Ludger Goeminne received his master in Bio-Science Engineering in 2013 at Ghent University. From September 2013 to May 2019, he was a PhD student in the Clement group, supervised by prof. dr. ir. Lieven Clement and co-supervised by prof. dr. Kris Gevaert from the VIB-UGent Center for Medical Biotechnology.
In the statOmics group, Ludger focussed on methods to improve quantification in MS-based proteomics. In 2015, he demonstrated that peptide-based models outperform summarization-based approaches. Encouraged by these results, he developed MSqRob, an R package that combines legitimate statistical modeling for relative protein quantification from peptide-level data with an easy-to-use graphical interface. Additionally, Ludger won the best flash talk presentation award at the Eubic Winter School 2017 and he was a member of the vibes 2017 PhD Symposium Organizing Committee. After his PhD, Ludger worked as a postdoctoral researcher in the group of prof. Johan Auwerx at EPFL in Switzerland. He is currently working as a postdoctoral researcher in the group of Prof. Vadim Gladyshev at Brigham and Women’s Hospital and Harvard Medical School in the USA.
Peer-reviewed publications at statOmics
Goeminne L.J.E., Sticker A., Martens L., Gevaert K. and Clement L. (2020). MSqRob Takes the Missing Hurdle: Uniting Intensity- and Count-Based Proteomics. Analytical Chemistry 92(9). 6278–6287.
Van Quickelberghe, E., Martens, A., Goeminne L.J.E., Clement, L., van Loo, G., Gevaert, K. (2018). Identification of immune-response gene 1 (IRG1) as a target of A20. Journal of Proteome Research. 17(6). 2182–2191.
Goeminne L.J.E., Gevaert K. and Clement L. (2018). Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob Journal of Proteomics. 117. 23-36.
Argentini A., Goeminne L.J.E., Verheggen K., Hulstaert N., Staes A., Clement L., Martens L. (2016). moFF: a robust and automated approach to extract peptide ion intensities. Nature Methods. 13(12). 964-966.
Goeminne L.J.E., Gevaert K. and Clement L. (2016). Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics. Molecular & Cellular Proteomics. 15 (2). 657-668.
Goeminne L.J.E., Argentini A., Martens L. and Clement L. (2015). Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines. Journal of Proteome Research. 14 (6). 2457-2465.
Argentini A., Goeminne L.J.E., Verheggen K., Hulstaert N., Staes A., Clement L., Martens L. (2016). moFF to Extract Peptide Ion Intensities from LC-MS experiments. Protocol Exchange. DOI: 10.1038/ protex. 2016. 085.
e-mail: lgoeminne at bwh dot harvard dot edu