Department of Plant Systems Biology

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Department of Plant Systems Biology

• Research at the Bioinformatics Computational
  Biology research groups

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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)

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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

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Group Leaders
Prof. Yves Van de Peer
Dr. Martin Kuiper
Dr. Pierre Rouzé
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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
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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

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Large-scale duplications
recent duplication
colinearity
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Ancient large-scale gene duplications
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B
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Functional divergence of duplicated genes
Tine Casneuf
Jeroen Raes

• 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.

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Functional divergence of duplicated genes
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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

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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

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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 genes belonging 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

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Transcription factors
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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

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Genome Annotation platform
SplicePredictor
Intrinsic approaches
NetGene2
Netstart
Predicted Genes (structural annotation)
Extrinsic approaches
cDNA EST
SP PIR
RepBase
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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
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Annotation of core cell cycle genes in
Ostreococcus tauri
The CDK gene family
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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

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Splice Machine
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Feature selection for acceptor prediction
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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

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Promoter prediction
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Non coding RNAs
Jan Wuyts
Eric Bonnet

• 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

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Non coding RNAs MIRfinder
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Comparison between plant species
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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)

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Genetic networks
Experiments
Gene profiles
Comb. p-value lt 0.01
k-core clustering
GO labeling visualization
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Genetic networks
Hierarchical clustering
Many other algorithms
- Regulatory interactions
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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

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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
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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
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• Databases
• European ribosomal RNA database
  http//www.psb.ugent.be/rRNA/
• European Plant Promoter database (PlantCARE)
• http//oberon.fvms.ugent.be8080/
• PlantCARE/index.html
• European Federated Plant Database Network
  (Planet) http//mips.gsf.de/proj/planet/about.htm
  l

• 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
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Part-time Phd students
Guy Baele Modelling the covarion hypothesis
Dirk Vandycke Extrinsic gene prediction
approaches
Secretary
Ann Bostyn
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Thanks to