Research focus

​Yves Van de Peer, in close collaboration with Pierre Rouzé, leads the Bioinformatics and Evolutionary Genomics (BEG) Division. The BEG division is a centre of excellence in the field of gene and genome annotation and in the field of comparative and evolutionary genomics. In the future, our research group would also like to engage more in what we would refer to as ‘evolutionary systems biology’, by studying the evolution of entire biological processes and networks through gene duplication and divergence of transcriptional regulation. We also try to integrate our research within a system biology perspective by linking our genome models with experimental genomics information as well as in silico functional predictions, with miRNAs and cis-acting transcriptional modules being typical examples.

Over the last few years, novel gene prediction and modeling tools have been developed, making wider use not only of machine learning algorithms but also of comparative approaches using sequence information from other genomes. The annotation tools that have been developed so far have been mainly used for re-annotating the Arabidopsis genome, and to obtain the first preliminary annotations of two novel genomes, namely that of the unicellular green alga Ostreococcus tauri and that of the poplar tree. In the coming years, we will further improve our gene prediction software, making use of feature subset selection techniques that are currently being developed in order to speed up annotation efforts and further increase the reliability and efficiency of genome annotation projects. Genome annotation will continue to be a major driver for our research (see below), since we will be involved in future gene prediction and genome annotation projects such as those of the tomato, Medicago, a different Ostreococcus strain, two mycorrhizal fungi (Laccaria and Glomus), a brown alga (Ectocarpus), and a moss (Physcomitrella). Consequently, further development of efficient gene annotation platforms and pipelines is of crucial importance.

These new genomes will provide us with a large amount of new sequence information, which we will exploit for further research during the coming years. In particular, these new genomes will be applied in a comparative way to:
• Identify conserved non-coding RNA genes
• Identify conserved cis-regulatory elements in promoter regions
• Delineate gene families across plant species
• Identify syntenic and collinear regions between different genomes
• Trace evolutionary history of some major genes, pathways and networks

One of the biological processes we would like to study in more detail, using an evolutionary systems biology approach, is the cell cycle. The availability of many new genomes will allow us to study all genes involved in the cell cycle across different species, and how the cell cycle has evolved through time and over the tree of life. Functional genomics data and comparative approaches will allow us to study the transcriptional network underlying the cell cycle, from more simple organisms to more complex ones.

Thirty-five years ago, Susumu Ohno´s seminal book ’Evolution by Gene Duplication’ outlined the potential role of gene duplication as the driving force behind the evolution of increasingly complex organisms. Recent analysis of complete eukaryotic genome sequences has revealed that gene duplication has indeed been rampant. Moreover, next to a continuous mode of small-scale gene duplications, in many organisms the complete genome – or at least large portions thereof – has been duplicated in their evolutionary past. In particular, such large-scale gene-duplication events have been associated with important evolutionary transitions, and are considered a major driving force for evolution, increased biological complexity and adaptive radiations of species. However, the mechanisms underlying evolutionary innovation through these genome duplications and the overall consequences for evolution and complexity remain unclear.

We have recently started to develop fully-fledged mathematical models that quantitatively simulate the population dynamics of duplicate genes. Such a model estimates the birth and death rates of genes, taking into account both major, genome-wide duplication events and a continuous mode of gene duplication and gene loss in a coupled differential-equation framework.

By applying the computational model to different functional categories of Arabidopsis genes, we can assess the importance of different gene duplication events for the evolution of specific gene functions or biological processes and pathways. Our model has already shown that decay rates for genes of most functional Gene Ontology categories are vastly different for large-scale and small-scale duplication events. Furthermore, it allows correlating gene duplication events with decisive moments in evolution. Applying such mathematical models will allow us to speculate on the evolution of important biological processes, and can provide working hypotheses for the further study of biological networks and evolution of transcriptional regulation.