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Lennart Martens
Computational Omics and Systems Biology Group
VIB Department of Medical Protein Research, UGent
PhD: Univ. of Ghent, Ghent, Belgium, '06
Postdoc: EMBL-EBI, Cambridge, UK, '06-'09
VIB Group leader since 2009 |
e-mail
phone +32 9 264 93 58
ADDRESS |
Current team members
Group leader: Lennart Martens
Ph.D. Student: Kenny Helsens
Keywords
computational - proteomics - degradomics - interactomics - systems biology
Science
The development of several high-throughput Omics fields (genomics, transcriptomics, proteomics, metabolomics) over the past few years, has already resulted in a wealth of data on these various stages in the flow of biological information (from genes to proteins, and on to their –metabolic- functions). Yet this large amount of information is at the same time quite challenging; indeed, making sense of such volumes of data is no longer straightforward. Initial data processing to obtain results, along with the required quality control of these results, has to be automated. Additionally, storage and retrieval of multi-experiment data requires specific informatics infrastructure as well. On a more global level, the dissemination of the (published) data to the scientific community also necessitates the building of publicly accessible, domain-specific repositories. Finally, the integration of the various results obtained across the different domains remains very much an ongoing research effort in the life sciences.
Throughout the technological maturation process of the Omics fields, one of the key roles of bioinformatics has been to analyse the information after it was obtained from an experiment: the so-called data-driven approach. As the various fields have matured however, targeted methodologies that start with more focused questions are again gaining in prominence. Correspondingly, bioinformatics analyses have to morph into computational planning approaches, where the brunt of the informatics effort is expended prior to running the experiment.
This exciting transition is in turn a pre-requisite to collecting sufficiently comprehensive and reliable data to allow the fine-tuning of systems biology models of reactions and pathways. Indeed, modelling efforts today are in part restricted by the limited amount of available data, resulting in the poor coverage of genes, proteins, or metabolites involved. Other aspects of the models, such as catalogues of protein-protein interactions, often have to deal with the converse problem of sometimes noisy data.
We therefore focus on three key points, aimed at enabling systems biology modelling:
- Data collection and integration across the various Omics fields
- (Semi-) automatic quality control of the obtained data using configurable expert systems
- Development of computational Omics to help set-up and guide experiments based on a user-supplied list of target entities
Selected Publications
Martens L, Chambers M, Sturm M, Kessner D, Levander F, Shofstahl J, Tang W, Rompp A, Neumann S, Pizarro A, Montecchi-Palazzi L, Tasman N, Coleman M, Reisinger F, Souda P, Hermjakob H, Binz P, Deutsch E
mzML - a Community Standard for Mass Spectrometry Data
MOL CELL PROTEOMICS e-pub, e-pub, 2010
Barsnes H, Vizcaino J, Eidhammer I, Martens L
PRIDE Converter: making proteomics data-sharing easy
NAT BIOTECHNOL 27, 598-9, 2009
Helsens K, Timmerman E, Vandekerckhove J, Gevaert K, Martens L
Peptizer, a tool for assessing false positive Peptide identifications and manually validating selected results
MOL CELL PROTEOMICS 7, 2364-72, 2008
Klie S, Martens L, Vizcaino J, Cote R, Jones P, Apweiler R, Hinneburg A, Hermjakob H
Analyzing large-scale proteomics projects with latent semantic indexing
J PROTEOME RES 7, 182-91, 2008
Martens L, Hermjakob H, Jones P, Adamski M, Taylor C, States D, Gevaert K, Vandekerckhove J, Apweiler R
PRIDE: The proteomics identifications database
PROTEOMICS 5, 3537-3545, 2005
Search Publications
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