Institut Suisse de Bioinformatique Schweizerisches Institut für Bioinformatik Swiss Institute of...

Institut Suisse de BioinformatiqueSchweizerisches Institut für Bioinformatik

Swiss Institute of Bioinformatics

The Swiss Institute of BioinformaticsThe Swiss Institute of Bioinformatics

Ernest FeytmansErnest FeytmansDirectorDirector



SIB activitiesSIB activities

The SIB The SIB brings Swiss experts in bioinformatics brings Swiss experts in bioinformatics togethertogether and and provides high quality servicesprovides high quality services to the to the national and international scientific community. national and international scientific community.

The SIB is a privileged The SIB is a privileged partner of Swiss Universitiespartner of Swiss Universities

Members of the SIB include research groups in Members of the SIB include research groups in Geneva, Lausanne, Basel and Zurich.Geneva, Lausanne, Basel and Zurich.

The SIB participates in The SIB participates in Master’s degreesMaster’s degrees of partner of partner universities and organises a universities and organises a doctoral schooldoctoral school in in Bioinformatics.Bioinformatics.



The SIB in SwitzerlandThe SIB in Switzerland



Group LeadersGroup Leaders 12 membres12 membres

Ron AppelRon Appel, , Proteome Informatics, UniGEProteome Informatics, UniGE Amos BairochAmos Bairoch, , Swiss-Prot, UniGESwiss-Prot, UniGE Bastien ChopardBastien Chopard, Computer simulations, UniGE, Computer simulations, UniGE Philipp BucherPhilipp Bucher, , Computational Cancer Genomics, ISRECComputational Cancer Genomics, ISREC Mauro DelorenziMauro Delorenzi, , Bioinformatics Core Facility, ISRECBioinformatics Core Facility, ISREC Félix NaefFélix Naef, , Computational Sytems Biology, ISRECComputational Sytems Biology, ISREC C. Victor JongeneelC. Victor Jongeneel, , Vital-IT et Transcriptome Analysis, LICRVital-IT et Transcriptome Analysis, LICR Olivier MichielinOlivier Michielin, , Molecular Modeling, UniL et LICRMolecular Modeling, UniL et LICR Michael PrimigMichael Primig, , Genome Bioinformatics, UniBasGenome Bioinformatics, UniBas Torsten SchwedeTorsten Schwede, , Protein Structure Bioinformatics, UniBasProtein Structure Bioinformatics, UniBas Erik van NimwegenErik van Nimwegen, , Genome Systems Biology, UniBasGenome Systems Biology, UniBas Mihaela ZavolanMihaela Zavolan, , RNA Regulatory Networks, UniBasRNA Regulatory Networks, UniBas

Gaston Gonnet, EPFZGaston Gonnet, EPFZ Joerg Stelling, EPFZJoerg Stelling, EPFZ Evgeny Zdobnov, UniGEEvgeny Zdobnov, UniGE Bernard Moret, EPFLBernard Moret, EPFL Marc Robinson-Rechavi, UniLMarc Robinson-Rechavi, UniL Sven Bergmann, UniLSven Bergmann, UniL



Size of SIB groupsSize of SIB groups

SIB groups

80

17

12 11 11 11 108 8 7 6

3 3 3

10

5 5 5 53

0

20

40

60

80

AB

RA

MD

TS

MP

PB

OM

EV

N

Vita

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VJ

MZ

BC

EM

Bne

t

FN

GG

MR

R

JS EZ

BM SB

groups

num

ber

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190

33



SIB collaboratorsSIB collaboratorsEvolution of the number of collaborators

(in FTE on SIB payroll)

1.02.3

3.34.4

6.3 6.67.9 8.1

0.5

4.4

19.4

28.4

37.7

44.8

54.0

58.2

0.7

4.9

9.7

12.2

16.2

12.4 11.6

0.8

4.7

7.79.9

14.1 15.0

1.5

6.9 7.5 8.0

0.0

10.0

20.0

30.0

40.0

50.0

60.0

1998 1999 2000 2001 2002 2003 2004 2005

Year

# co

llab

ora

tors

(F

TE

)

Administration Swiss-Prot PIG Lausanne Bâle



SIB revenuesSIB revenuesEvolution of SIB revenues

3.1 3.1

7.2

5.0

6.15.9

1.92.0 2.0

2.6 2.6

4.1

5.7

0.3 0.3 0.1

1.1 1.1 1.1

0.6

2.2

1.82.0

0.9

1.51.4

1.5

0.8 0.7

1.21.6

0.0

2.0

4.0

6.0

8.0

1998 1999 2000 2001 2002 2003 2004 2005

Year

Su

bsi

die

s (m

illio

ns

CH

F)

Produits de Services Confédération Recherche NIH Divers services



Swiss repartition (2006)Swiss repartition (2006)

16%

47%

30%

7%

Basel

Geneva

Lausanne

Zurich



SIB activitiesSIB activities The SIB has three missions: The SIB has three missions: research & development, education research & development, education

and serviceand service..

research and development activitiesresearch and development activities related to the databases related to the databases and software developed within the Institute. and software developed within the Institute.

Master’s degreesMaster’s degrees of partner universities and of partner universities and SwissSwiss doctoral doctoral schoolschool in Bioinformatics. in Bioinformatics.

databasesdatabases of international standing (Swiss-Prot, Prosite, EPD, of international standing (Swiss-Prot, Prosite, EPD, Swiss-2Dpage, Human Chromosome 21, TrEST, TrGen, AGBD, Hits, Swiss-2Dpage, Human Chromosome 21, TrEST, TrGen, AGBD, Hits, Swiss Model Repository, GermOnline). Swiss Model Repository, GermOnline). software and servicessoftware and services that can be accessed from the SIB web that can be accessed from the SIB web servers (Melanie, T-COFFEE, PFTOOLS, ESTScan, Dotlet, SEView, servers (Melanie, T-COFFEE, PFTOOLS, ESTScan, Dotlet, SEView, Snp_detect, Mmsearch, Swiss-Model, DeepView/Swiss-PdbViewer, Snp_detect, Mmsearch, Swiss-Model, DeepView/Swiss-PdbViewer, MIMAS). MIMAS). servicesservices to the Swiss biomedical research community within the to the Swiss biomedical research community within the framework of EMBnet and NCCRframework of EMBnet and NCCRTogether with the Together with the Universities of Lausanne, Geneva and Basel, the Universities of Lausanne, Geneva and Basel, the Swiss Federal Institutes of Technology of Lausanne (EPFL) and Swiss Federal Institutes of Technology of Lausanne (EPFL) and Zurich (EPFZ), and three private partners, Hewlett-Packard Inc., Zurich (EPFZ), and three private partners, Hewlett-Packard Inc., Intel Corp. and OracleIntel Corp. and Oracle, the SIB contributed to the creation of a , the SIB contributed to the creation of a high-high-performance informatics platform (Vital-IT) exclusively performance informatics platform (Vital-IT) exclusively dedicated to life sciences.dedicated to life sciences.



Scientific CouncilScientific Council

Seven membersSeven members

Peer BorkPeer Bork, , European Molecular Biology Laboratory, GermanyEuropean Molecular Biology Laboratory, Germany.. Michael DunnMichael Dunn, , Conway Institute of Biomolecular and Conway Institute of Biomolecular and

Biomedical Research, University College Dublin, IrelandBiomedical Research, University College Dublin, Ireland.. Takashi GojoboriTakashi Gojobori, , National Institute of Genetics, JapanNational Institute of Genetics, Japan.. Manolo GouyManolo Gouy, , C.N.R.S., Université Claude Bernard-Lyon 1, C.N.R.S., Université Claude Bernard-Lyon 1,

FranceFrance.. Wilhelm GruissemWilhelm Gruissem, , Institute of Plant Sciences, ETH Zentrum, Institute of Plant Sciences, ETH Zentrum,

ZürichZürich.. Thomas LengauerThomas Lengauer, , ChairmanChairman,, Max-Planck-Institut für Max-Planck-Institut für

Informatik, GermanyInformatik, Germany.. Christine OrengoChristine Orengo, , Dept. of Biochemistry & Molecular Biology, Dept. of Biochemistry & Molecular Biology,

University College London, UKUniversity College London, UK..



The Computational Biology ChallengeThe Computational Biology Challenge

"In principle, the string of genetic bits "In principle, the string of genetic bits holds long-sought secrets of human holds long-sought secrets of human development, physiology and development, physiology and medicine. In practice, medicine. In practice, our ability to our ability to transform such information into transform such information into understandingunderstanding remains woefully remains woefully inadequateinadequate".". The Genome International Sequencing Consortium, ”Initial sequencing and analysis of the human genome,” The Genome International Sequencing Consortium, ”Initial sequencing and analysis of the human genome,” NatureNature 409 409: :

860-921 (2001) [Emphasis added]860-921 (2001) [Emphasis added]



Computational Biology TodayComputational Biology Today

GenomeGenome analysis – from raw sequence data to fully analysis – from raw sequence data to fully assembled and annotated genomesassembled and annotated genomes

ProteomeProteome analysis – from mass spectra of complex analysis – from mass spectra of complex protein mixtures to full identification of their protein mixtures to full identification of their components and analysis of their structurecomponents and analysis of their structure

ExpressionExpression profilingprofiling – microarrays, SAGE, MPSS, ESTs – microarrays, SAGE, MPSS, ESTs Comparative genomicsComparative genomics – phylogeny, polymorphisms, – phylogeny, polymorphisms,

fingerprintingfingerprinting Modelling of Modelling of macromolecular systemsmacromolecular systems – deducing – deducing

properties from atomic interactionsproperties from atomic interactions Modelling of Modelling of complex systemscomplex systems – protein interactions, – protein interactions,

pathways, regulatory networks, whole organ models: pathways, regulatory networks, whole organ models: “Systems Biology”“Systems Biology”



Computational Biology needs HPC!Computational Biology needs HPC!

Problems of scaleProblems of scale Genomes with millions to billions of nucleotidesGenomes with millions to billions of nucleotides Profiling experiments with tens of thousands of data Profiling experiments with tens of thousands of data

points measured on hundreds or thousands of samplespoints measured on hundreds or thousands of samples Thousands of protein mass spectra representing Thousands of protein mass spectra representing

GigaBytes of data/experimentGigaBytes of data/experiment Problems of complexityProblems of complexity

Combinatorial: >3Combinatorial: >3××101044 interacting gene products can interacting gene products can create more functions than there are atoms in the create more functions than there are atoms in the UniverseUniverse

Structural: >10Structural: >1055 dynamically interacting atoms make up dynamically interacting atoms make up the smallest of molecular machinesthe smallest of molecular machines



NetworkNetworkStorageStorageComputing Computing

SpeedSpeedProblemProblem

10 Gbps10 Gbps100s PB100s PB1 PFlops 1 PFlops First Principle Molecular First Principle Molecular DynamicsDynamics

??????1000s 1000s PBPB

>1 PFlops>1 PFlopsSimulation of Biological Simulation of Biological NetworksNetworks

2 Gbps2 Gbps10s PB10s PB100 TFlops 100 TFlops Per DNA-protein Per DNA-protein

interactioninteraction

Classical Molecular Classical Molecular DynamicsDynamics

500 500 MbpsMbps

1s PB1s PB> 100 > 100 TFlopsTFlops

Protein Structure Protein Structure PredictionPrediction

100 100 MbpsMbps

300 TB300 TB> 10 TFlops> 10 TFlopsGenome AssemblyGenome Assembly

Life Science ICT NeedsLife Science ICT Needs



The Vital-IT CenterThe Vital-IT Center

Joint venture between academic and industrial Joint venture between academic and industrial partnerspartners Universities of Lausanne, Geneva and Basel, Swiss Federal Universities of Lausanne, Geneva and Basel, Swiss Federal

Inst. of Technology Lausanne, Ludwig Institute for Cancer Inst. of Technology Lausanne, Ludwig Institute for Cancer ResearchResearch

Hewlett-Packard, Intel Corp. and OracleHewlett-Packard, Intel Corp. and Oracle Managed by the Swiss Institute of BioinformaticsManaged by the Swiss Institute of Bioinformatics

An HPC center An HPC center exclusivelyexclusively dedicated to life sciencesdedicated to life sciences Software development and optimizationSoftware development and optimization HPC resources for biology and medicineHPC resources for biology and medicine Consulting for the life science and health industriesConsulting for the life science and health industries



R&D projectsR&D projects Porting of existing code to ItaniumPorting of existing code to Itanium®®

Optimisation of code for Itanium architectureOptimisation of code for Itanium architecture Adaptation of software to cluster environmentAdaptation of software to cluster environment Ad hocAd hoc software development for technology platforms software development for technology platforms

Infrastructure projectsInfrastructure projects Compute engine behind Web interfacesCompute engine behind Web interfaces Database engine for genomic/proteomic dataDatabase engine for genomic/proteomic data Computational resource for bioinformatics research Computational resource for bioinformatics research

projectsprojects Providing resources to Providing resources to SwissBioGridSwissBioGrid, , SystemsXSystemsX Transnational ResourceTransnational Resource for EU Countries for EU Countries

Scope of Scope of Vital-ITVital-IT



Vital-ITVital-IT in in SwissBioGRIDSwissBioGRID

SwissBioGRID collaborationSwissBioGRID collaboration

large-scale computational applications in bioinformatics, large-scale computational applications in bioinformatics, biosimulation, chemoinformatics and bio-medical sciences biosimulation, chemoinformatics and bio-medical sciences by utilizing distributed high-performance computing, high by utilizing distributed high-performance computing, high speed networks, massive data collections and archives speed networks, massive data collections and archives

CSCS manages GRID infrastructureCSCS manages GRID infrastructure Vital-IT has primary responsibility for providing Vital-IT has primary responsibility for providing

bioinformatics bioinformatics Web services, validation and Web services, validation and optimizationoptimization

Virtual Screening of Dengue Virus Target Proteins



Vital-ITVital-IT in in SystemsXSystemsX

ETHZ, Uni ZH, UniBS (and others to come…)ETHZ, Uni ZH, UniBS (and others to come…) CHF 10 mio funding for 2006-07CHF 10 mio funding for 2006-07 Scientific Nodes Scientific Nodes

Center of Biosystems Center of Biosystems Competence Center for Systems Physiology Competence Center for Systems Physiology Center for Model Organism Proteomics Center for Model Organism Proteomics Institute for Molecular Systems Biology Institute for Molecular Systems Biology

Glue Projects (planned)Glue Projects (planned) Center for Information Sciences and Databases Center for Information Sciences and Databases Center for Molecular Analysis and Bioinformatics Center for Molecular Analysis and Bioinformatics Center for Cellular Nano Analytics Center for Cellular Nano Analytics

Vital-IT will collaborate to provide core computing Vital-IT will collaborate to provide core computing resources for resources for SystemsXSystemsX



Thank you…Thank you…

THANK YOUTHANK YOU

http://www.isb-sib.chhttp://www.isb-sib.ch



ExPASy serverExPASy server

ExExpert pert PProtein rotein AAnalysis nalysis SySystemstem http://www.expasy.orghttp://www.expasy.org

Access Statistics Access Statistics January 31, 2006January 31, 2006 Total number of connectionsTotal number of connections since August 1993:since August 1993:

743’605’459743’605’459 June 2006June 2006 (connections) (connections)

22’190’25122’190’251 (approx. 9/sec)(approx. 9/sec)

Mirror sites:Mirror sites: USA, Canada, Australia, China, BrasilUSA, Canada, Australia, China, Brasil



access to ExPASyaccess to ExPASy

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ExPASy connections / countryExPASy connections / country

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THANK YOUTHANK YOU



Thank you !Thank you !



The two components of The two components of bioinformaticsbioinformatics

macromolecular data banksmacromolecular data banks Sequence data banks of Sequence data banks of DNADNA (EMBL/GenBank) or (EMBL/GenBank) or

proteinsproteins (Swiss-Prot); (Swiss-Prot); genomesgenomes (FlyBase), (FlyBase), 3D-3D-structuresstructures (PDB), (PDB), referencesreferences (Medline), etc; (Medline), etc;

software toolssoftware tools analysis of intrinsic properties of sequencesanalysis of intrinsic properties of sequences comparison of sequencescomparison of sequences analysis and storage of gene expression dataanalysis and storage of gene expression data analysis and storage of proteomics data analysis and storage of proteomics data visualization and modeling of 3D-structuresvisualization and modeling of 3D-structures



Genome analysis Genome analysis Philipp Philipp BucherBucher

Signal search analysis (SSA) Signal search analysis (SSA) a method to discover and characterize sequence a method to discover and characterize sequence

motifs that occur at a constrained distance from a motifs that occur at a constrained distance from a physiological site, for instance a transcription physiological site, for instance a transcription initiation site. initiation site.

The Eukaryotic Promoter Database (EPD)The Eukaryotic Promoter Database (EPD) a database of experimentally characterized a database of experimentally characterized

eukaryotic promoters (transcription initiation site). eukaryotic promoters (transcription initiation site).

CleanEx: a database of heterogeneous gene CleanEx: a database of heterogeneous gene expression data, based on a consistent gene expression data, based on a consistent gene nomenclature. nomenclature. Provides access to public gene expression data Provides access to public gene expression data

via unique gene names.via unique gene names.



Genome AnalysisGenome AnalysisErik van Nimwegen – Biozentrum U.BaselErik van Nimwegen – Biozentrum U.Basel

Genome-wide predictions of regulons in bacterial Genome-wide predictions of regulons in bacterial genomes, using comparative genomicsgenomes, using comparative genomics.. Identification and prediction of putative transcription factor Identification and prediction of putative transcription factor

binding sites on a genome-wide scale, using significantly binding sites on a genome-wide scale, using significantly conserved fragments between promoter regions of conserved fragments between promoter regions of orthologous genes in related bacterial speciesorthologous genes in related bacterial species..

Scaling-laws in functional gene-contentScaling-laws in functional gene-content Comparison of Comparison of the number of genes in different functional the number of genes in different functional

classes across genomesclasses across genomes, ranging from the simplest , ranging from the simplest bacteria to human. bacteria to human.

the number of genes in a given functional class is related the number of genes in a given functional class is related to the total number of genes in the genome for a large to the total number of genes in the genome for a large number of high-level functional classes. number of high-level functional classes.



Regulation of gene expressionRegulation of gene expressionMihaela Zavolan – Biozentrum, U.BaselMihaela Zavolan – Biozentrum, U.Basel

development of computational methods for genome-development of computational methods for genome-wide annotation of wide annotation of transcription factor binding sitestranscription factor binding sites in mammalian genomesin mammalian genomes

analysis of the functionality of analysis of the functionality of alternative splice alternative splice

formsforms.. analyzing mouse, human and rat transcriptomes analyzing mouse, human and rat transcriptomes

annotation of annotation of small RNA sequencessmall RNA sequences obtained through obtained through large-scale cloning, large-scale cloning,

discovery of novel discovery of novel regulatory RNAsregulatory RNAs

characterization of the downstream characterization of the downstream targets of targets of miRNAsmiRNAs. .



the Universal Protein Resource: the Universal Protein Resource: UniProtKBUniProtKB

The past 2 decades have seen the creation of The past 2 decades have seen the creation of Swiss-ProtSwiss-Prot and and TrEMBLTrEMBL operated by researchers operated by researchers from the from the Swiss Institute of Bioinformatics (SIB)Swiss Institute of Bioinformatics (SIB) and and the European Bioinformatics Institute (EBI),the European Bioinformatics Institute (EBI),

as well as the as well as the Protein Information ResourceProtein Information Resource operated by the operated by the National Biomedical Research National Biomedical Research Foundation (NBRF).Foundation (NBRF).

These groups are combining the strengths of These groups are combining the strengths of each of their databases into a central public each of their databases into a central public resource: the Universal Protein Resource or resource: the Universal Protein Resource or UniProtKBUniProtKB;;



AUUUUAUUAAUAUAAUUCUUAAUAAUUAUAAAAAAAAAAAAAAAAAAACAACCAAAAUAUUAUAUUAUUUUUUGUAAAUAUAAAUUAAAAAAAUGAAAUUCCAACAUACAUUUAUUGCAUUAUUAUCACUAUUAACAUAUGCAAAUGCAUAUGAUUAUUUCACAACAACAUUGGCCAAUCAAAAUCCAGUUUGUGCUUCAGUAGAUGUCAUACAAAAUGUUUGUACUGAGGUUUGUGGUAGAUUUGUCCGUUACAUUCCUGAUGCUACCAAUACAAAUCAAUUCACUUUUGCCGAAUAUACUACAAACCAAUGUACUGUUCAAGUAACACCAGCUGUAACAAAUACAUUUACUUGCGCUGAUCAAACUUC

ATTTTATTAATATAATTCTTAATAATTATAAAAAAAAAAAAAAAAAAACAACCAAAATATTATATTATTTTTTGTAAATATAAATTAAAAAAATGAAATTCCAACATACATTTATTGCATTATTATCACTATTAACATATGCAAATGCATATGATTATTTCACAACAACATTGGCCAATCAAAATCCAGTTTGTGCTTCAGTAGATGTCATACAAAATGTTTGTACTGAGGTTTGTGGTAGATTTGTCCGTTACATTCCTGATGCTACCAATACAAATCAATTCACTTTTGCCGAATATACTACAAACCAATGTACTGTTCAAGTAACACCAGCTGTAACAAATACATTTACTTGCGCTGATCAAACTTCTTCACATGCACTCGGTTCTGATTGGAGTGGAGTTTGTAAAATCACTGCCACACCCGCACCAACAGTTACACCAACAGTTACACCAACAGTTACACCAACAGTAACACCAACACCAACAAATACACCAAATCCAACCCCGTCCCAAACATCAACAACAACTGGTTCTGCTTCAACTGTTGTTGCAAGTTTATCACTCATTATTTTCTCAATGATTCTTTCCCTTTGTTAATTTTAAAAATAACAAATGGTAAAAATTATTTGGCATTTTCAAGTACAATTTTAAATTTTTCAAAAAAAAATAAAATTAAATAAATAATTAAATAAATTAATTAATAAAAAAAATAAAATTAAAACTTTTTTAATTTA

DNA (Genome)

RNA (Transcriptome)

Protein (Proteome)

Structure & “Function”

(Genotype)

(Phenotype)

microarrays

Transcription

TranslationPost-translationalmodifications

MetLysPheGlnHisThrPheIleAlaLeuLeuSerLeuLeuThrTyrAlaAsnAlaTyrAspTyrPheThrThrThrLeuAlaAsnGlnAsnProValCysAlaSerValAspValIleGlnAsnValCysThrGluValCysGlyArgPheValArgTyrIleProAspAlaThrAsnThrAsnGlnPheThrPheAlaGluTyrThrThrAsnGlnCysThrValGlnValThrProAlaValThrAsnThrPheThrCysAlaAspGlnThrSerSerHisAlaLeuGlySerAspTrpSerGlyValCysLysIleThrAlaThrProAlaProThrValThrProThrValThrProThrValThrProThrValThrProThrProThrAsnThrProAsnProThrProSerGlnThrSerThrThrThrGlySerAlaSerThrValValAlaSerLeuSerLeuIleIlePheSerMetIleLeuSerLeuCysEnd

Alternative Splicing

Central Dogma of Molecular Biology:Central Dogma of Molecular Biology:high-throughput data productionhigh-throughput data production

mass spectrometry

DNA sequencing



Genome studiesGenome studies

Signal search analysis (SSA) Signal search analysis (SSA) (P. Bucher)(P. Bucher)

Eukaryotic Promoter Database (EPD) Eukaryotic Promoter Database (EPD) (P. Bucher)(P. Bucher)

CleanEx: a database of heterogeneous gene expression data, CleanEx: a database of heterogeneous gene expression data, based on a consistent gene nomenclature. based on a consistent gene nomenclature. (P. Bucher)(P. Bucher)

Genome-wide predictions of regulons in bacterial genomes, Genome-wide predictions of regulons in bacterial genomes, using comparative genomics. using comparative genomics. (E. van Nimwegen)(E. van Nimwegen)

Scaling-laws in functional gene-content:Scaling-laws in functional gene-content: Comparison of Comparison of the number of genes in different functional classes the number of genes in different functional classes

across genomesacross genomes, ranging from the simplest bacteria to human. , ranging from the simplest bacteria to human. (E. (E. van Nimwegen)van Nimwegen)



Regulation of gene expression Regulation of gene expression (M. (M. Zavolan)Zavolan)

development of computational methods for genome-development of computational methods for genome-wide annotation of wide annotation of transcription factor binding sitestranscription factor binding sites in mammalian genomes in mammalian genomes

analysis of the functionality of analysis of the functionality of alternative splice alternative splice

formsforms.. analyzing mouse, human and rat transcriptomes analyzing mouse, human and rat transcriptomes

annotation of annotation of small RNA sequencessmall RNA sequences obtained through obtained through large-scale cloning,large-scale cloning,

discovery of novel discovery of novel regulatory RNAsregulatory RNAs

characterization of the downstream characterization of the downstream targets of targets of miRNAsmiRNAs..



Gene expressionGene expression

Storage and analysis of microarray data Storage and analysis of microarray data (M. Delorenzi)(M. Delorenzi)

Discrimination and gene selection methods for cancer diagnosis Discrimination and gene selection methods for cancer diagnosis (M. Delorenzi)(M. Delorenzi)

Recognition and prediction of genetic aberrations in gene Recognition and prediction of genetic aberrations in gene expression data based on a hidden Markov model expression data based on a hidden Markov model (M. Delorenzi)(M. Delorenzi)

Development of Development of knowledgebases and microarray data knowledgebases and microarray data management/analysismanagement/analysis solutions. solutions. (M. Primig)(M. Primig)

Expression profiling of gametogenesisExpression profiling of gametogenesis in yeast and mammals in yeast and mammals Identification of Identification of candidate genes for the regulation of fertilitycandidate genes for the regulation of fertility in in

mammals by large-scale expression profilingmammals by large-scale expression profiling Development of a novel cross-species and subject-oriented Development of a novel cross-species and subject-oriented

approach to approach to genome annotation and microarray data managementgenome annotation and microarray data management.. Microarray Data Management and Analysis SystemMicroarray Data Management and Analysis System (MIMAS) (MIMAS)



Computational Systems Biology Computational Systems Biology (F. Naef)(F. Naef)

Multi-dimensional functional data, i.e. Multi-dimensional functional data, i.e. from expression arrays, open the door from expression arrays, open the door to a to a systems level understanding of systems level understanding of biological complexitybiological complexity. .

theoretical and computational theoretical and computational methodologies for studying functional methodologies for studying functional properties and design principles of properties and design principles of genetic networksgenetic networks, relevant to , relevant to cancer cancer biologybiology..



Protein Identification using Mass Protein Identification using Mass SpectrometrySpectrometry

protein from gel/PVDF/LC fraction tryptic digestion &

peptide extraction

PMF identificationMass spectrometry,

peptide mass fingerprints

TYGGAARPSTTGVEMFR

EHICLLGKGANK

unmodified and modified peptides

1-DE, 2-DE, LC

MS/MS identification

Mass spectrometry,peptide MS fragments

MS Fragmentation



Protein 3D-structure prediction by Protein 3D-structure prediction by homologyhomology

Homology modelingHomology modeling = Comparative protein modeling = Comparative protein modeling = Knowledge-based modeling= Knowledge-based modeling

Using experimental 3D-structures of related family Using experimental 3D-structures of related family members (templates), calculate a model for a new members (templates), calculate a model for a new sequence (target): sequence (target): Swiss-ModelSwiss-Model



Free energy calculations Free energy calculations

Cytotoxic T Lymphocyte (CTL) Cytotoxic T Lymphocyte (CTL) activity against tumor cellsactivity against tumor cells

X-ray structure of the T cell receptor (TCR) bound to apeptide – MHC complex

TCR

Peptide

MHC

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