The Bioinformatics group uses computational tools to bring together data from different domains in order to better understand human disease. The group develops and operates advanced mass spectrometry instrumentation, such as an FTICR-ion trap cluster, for large-scale and label-free proteomics data. This system was used to produce the first population-based proteomics study, linking the plasma protein abundance (phenotype) to genotypic variability. We also optimize design-of-experiment, by using scientific workflows to integrate all available resources and information, including mass spectral libraries to select the best surrogate peptides for targeted, labeled, quantitative proteomics. This is both a research method and a technique eminently amenable to routine application in clinical labs. We have pioneered direct spectral comparison techniques for molecular phylogenetics and the similar use of spectral libraries to differentiate and identify biological samples, including important human pathogens. We also use model systems such as mouse, and in particular zebrafish, to study infectious diseases and cancer in collaboration with several groups within the LUMC and at Leiden University.
Our software is used by many groups worldwide, and our spectral libraries included in the NIST high-quality spectral library collection. We strive to always be ahead of the rapidly changing and improving instrumentation with simulation and analyzing massive amounts of data acquired in our and other researchers’ labs. We publish our work in leading journals in the field, including Molecular and Cellular Proteomics, Journal of Proteome Research, Analytical Chemistry and Journal of Proteomics, but also general journals such as P.N.A.S. We also mine the scientific literature using advanced, scientific workflow-based, bibliometric methods.
Palmblad M. Visual and Semantic Enrichment of Analytical Chemistry Literature Searches by Combining Text Mining and Computational Chemistry. Analytical chemistry. 2019 Mar 13; PubMed PMID:30835438
Palmblad M, Lamprecht AL, Ison J, Schwämmle V. Automated workflow composition in mass spectrometry based proteomics. Bioinformatics. 2018 Jul 24; PMID:30060113
Mohammed Y, Palmblad M. Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention. Journal of separation science. 2018 Jul 26; PMID:30047222
Lee JY, Choi H, Colangelo CM, Davis D, Hoopmann MR, Käll L, Lam H, Payne SH, Perez-Riverol Y, The M, Wilson R, Weintraub ST, Palmblad M. ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data. Journal of biomolecular techniques. 2018 Jun 21; PMID:29977167
The M, Edfors F, Perez-Riverol Y, Payne SH, Hoopmann MR, Palmblad M, Forsström B, Käll L. A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms. Journal of proteome research. 2018 Apr 16; PMID:29631402
Mohammed Y, van Vlijmen BJ, Yang J, Percy AJ, Palmblad M, Borchers CH, Rosendaal FR. Multiplexed targeted proteomic assay to assess coagulation factor concentrations and thrombosis-associated cancer. Blood advances. 2017 Jun 27; PMID:29296750
Hussaarts L, Kaisar MMM, Guler AT, Dalebout H, Everts B, Deelder AM, Palmblad M, Yazdanbakhsh M. Human Dendritic Cells with Th2-Polarizing Capacity: Analysis Using Label-Free Quantitative Proteomics. International archives of allergy and immunology. 2017 Nov 10; PMID:29130972
Palmblad M, Torvik VI. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Tropical medicine and health. 2017; PMID:29093641
Travin D, Popov I, Guler AT, Medvedev D, van der Plas-Duivesteijn S, Varela M, Kolder ICRM, Meijer AH, Spaink HP, Palmblad M. COMICS: Cartoon Visualization of Omics Data in Spatial Context Using Anatomical Ontologies. Journal of proteome research. 2017 Nov 13; PMID:29083911
Mohammed Y, Palmblad M. Visualizing and comparing results of different peptide identification methods. Briefings in bioinformatics. 2016 Dec 22; PMID:28011752
Choi M, Eren-Dogu ZF, Colangelo C, Cottrell J, Hoopmann MR, Kapp EA, Kim S, Lam H, Neubert TA, Palmblad M, Phinney BS, Weintraub ST, MacLean B, Vitek O. ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments. Journal of proteome research. 2017 Feb 03; PMID:27990823