Research Topics

Research at the Bioinformatics Group focuses on the analysis of genomes. The main techniques used hereby are data integration and data mining, or in plain text: "how do we make the huge amounts of biomolecular data accessible, and can we generate new knowledge from these data". Bioinformatics is by definition a multidisciplinary science, and therefore it will come as no surprise that we use many different techniques and tools, such as programming languages (C/C++, Java, Perl, Python, PHP, ...), databases (Oracle, mySQL), machine learning, text mining, web services, and data visualisation.
Since the bioinformatics tools and algorithms that we develop are in principle species-independent, the Laboratory of Bioinformatics studies not only plant but also animal and human systems.

Most of the software and database services developed by the group are freely accessible for researchers, such as Protein Orthology Group Mapping database, the protein tandem repeats database, SNP detection and haplotyping tools, protein domain structure visualisation, and many more. We also offer a number of public services, such as our SRS server, the GeneCards server, homology search servers (BLAST, FASTA, BLAT) (via web services), sequence analysis tools (EMBOSS, ClustalW, T-Coffee, Muscle) and phylogeny software.
See the Bioinformatics Toolbox page for an overview.

• Current research topics include:
• Bioinformation systems. By using data integration and data mining techniques we try to link the phenotype to the genotype in plants and animals. We also devote attention to rational methods of storing and retrieving data.
• Analysis and management of ~omics data. We aim to improve the resolving power of transcriptomics, metabolomics, proteomics and next-gen sequecing data, amongst others by using Bayesian networks and combining the different data sets.
• Orthology detection, genome and protein evolution, and phylogeny. We study the evolution of sequences and genomes, ranging from simple structures like amino acid and nucleotide repeats, up to the evolution of complete genomes, in particular of plants.
• SNP discovery and haplotyping. We develop new and improved methods to reliably detect SNPs in next-generation sequencing data sets.
• (semi-) Automated mapping of anatomical ontologies.
• Pathway reconstruction using text mining techniques.
• Large-scale data visualisation using tiled displays.
• Development of GRID and/or GPU enabled bioinformatics applications.