Data generation in omics involves many steps which can introduce technical bias that may in suppress and confound the biological signal. For successful biomarker discovery we need to analyze data in a way that allows us to find biological differences and not signals introduced during sample handling, data generation or data processing. We constantly work with developing methods to improve the possibilities to detect relevant biological signals. Results from this work includes the Normalyzer and NormalyzerDE software.
We also work with development of computational methods to extract as much protein information as possible from mass spectrometry proteomics data.
Most of the software we have developed are available for download and online use at quantiativeproteomics.org