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Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting.

Abstract

UNLABELLED: A set of new algorithms and software tools for automatic protein identification using peptide mass fingerprinting is presented. The software is automatic, fast and modular to suit different laboratory needs, and it can be operated either via a Java user interface or called from within scripts. The software modules do peak extraction, peak filtering and protein database matching, and communicate via XML. Individual modules can therefore easily be replaced with other software if desired, and all intermediate results are available to the user. The algorithms are designed to operate without human intervention and contain several novel approaches. The performance and capabilities of the software is illustrated on spectra from different mass spectrometer manufacturers, and the factors influencing successful identification are discussed and quantified. MOTIVATION: Protein identification with mass spectrometric methods is a key step in modern proteomics studies. Some tools are available today for doing different steps in the analysis. Only a few commercial systems integrate all the steps in the analysis, often for only one vendor's hardware, and the details of these systems are not public. RESULTS: A complete system for doing protein identification with peptide mass fingerprints is presented, including everything from peak picking to matching the database protein. The details of the different algorithms are disclosed so that academic researchers can have full control of their tools. AVAILABILITY: The described software tools are available from the Halmstad University website www.hh.se/staff/bioinf/ SUPPLEMENTARY INFORMATION: Details of the algorithms are described in supporting information available from the Halmstad University website www.hh.se/staff/bioinf/

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BibTeXRIS

Jim Samuelsson, Daniel Dalevi, Fredrik Levander, Thorsteinn Rögnvaldsson. 2004-08-05. Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting.. https://doi.org/10.1093/bioinformatics%2Fbth460

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