Bioinformatics

Research at the bioinformatics subunit revolves around the analysis of data acquired from biological/clinical samples using high−end mass spectrometers and other instruments. While this type of data is extremely information−rich and allows for comprehensive analyses, its sheer nature and volume can severely complexify the procedure. Dealing with this complexity and taking advantage of it, requires research combining computational science, mathematical, statistical and physical disciplines.

Difference detection between samples belonging to two different classes (e.g. patients suffering from a specific disease and controls), is an important example of an analysis that we perform.
This involves the development and application of new machine learning techniques, like support vector machines, neural networks and linear discriminant methods.
The efficacy of machine learning techniques and robustness of the derived results, depend strongly on the preprocessing of the data.
Development of preprocessing methods and algorithms is therefore an important part of our research.
Furthermore, we research optimal statistical methods to determine significant differences between different classes.

We also study methods for data mining and data integration of biologically related information, the design and realization of the required systems. Incidentaly, we develop ad hoc software for specific projects.

All these activities severely strain our computing resources.In order to improve performance, or even enable some analyses, we also research hardware, operating systems, file systems and other infrastructural aspects of our analysis and storage platform.
In order to store and process vast data volumes, both time and cost efficiently, we have, independently and in collaboration with our IT division, deployed a parallel computing infrastructure with more than 80 CPU cores, connected at Gigabit per second bandwidth to more than 35 Terabytes of on−line storage space and tape−based long term storage that can be expanded to Petabyte scale volumes.

Contact

 Dr. Alex A. Henneman

Phone: +31 (0)71 526 5078
Fax: +31 (0)71 526 6907
a.a.henneman@lumc.nl 

Staff

 Dr. Alex A. Henneman
 Ir. Rob J. Marissen

Key publication

FONAGER, J., CUNNINGHAM, D., JARRA, W., KOERNIG S., HENNEMAN A.A., LANGHORNE J., PREISER P. (2007).  Transcription and alternative splicing in the yir multigene family of the malaria parasite Plasmodium y. yoelii: identification of motifs suggesting epigenetic and post-transcriptional control of RNA expression. Molecular and Biochemical Parasitology 156(1):1-11.