen, Sand 14, 72076 Tübingen, Germany
                [2 ]Algorithmic Bioinformatics, Free University Berlin, Takustr. 9, 14195 Berlin, Germany
                [3 ]Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, 14195 Berlin, Germany
                [4 ]Center for Bioinformatics, Saarland University, Stuhlsatzenhausweg, Bld. E11, 66041 Saarbrücken, Germany
                Article
                1471-2105-9-163
                10.1186/1471-2105-9-163
                2311306
                18366760
                02b643c3-516a-440c-968e-d555289b331a
                Copyright © 2008 Sturm et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 November 2007
                : 26 March 2008
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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