Research goal

We have done pioneering work in the area of Gibbs sampling based motif detection (MotifSampler), coexpression analysis and data integration (Remodiscovery, DISTILLER, :Lemone) and this mainly to infer interaction networks from publicly available data. More recently we have developed tools in which these networks inferred from publicly available data can be used as a scaffold to interpret in-house data (network-based data-interpretation). Anticipating that genomic variation at the level of the individual will provide us with an unprecedented source of information, not only to infer networks but also to study their evolution, we started focusing on the analysis and evolution of clonal communities by using genotype-phenotype analysis (eQTL) and this with applications in both bacterial (microbial evolution studies, understanding molecular interactions driving a microbial community) and eukaryotic clonal systems.

Research topics

Network-based systems genetics in non-clonal systems

We have expertise in applying standard bioinformatics pipelines for NGS sequencing, clustering, biclustering of omics data, use of probabilistic models, item set mining for data-integration. We have developed proprietary software for genotype-phenotype mapping in both clonal and non clonal systems.

Unveiling driver pathways in clonal systems

eQTL analysis of strains with a phenotype of interest offers a great potential for trait identification and studying natural variation. Classical eQTL analysis searches for a statistical association between a certain genetic locus and an expression phenotype. However, in independently evolved clonal systems, such as bacteria there is no guarantee that exactly the same locus is responsible for the observed phenotype. Rather mutations in the same pathways will result in the adaptive phenotype. For clonal systems eQTL analysis thus depends on the search for mutational consistency in terms of pathways, rather than in terms of individual mutations. To facilitate clonal eQTL we propose a network-based eQTL methods. These methods assumes that genes with adaptive mutations (driver mutations) obtained from different parallel evolved clones are more tightly ‘connected’ to each other and to the genes involved in a downstream expression phenotype on an interaction network than randomly acquired passenger mutations without relation to the focal phenotype. We have been tuning these tools for the identification of driver genes and pathways in cancer (pancancer analysis) and for studying adaptive microbial evolution.

Unveiling molecular mechanisms of interaction in simple microbial communities

Clonal interactions are believed to more easily establish in spatially structured or in heterogeneous environments than in homogenous ones. However, studies that mapped the evolutionary population dynamics indicate that interactions between clones might emerge even in homogenous cultures more frequently than expected although transiently and depending on the stochastic nature of the followed mutational trajectories. Clonal interactions might have remained unnoticed, because of the low frequency of one of the interacting genotypes and the lack of a comprehensive mapping of population dynamics during evolution. The understanding of clonal interactions and co-evolution in terms of molecular networks is also still largely understudied. We develop and apply methods to reconstruct genomes from pooled sequence data of simple microbial communities and use our network-based eQTL techniques to combine this genotypic information with coupled meta transcriptome data to reconstruct the molecular mechanism underlying the observed clonal interactions.

Expertise

We have expertise in applying standard bioinformatics pipelines for NGS sequencing, clustering, biclustering of omics data, use of probabilistic models, item set mining for data-integration. We have developed proprietary software for genotype-phenotype mapping in both clonal and non clonal systems.