The Swiss Institute of Bioinformatics

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The Swiss Institute of Bioinformatics

• Ernest Feytmans
• Director

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SIB activities

• The SIB brings Swiss experts in bioinformatics
  together and provides high quality services to
  the national and international scientific
  community.
• The SIB is a privileged partner of Swiss
  Universities
• Members of the SIB include research groups in
  Geneva, Lausanne, Basel and Zurich.
• The SIB participates in Masters degrees of
  partner universities and organises a doctoral
  school in Bioinformatics.

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The SIB in Switzerland
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Group Leaders

• 12 membres
• Ron Appel, Proteome Informatics, UniGE
• Amos Bairoch, Swiss-Prot, UniGE
• Bastien Chopard, Computer simulations, UniGE
• Philipp Bucher, Computational Cancer Genomics,
  ISREC
• Mauro Delorenzi, Bioinformatics Core Facility,
  ISREC
• Félix Naef, Computational Sytems Biology, ISREC
• C. Victor Jongeneel, Vital-IT et Transcriptome
  Analysis, LICR
• Olivier Michielin, Molecular Modeling, UniL et
  LICR
• Michael Primig, Genome Bioinformatics, UniBas
• Torsten Schwede, Protein Structure
  Bioinformatics, UniBas
• Erik van Nimwegen, Genome Systems Biology, UniBas
• Mihaela Zavolan, RNA Regulatory Networks, UniBas
• Gaston Gonnet, EPFZ
• Joerg Stelling, EPFZ
• Evgeny Zdobnov, UniGE
• Bernard Moret, EPFL
• Marc Robinson-Rechavi, UniL

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Size of SIB groups
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SIB collaborators
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SIB revenues
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Swiss repartition (2006)
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SIB activities

• The SIB has three missions research
  development, education and service.
• research and development activities related to
  the databases and software developed within the
  Institute.
• Masters degrees of partner universities and
  Swiss doctoral school in Bioinformatics.
• databases of international standing (Swiss-Prot,
  Prosite, EPD, Swiss-2Dpage, Human Chromosome 21,
  TrEST, TrGen, AGBD, Hits, Swiss Model Repository,
  GermOnline).
• software and services that can be accessed from
  the SIB web servers (Melanie, T-COFFEE, PFTOOLS,
  ESTScan, Dotlet, SEView, Snp_detect, Mmsearch,
  Swiss-Model, DeepView/Swiss-PdbViewer, MIMAS).
• services to the Swiss biomedical research
  community within the framework of EMBnet and NCCR
• Together with the Universities of Lausanne,
  Geneva and Basel, the Swiss Federal Institutes of
  Technology of Lausanne (EPFL) and Zurich (EPFZ),
  and three private partners, Hewlett-Packard Inc.,
  Intel Corp. and Oracle, the SIB contributed to
  the creation of a high-performance informatics
  platform (Vital-IT) exclusively dedicated to life
  sciences.

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Scientific Council

• Seven members
• Peer Bork, European Molecular Biology Laboratory,
  Germany.
• Michael Dunn, Conway Institute of Biomolecular
  and Biomedical Research, University College
  Dublin, Ireland.
• Takashi Gojobori, National Institute of Genetics,
  Japan.
• Manolo Gouy, C.N.R.S., Université Claude
  Bernard-Lyon 1, France.
• Wilhelm Gruissem, Institute of Plant Sciences,
  ETH Zentrum, Zürich.
• Thomas Lengauer, Chairman, Max-Planck-Institut
  für Informatik, Germany.
• Christine Orengo, Dept. of Biochemistry
  Molecular Biology, University College London, UK.

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The Computational Biology Challenge

• "In principle, the string of genetic bits holds
  long-sought secrets of human development,
  physiology and medicine. In practice, our ability
  to transform such information into understanding
  remains woefully inadequate".
• The Genome International Sequencing Consortium,
  Initial sequencing and analysis of the human
  genome, Nature 409 860-921 (2001) Emphasis
  added

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Computational Biology Today

• Genome analysis from raw sequence data to fully
  assembled and annotated genomes
• Proteome analysis from mass spectra of complex
  protein mixtures to full identification of their
  components and analysis of their structure
• Expression profiling microarrays, SAGE, MPSS,
  ESTs
• Comparative genomics phylogeny, polymorphisms,
  fingerprinting
• Modelling of macromolecular systems deducing
  properties from atomic interactions
• Modelling of complex systems protein
  interactions, pathways, regulatory networks,
  whole organ models Systems Biology

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Computational Biology needs HPC!

• Problems of scale
• Genomes with millions to billions of nucleotides
• Profiling experiments with tens of thousands of
  data points measured on hundreds or thousands of
  samples
• Thousands of protein mass spectra representing
  GigaBytes of data/experiment
• Problems of complexity
• Combinatorial gt3104 interacting gene products
  can create more functions than there are atoms in
  the Universe
• Structural gt105 dynamically interacting atoms
  make up the smallest of molecular machines

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Life Science ICT Needs
Network
Storage
Computing Speed
Problem
100 Mbps
300 TB
gt 10 TFlops
Genome Assembly
500 Mbps
1s PB
gt 100 TFlops
Protein Structure Prediction
2 Gbps
10s PB
100 TFlops Per DNA-protein interaction
Classical Molecular Dynamics
10 Gbps
100s PB
1 PFlops
First Principle Molecular Dynamics
???
1000s PB
gt1 PFlops
Simulation of Biological Networks
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The Vital-IT Center

• Joint venture between academic and industrial
  partners
• Universities of Lausanne, Geneva and Basel, Swiss
  Federal Inst. of Technology Lausanne, Ludwig
  Institute for Cancer Research
• Hewlett-Packard, Intel Corp. and Oracle
• Managed by the Swiss Institute of Bioinformatics
• An HPC center exclusively dedicated to life
  sciences
• Software development and optimization
• HPC resources for biology and medicine
• Consulting for the life science and health
  industries

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Scope of Vital-IT

• RD projects
• Porting of existing code to Itanium
• Optimisation of code for Itanium architecture
• Adaptation of software to cluster environment
• Ad hoc software development for technology
  platforms
• Infrastructure projects
• Compute engine behind Web interfaces
• Database engine for genomic/proteomic data
• Computational resource for bioinformatics
  research projects
• Providing resources to SwissBioGrid, SystemsX
• Transnational Resource for EU Countries

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Vital-IT in SwissBioGRID

• SwissBioGRID collaboration
• large-scale computational applications in
  bioinformatics, biosimulation, chemoinformatics
  and bio-medical sciences by utilizing distributed
  high-performance computing, high speed networks,
  massive data collections and archives
• CSCS manages GRID infrastructure
• Vital-IT has primary responsibility for providing
  bioinformatics Web services, validation and
  optimization

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Vital-IT in SystemsX

• ETHZ, Uni ZH, UniBS (and others to come)
• CHF 10 mio funding for 2006-07
• Scientific Nodes
• Center of Biosystems
• Competence Center for Systems Physiology
• Center for Model Organism Proteomics
• Institute for Molecular Systems Biology
• Glue Projects (planned)
• Center for Information Sciences and Databases
• Center for Molecular Analysis and Bioinformatics
• Center for Cellular Nano Analytics
• Vital-IT will collaborate to provide core
  computing resources for SystemsX

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Thank you

• THANK YOU
• http//www.isb-sib.ch

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ExPASy server

• Expert Protein Analysis System
• http//www.expasy.org
• Access Statistics January 31, 2006
• Total number of connections since August 1993
• 743605459
• June 2006 (connections)
• 22190251 (approx. 9/sec)
• Mirror sites
• USA, Canada, Australia, China, Brasil

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access to ExPASy
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ExPASy connections / country
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ExPASy connections / country / inhab.
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• THANK YOU

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Thank you !
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The two components of bioinformatics

• macromolecular data banks
• Sequence data banks of DNA (EMBL/GenBank) or
  proteins (Swiss-Prot) genomes (FlyBase),
  3D-structures (PDB), references (Medline), etc
• software tools
• analysis of intrinsic properties of sequences
• comparison of sequences
• analysis and storage of gene expression data
• analysis and storage of proteomics data
• visualization and modeling of 3D-structures

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Genome analysis Philipp Bucher

• Signal search analysis (SSA)
• a method to discover and characterize sequence
  motifs that occur at a constrained distance from
  a physiological site, for instance a
  transcription initiation site.
• The Eukaryotic Promoter Database (EPD)
• a database of experimentally characterized
  eukaryotic promoters (transcription initiation
  site).
• CleanEx a database of heterogeneous gene
  expression data, based on a consistent gene
  nomenclature.
• Provides access to public gene expression data
  via unique gene names.

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Genome AnalysisErik van Nimwegen Biozentrum
U.Basel

• Genome-wide predictions of regulons in bacterial
  genomes, using comparative genomics.
• Identification and prediction of putative
  transcription factor binding sites on a
  genome-wide scale, using significantly conserved
  fragments between promoter regions of orthologous
  genes in related bacterial species.
• Scaling-laws in functional gene-content
• Comparison of the number of genes in different
  functional classes across genomes, ranging from
  the simplest bacteria to human.
• the number of genes in a given functional class
  is related to the total number of genes in the
  genome for a large number of high-level
  functional classes.

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Regulation of gene expressionMihaela Zavolan
Biozentrum, U.Basel

• development of computational methods for
  genome-wide annotation of transcription factor
  binding sites in mammalian genomes
• analysis of the functionality of alternative
  splice forms.
• analyzing mouse, human and rat transcriptomes
• annotation of small RNA sequences obtained
  through large-scale cloning,
• discovery of novel regulatory RNAs
• characterization of the downstream targets of
  miRNAs.

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the Universal Protein Resource UniProtKB

• The past 2 decades have seen the creation of
  Swiss-Prot and TrEMBL operated by researchers
  from the Swiss Institute of Bioinformatics (SIB)
  and the European Bioinformatics Institute (EBI),
• as well as the Protein Information Resource
  operated by the National Biomedical Research
  Foundation (NBRF).
• These groups are combining the strengths of each
  of their databases into a central public
  resource the Universal Protein Resource or
  UniProtKB

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Central Dogma of Molecular Biologyhigh-throughpu
t data production
DNA (Genome)
(Genotype)
DNA sequencing
Alternative Splicing
Transcription
RNA (Transcriptome)
microarrays
Post-translational modifications
Translation
Protein (Proteome)
mass spectrometry
(Phenotype)
Structure Function
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Genome studies

• Signal search analysis (SSA) (P. Bucher)
• Eukaryotic Promoter Database (EPD) (P. Bucher)
• CleanEx a database of heterogeneous gene
  expression data, based on a consistent gene
  nomenclature. (P. Bucher)
• Genome-wide predictions of regulons in bacterial
  genomes, using comparative genomics. (E. van
  Nimwegen)
• Scaling-laws in functional gene-content
• Comparison of the number of genes in different
  functional classes across genomes, ranging from
  the simplest bacteria to human. (E. van Nimwegen)

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Regulation of gene expression (M. Zavolan)

• development of computational methods for
  genome-wide annotation of transcription factor
  binding sites in mammalian genomes
• analysis of the functionality of alternative
  splice forms.
• analyzing mouse, human and rat transcriptomes
• annotation of small RNA sequences obtained
  through large-scale cloning,
• discovery of novel regulatory RNAs
• characterization of the downstream targets of
  miRNAs.

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Gene expression

• Storage and analysis of microarray data (M.
  Delorenzi)
• Discrimination and gene selection methods for
  cancer diagnosis (M. Delorenzi)
• Recognition and prediction of genetic aberrations
  in gene expression data based on a hidden Markov
  model (M. Delorenzi)
• Development of knowledgebases and microarray data
  management/analysis solutions. (M. Primig)
• Expression profiling of gametogenesis in yeast
  and mammals
• Identification of candidate genes for the
  regulation of fertility in mammals by large-scale
  expression profiling
• Development of a novel cross-species and
  subject-oriented approach to genome annotation
  and microarray data management.
• Microarray Data Management and Analysis System
  (MIMAS)

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Computational Systems Biology (F. Naef)

• Multi-dimensional functional data, i.e. from
  expression arrays, open the door to a systems
  level understanding of biological complexity.
• theoretical and computational methodologies for
  studying functional properties and design
  principles of genetic networks, relevant to
  cancer biology.

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Protein Identification using Mass Spectrometry
protein from gel/ PVDF/LC fraction
tryptic digestion peptide extraction
TYGGAAR
EHICLLGK
1-DE, 2-DE, LC
PSTTGVEMFR
GANK
Mass spectrometry, peptide mass fingerprints
PMF identification
unmodified and modified peptides
MS/MS identification
MS Fragmentation
Mass spectrometry, peptide MS fragments
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Protein 3D-structure prediction by homology

• Homology modeling
• Comparative protein modeling
• Knowledge-based modeling
• Using experimental 3D-structures of related
  family members (templates), calculate a model for
  a new sequence (target) Swiss-Model

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Free energy calculations
Cytotoxic T Lymphocyte (CTL) activity against
tumor cells
TCR
Peptide
MHC
X-ray structure of the T cell receptor (TCR)
bound to a peptide MHC complex