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Computational methods for high-resolution mass spectrometry data and massive parallel sequencing (R-9605)
Current developments in high-throughput techniques for life sciences have resulted in ever growing amounts of data. This has led to a situation where the interpretation of data and the formulation of hypotheses lag the pace at which data are produced. The proposed project focuses on mass spectrometrybased proteomics and massive parallel sequencing that measure the expression and fragmentation patterns of thousands of biomolecules in a single experiment. The resulting massive datasets are often analyzed in a sub-optimal manner. As a result, valuable information is lost. It is our ambition to apply statistical and data mining techniques that would allow addressing the issue and enable obtaining new insights and hypotheses. The algorithms are applied to data from fields like, e.g., proteomics, lipidomics, or epigenomics generated by various types of biotechnology instrumentation. To accomplish our objectives, we adopt theoretical and applied research conducted at the University of Warsaw (UW), Warsaw University of Technology (WUT), and Hasselt University (UH). The project is centered on topic-related problems (work packages) that aim at solving the data processing issues. The topics fit within the joint research and Ph.D. tutoring programs at HU, UW, and WUT. The scope of the funding is to extend collaboration and researcher's mobility.
Date:1 Jan 2019 → Today
Keywords:Data Science, Genomics, Mass spectrometry
Disciplines:Analytical spectrometry, Spectroscopic methods