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MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data

Jürgen Hartler12, Gerhard G Thallinger1, Gernot Stocker1, Alexander Sturn1, Thomas R Burkard1, Erik Körner3, Robert Rader1, Andreas Schmidt4, Karl Mechtler5 and Zlatko Trajanoski1*

Author Affiliations

1 Institute for Genomics and Bioinformatics and Christian-Doppler Laboratory for Genomics and Bioinformatics, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria

2 Austrian Research Centers GmbH -ARC, eHealth Systems, Reininghausstrasse 13/1, 8020 Graz, Austria

3 FH Joanneum, Kapfenberg, Werk-VI-Sraße 46, 8605 Kapfenberg, Austria

4 Christian Doppler Laboratory for Proteome Analysis, Dr. Bohr-Gasse 3, 1030 Vienna, Austria

5 Research Institute of Molecular Pathology, Dr. Bohr-Gasse 7, 1030 Vienna, Austria

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BMC Bioinformatics 2007, 8:197  doi:10.1186/1471-2105-8-197

Published: 13 June 2007



The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.


We have developed the MAss SPECTRometry Analysis System (MASPECTRAS), a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo) relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI). Analysis modules include: 1) import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2) peptide validation, 3) clustering of proteins based on Markov Clustering and multiple alignments; and 4) quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio). The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE). MASPECTRAS is freely available at webcite


Given the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.