The research area Proteomics focuses on the analysis of proteins and peptides in biological data sets. Proteins are essential building blocks for every living organism and over the last years, mass spectrometry has become the method of choice for the analysis of those. To understand and investigate cell processes or gain new insights in illnesses triggered by certain proteins or the absence or oversupply of those, the identification and quantification of proteins in the tissue is of primary importance. Since 2011 and in close collaboration with the Mechtler group at the IMP, Vienna, our research group has focused on the area of computational proteomics and therein on the development of algorithms to identify proteins and peptides in biological samples. This has led to the emergence of the MS Amanda algorithm, a database search engine especially designed for high resolution data sets and perfectly capable of identifying chimeric spectra. In addition, we are currently working on a library search engine, MS Ana, that is able to identify peptides based on a previously created library of mass spectra with known peptide identifications. To complement that, also a search engine for the identification of peptides connected with a cleavable cross-linker, called MS Annika, is in progress.



Selected Publications

2019 Sebastian Dorl, Stephan M. Winkler, Karl Mechtler, and Viktoria Dorfer: MS Ana: A Spectral Library Search Engine Optimized for High-Accuracy Fragment Ion Data European Bioinformatics Community (EuBIC) Winter School 2019
2018 Sebastian Dorl, Stephan Winkler, Karl Mechtler, and Viktoria Dorfer: PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search Journal of Proteome Research , Vol. 17 , pp. 290–295
2018 Viktoria Dorfer, Sergey Maltsev, Stephan M. Winkler, and Karl Mechtler: CharmeRT: Boosting peptide identifications by chimeric spectra identification and retention time prediction Journal of Proteome Research , Vol. 17 , pp. 2581–2589
2014 Viktoria Dorfer, Peter Pichler, Thomas Stranzl, Johannes Stadlmann, Thomas Taus, Stephan M. Winkler, and Karl Mechtler: MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra Journal of Proteome Research , Vol. 13 , pp. 3679-84

Selected Projects

MS Amanda

MS Amanda is a scoring system to identify peptides out of tandem mass spectrometry data using a database of known proteins. This algorithm is especially designed for high resolution and high accuracy tandem mass spectra. One advantage of MS Amanda is the high speed of spectrum identification, especially with MS Amanda 2.0. In addition, MS Amanda is also very accurate, as we observe a high overlap of identified spectra with gold-standard algorithms Mascot and SEQUEST. Furthermore, MS Amanda was extended to allow for second search to identify peptides in chimeric tandem mass spectra as part of the CharmeRT workflow.

It is motivated by the fact that only 30-50 % of the spectra of a mass spectrometry run can be satisfyingly interpreted due to various reasons, including – often unknown – posttranslational modifications. In addition, the algorithms used by the majority of the proteomics community have been developed more than 10 years ago and do not provide sufficient results for high resolution tandem mass spectrometers which only emerged recently.

You can download MS Amanda Standalone or MS Amanda for for Proteome Discoverer here:

You can learn more about MS Amanda Second Search and the CharmeRT workflow here:


DI(FH) Dr. Viktoria Dorfer MSc.


DI(FH) Dr. Viktoria Dorfer MSc.

Sebastian Dorl MSc

Marina Strobl MSc

Prof.(FH) DI Dr. Stephan Winkler


2011 - present

Research Areas


Research Institutions

University of Applied Sciences Upper Austria, Hagenberg, Austria

Research Institute of Molecular Pathology, Vienna, Austria

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