Department of Plant Systems Biology

1. Department of Plant Systems Biology Research at the Bioinformatics & Computational Biology research groups

2. Department of Plant Systems Biology • Headed by Prof. Dirk Inzé • 203 people (179 research staff, 24 technical/administrative staff) • 6 Research Divisions • Biology (146) • Molecular Genetics Division (87) • Functional Genomics Division (19) • Plant-Microbe Division (19) • Genome Dynamics and Gene Regulation Division (19) • (Bio)Informatics (33) • Bioinformatics and Evolutionary Genomics Division (24) • Computational Biology Division (9) Yvan Saeys, Donostia 2004

3. 2 “Computational” research groups • Bioinformatics and Evolutionary Genomics (BEG) • Mainly deal with sequence data • Comparative Genomics (Yves Van de Peer) • Gene prediction & Annotation (Pierre Rouzé) • Computational Biology Division (CBD) • Explore biological systems (networks) • Headed by Martin Kuiper Yvan Saeys, Donostia 2004

4. Group Leaders Prof. Yves Van de Peer Dr. Martin Kuiper Dr. Pierre Rouzé Yvan Saeys, Donostia 2004

5. Research activities Ancient large-scale gene duplications Transcription factors Annotation of genomes Machine Learning Bacterial comparative genomics Gene Prediction & Genome Annotation Comparative Genomics Non coding RNAs Gene network modelling Functional divergence of duplicated genes Promoters and regulatory elements Heterosis Yvan Saeys, Donostia 2004

6. Ancient large-scale gene duplications Klaas Vandepoele Cedric Simillion • Investigate major events during evolutionary past of genomes: • Large scale gene duplications • Genome duplications • Research • Algorithms to detect colinear regions • Compare intra and inter species • Arabidopsis: 3 whole genome duplications • Comparisons between Arabidopsis and Rice • Duplications in vertebrate genomes Yvan Saeys, Donostia 2004

7. ancient duplication HsaC1 C2 C4 HsaC9 synteny Large-scale duplications recent duplication colinearity Yvan Saeys, Donostia 2004

8. C 11 11 11 11 11 11 11 11 10 9 8 7 6 5 4 4 2 1 A 1 2 4 5 6 7 8 9 10 11 11 11 11 11 11 Not significant B 2 3 4 6 7 8 9 11 11 11 11 11 11 11 11 11 11 11 C 11 11 11 11 11 11 11 11 10 9 8 7 6 5 4 4 2 1 11 Building genomic profiles Not significant ! 11 11 11 A 1 2 4 5 6 7 8 9 10 11 11 11 11 11 11 11 10 9 B 2 3 4 6 7 8 9 11 11 11 11 11 11 11 11 8 7 6 5 4 4 2 significant homology! 1 C Ancient large-scale gene duplications A 1 2 3 4 5 6 7 8 9 10 11 11 11 11 11 B 1 2 3 4 5 6 7 8 9 10 11 11 11 11 11 Yvan Saeys, Donostia 2004

9. Functional divergence of duplicated genes Jeroen Raes Tine Casneuf • Duplications stimulate biological novelties • Investigate what happens to duplicated genes • Study of models for gene evolution • Genes are not individual entities, but members of gene families • Research • Up to 65% of the genes in Arabidopsis belong to a gene family • Divergence at the regulatory/expression level • Divergence at the coding level. Yvan Saeys, Donostia 2004

10. Functional divergence of duplicated genes Yvan Saeys, Donostia 2004

11. Bacterial comparative genomics Dirk Gevers • Investigation of multiple bacterial genomes • Genomes evolve over time, changing in subtle or radical ways, constantly adapting to the surrounding environment • Genomes can evolve gradually through vertical transmission of mutations, gene duplications, deletions, and rearrangements • Alternatively, they can evolve more suddenly and sporadically via horizontal transfer of genetic information between different microbial species • Research • Assess the contribution of gene duplications to genome evolution in prokaryotes Yvan Saeys, Donostia 2004

12. Bacterial comparative genomics • Functional Landscape of the Paranome (FLOP): • Linking functional information to the paranome information • Allows us to determine whether paralog retention is biased towards specific functional classes for each of the bacterial strains Yvan Saeys, Donostia 2004

13. Transcription factors Stefanie De Bodt • Towards a better understanding of the link between evolution and development (evo-devo) • Transcription factors play a major role in the regulation of gene expression • Study the evolutionary and functional divergence of genesbelonging to large transcription factor gene families • Research • Structural and phylogenetic analyses of the MADS-box gene family • Comprehensive view on the regulatory role of MADS-box genes in plant development • Phylogenetic footprinting Yvan Saeys, Donostia 2004

14. Transcription factors Yvan Saeys, Donostia 2004

15. Genome Annotation Stephane Rombauts Lieven Sterck Steven Robbens • Structural annotation of genes/genomes • Locate genes in genomes • Find the exact gene structures • Investigation of particular gene families • Research • Development of an automatic annotation platform that can be applied to different genomes • Genomes: Arabidopsis, Poplar, Medicago, Ostrecoccus tauri Yvan Saeys, Donostia 2004

16. Coding potential search EuGene RepeatMasker Blastn Blastx Genome Annotation platform SplicePredictor Intrinsic approaches NetGene2 Netstart Predicted Genes (structural annotation) Extrinsic approaches cDNA & EST SP & PIR RepBase Yvan Saeys, Donostia 2004

17. IMM Poplar IMM Select predicted genes covered by FL cDNA Let EuGene make prediction based on extrinsic data Blastn Blastx RepeatMasker SpliceMachine Blast against Arabidopsis proteins with full length, discard cDNAs that have no hit Training set of mapped cDNAs Dataset construction for Poplar EuGene framework Poplar RepBase Poplar cDNA & EST Arabidopsis proteins Extrinsic approaches Final prediction of EuGene EuGene Intrinsic approaches Splicing: WAM Start: const Start prediction Yvan Saeys, Donostia 2004

18. Annotation of core cell cycle genes in Ostreococcus tauri The CDK gene family Yvan Saeys, Donostia 2004

19. Machine Learning(applied to genome annotation) Sven Degroeve Yvan Saeys • Computational techniques to identify structural elements • Supervised classification methods • Support Vector Machines • Feature selection for knowledge extraction • Research • New splice site prediction models • New feature selection techniques for gene prediction • Leads to more accurate gene models Yvan Saeys, Donostia 2004

20. Splice Machine Yvan Saeys, Donostia 2004

21. Feature selection for acceptor prediction Yvan Saeys, Donostia 2004

22. Promoter prediction Kobe Florquin • Computational identification of promoter regions • Signal elements • Structural features • Still many false positives • Research • Develop new tools and approaches for the automatic delineation of promoters • Motif detection • Detecting cis-regulatory elements • Phylogenetic footprinting Yvan Saeys, Donostia 2004

23. Promoter prediction Yvan Saeys, Donostia 2004

24. Non coding RNAs Eric Bonnet Jan Wuyts • Many RNA molecules are not protein coding but instead function through their RNA form • Known a long time: transfer RNAs (tRNA), ribosomal RNAs (rRNA) • Only recently discovered: small interfering RNAs (siRNA), micro RNAs (miRNA), … • Regulate gene expression at the post-transcriptional level • Research • Developing different computational tools and techniques to detect and characterize non-coding RNAs in Arabidopsis and other plant genomes Yvan Saeys, Donostia 2004

25. Non coding RNAs: MIRfinder Yvan Saeys, Donostia 2004

26. Comparison between plant species Yvan Saeys, Donostia 2004

27. Genetic networks Steven Maere Steven Vercruysse • Integrate functional genomics data of all types in a global network that reflects the regulatory wiring and modularity of an organism • Micro-array data from perturbation experiments • Leaf development • Research • Novel methods, based on combinatorial statistics and graph theory • Unsupervised classification techniques (k-core clustering, Kohonen maps) Yvan Saeys, Donostia 2004

28. Genetic networks Experiments Gene profiles Comb. p-value < 0.01 k-core clustering GO labeling & visualization Yvan Saeys, Donostia 2004

29. Self-organizing map Goal: getting information about: - Protein function (same profile => same biol. process?) Genetic networks Hierarchical clustering Many other algorithms… - Regulatory interactions Yvan Saeys, Donostia 2004

30. Heterosis Jeroen Meeus Elena Tsiporkova • Modeling of “hybrid vigour” • Improved performance of F1 hybrids with respect to the parents • Dominance Model • Over-dominance Model • Epistatic Model • biometrics versus soft-computing approach • Research • Additive versus dominance effects • Estimation of the molecular phenotype of the hybrid Yvan Saeys, Donostia 2004

31. Step 1 correlation hybrid-parents Step 3 prediction Step 2 correlation morphological-molecular phenotypes Step 2 correlation morphological-molecular phenotypes heterotic non-heterotic Heterosis: Biometrics Approach 10 parents 45 hybrids Molecular Phenotypes 25000 genes 25000 genes biomass leaf size … biomass leaf size … Morphological Phenotypes 45 hybrids 10 parents Yvan Saeys, Donostia 2004

32. simulation association association direct classification heterotic non-heterotic Heterosis: Soft-Computing Approach 10 parents 45 hybrids Molecular Phenotypes 25000 genes 25000 genes biomass leaf size … biomass leaf size … Morphological Phenotypes 45 hybrids 10 parents Yvan Saeys, Donostia 2004

33. Databases European ribosomal RNA database http://www.psb.ugent.be/rRNA/ European Plant Promoter database (PlantCARE) http://oberon.fvms.ugent.be:8080/ PlantCARE/index.html European Federated Plant Database Network (Planet)http://mips.gsf.de/proj/planet/about.html Software Tree construction: TreeCon Tools: ForCon, SPADS, ZT, AFLPinSilico Large-scale duplications: Adhore, i-Adhore, ASaturA Website http://bioinformatics.psb.ugent.be Francis Dierick: databases, webmaster, support Gert Sclep: CATMA and CAGE databases Yvan Saeys, Donostia 2004

34. “Part-time” Phd students Guy Baele: Modelling the covarion hypothesis Dirk Vandycke: Extrinsic gene prediction approaches Secretary Ann Bostyn Yvan Saeys, Donostia 2004

35. Thanks to… Yvan Saeys, Donostia 2004