We are establishing bioinformatics tools that are aimed to facilitate mass spectrometric analyses of prokaryotic as well as for eukaryotic organisms with complex gene structures. Here we are aiming to use mass spectrometric data for genomic data mining and annotation of protein-coding genes. We devised an algorithm, the GenomicPeptideFinder (GPF), which takes advantage of de novo amino acid predictions of MS/MS data enabling detection of intron-split and/or alternatively spliced peptides when deduced from genomic DNA (Allmer et al., 2004, Allmer et al., 2006, Specht et al.,2011a). Further, we generated a platform for proteomic analysis (Specht et al. 2011b), and we devised different algorithms aimed for improved mass spectrometric data mining, including quantitative analyses using qTRACE (Terashima et al., 2010,) and pyQms (Leufken et al., 2017), we developed a fast parser for mzML data (Bald et al., 2011, Kosters et al., 2018) and established a python-based tool named “SugarPy” for de novo identification of N-glycosylated peptides and their N-glycan compositions from bacteria to human (Esquivel et al., 2016, Schulze et al., 2017, 2018, Oltmanns 2020, Schulze et al., 2020). With Crosslinx, we also devised a tool for the analysis of crosslinked peptides via mass spectrometry (Ozawa et al., 2018).
For proteomic analyses the laboratory is equipped with a hybrid linear ion-trap mass spectrometer (Q-Exactive plus-Orbitrap, Thermo Fischer) that is coupled to an Ultimate Nano liquid chromatography system.
GenomicPeptideFinder: Mass spectrometric data mining in genomic data bases (Freiburg 10.07)