99thth International Conference on International Conference on Intelligent System for Molecular Intelligent System for Molecular

BiologyBiology

Tivoli Gardens, Copenhagen, DenmarkTivoli Gardens, Copenhagen, Denmark

July 19-26, 2001 July 19-26, 2001

Park, Ji-YoonPark, Ji-Yoon

Satellite Meetings(July 19-20)Satellite Meetings(July 19-20)

July 19July 19

- The Open Source author’s contract : Steven Brenner - The Open Source author’s contract : Steven Brenner

- BioJava project report: Thomas Down and Matthew Pocock - BioJava project report: Thomas Down and Matthew Pocock

- Biopython project report: Andrew Dalke- Biopython project report: Andrew Dalke

- BioCORBA project report: Jason Stajich- BioCORBA project report: Jason Stajich

- biok: Catherine Letondal- biok: Catherine Letondal

- Lightning Talks:- Lightning Talks:

•• OMG’s new Model Driven Architecture(MDA): Scott MarkelOMG’s new Model Driven Architecture(MDA): Scott Markel

•• BioRuby: Yoshinori Okuju, Toshiaki Katayama, and Mitsuteru NakaoBioRuby: Yoshinori Okuju, Toshiaki Katayama, and Mitsuteru Nakao

•• Genquire: David BlockGenquire: David Block

•• Generation and use of substitution matrices in Biopython: IDDo Friedberg Generation and use of substitution matrices in Biopython: IDDo Friedberg

and Brad Chapmanand Brad Chapman

Satellite Meetings(July 19-20)Satellite Meetings(July 19-20)

July 20July 20 : Biopathway : Biopathway - Bioperl project report: Hilmar Lapp- Bioperl project report: Hilmar Lapp - EnsEMBL project report: Arne Stabenau- EnsEMBL project report: Arne Stabenau - Lightning Talks: - Lightning Talks:

• • Bioperl-project: Ewan BirneyBioperl-project: Ewan Birney

• • OpenBSA: Juha Muilu, Martin Senger and Alan RobinsonOpenBSA: Juha Muilu, Martin Senger and Alan Robinson

• • Genetic algorithm and neural network libraries: Brad ChapmanGenetic algorithm and neural network libraries: Brad Chapman

•• Mining gene expression information using a controlled hierarchical vocabularyMining gene expression information using a controlled hierarchical vocabulary

: Peter van heusden: Peter van heusden

•• TFBS: Perl modules for transcription factor detection and analysis: Boris Lenhard TFBS: Perl modules for transcription factor detection and analysis: Boris Lenhard

- A tool suite for the Gene Ontology: Chris Mungall, Hohn Richter, Bradley Marshall, and Suzann- A tool suite for the Gene Ontology: Chris Mungall, Hohn Richter, Bradley Marshall, and Suzanna Lewis a Lewis

- DeCAL: A system for constructing comparative maps: Debra Goldberg, Jon Kleinberg, and Susa- DeCAL: A system for constructing comparative maps: Debra Goldberg, Jon Kleinberg, and Susan McCouch n McCouch

Tutorial( July 21)Tutorial( July 21)

* * Morning Turorials: Sat July 21 8:30-12:30Morning Turorials: Sat July 21 8:30-12:30

[Statistical analysis of micro-arrays studies][Statistical analysis of micro-arrays studies]

: Emmanuel Lazaridis, : Emmanuel Lazaridis, University of South FloridaUniversity of South Florida

Afternoon Turorials: Sat July 21 14:00-18:00Afternoon Turorials: Sat July 21 14:00-18:00

[Network genomics][Network genomics]

: Christian Forst, : Christian Forst, Los Alamos National LaboratoryLos Alamos National Laboratory

Sequence motifs, alignments and families: Sequence motifs, alignments and families: July 22 July 22

[Keynote: Protein folding, molecular evolution, and human disease][Keynote: Protein folding, molecular evolution, and human disease]

: Christopher M. Dobson, : Christopher M. Dobson, University of Oxford University of Oxford

►►PProtein misfolding in diseaserotein misfolding in disease

►►misfolded polypeptide misfolded polypeptide →→ folded protein folded protein

↓ ↓

↓ ↓ misfolding misfolding ↓↓

↓ ↓

improper trafficking toxic folding degradationimproper trafficking toxic folding degradation

►►Asp67His: Amyloid formation(; aggregation) Asp67His: Amyloid formation(; aggregation)

►►SH3 domain of PI3 kinase: Cross-SH3 domain of PI3 kinase: Cross- structure structure

Sequence motifs, alignments and families: Sequence motifs, alignments and families: July 22 July 22

** An insight into domain combinations An insight into domain combinations

* Prediction of the coupling specificity of G protein coupled * Prediction of the coupling specificity of G protein coupled

receptors to their G proteinsreceptors to their G proteins

* Improved prediction of the number of residue contacts in * Improved prediction of the number of residue contacts in

proteins by recurrent neural networksproteins by recurrent neural networks

* Non-symmetric score matrices and the detection of homologous * Non-symmetric score matrices and the detection of homologous

transmembrane proteins transmembrane proteins

* Generating protein interaction maps from incomplete data: * Generating protein interaction maps from incomplete data:

application to fold assignmentapplication to fold assignment

Sequence motifs, alignments and families: Sequence motifs, alignments and families: July 22July 22[Keynote - Structural Genomics] [Keynote - Structural Genomics]

- Christopher M. Dobson, - Christopher M. Dobson, University of OxfordUniversity of Oxford

- Goal : All protein domains carry all functional families - Goal : All protein domains carry all functional families

How many experimental structure? How many experimental structure?

- Coordination of international programs in structural genomics - Coordination of international programs in structural genomics

- Pathways in expression profile- Pathways in expression profile

* 0j-py: a software tool for low complexity proteins and protein domains * 0j-py: a software tool for low complexity proteins and protein domains

: Michael J. Wise, : Michael J. Wise, Centre for Communications Systems ResearchCentre for Communications Systems Research

→ → new tool for looking peptide new tool for looking peptide

* Separating real motifs from their artifacts* Separating real motifs from their artifacts

* Feature selection for DNA methylation based cancer classification* Feature selection for DNA methylation based cancer classification

* An algorithm for finding signals of unknown length in DNA sequences* An algorithm for finding signals of unknown length in DNA sequences

Networks and Modeling: July 23Networks and Modeling: July 23

[Keynote- Protein Interactions][Keynote- Protein Interactions] : David Eisenberg, : David Eisenberg, University of California, LosAngelesUniversity of California, LosAngeles • • Rossetta StoneRossetta Stone - - Fusion of functionally-linked domainFusion of functionally-linked domain - - http:// dip.doe-mbi.ucla.eduhttp:// dip.doe-mbi.ucla.edu • • Phylogenic profilePhylogenic profile - correlated occurrence of pairs of proteins in genomes- correlated occurrence of pairs of proteins in genomes • • Gene functionGene function • • Database interacting proteinDatabase interacting protein • • 33D D domain swappingdomain swapping • • Signaling path Signaling path

Networks and Modeling: July 23Networks and Modeling: July 23

<<Protein-protein interaction map inference using interacting domain profile Protein-protein interaction map inference using interacting domain profile pairs> pairs>

: Jerome Wojcik, Vincent Schachter, : Jerome Wojcik, Vincent Schachter, Hybrigenics S.AHybrigenics S.A ►► Computational prediction of protein network Computational prediction of protein network ► ► IDPP(Interacting domain profile pair) methodIDPP(Interacting domain profile pair) method •• Interacting domain cluster = verticsInteracting domain cluster = vertics •• Interacting domain profile pair = edge Interacting domain profile pair = edge ► ►Assessment of predictive accuracy: reference data problemAssessment of predictive accuracy: reference data problem

* * Inferring subnetworks from perturbed expression profilesInferring subnetworks from perturbed expression profiles

: http:// www.cs.huji.ac.il/labs/combio: http:// www.cs.huji.ac.il/labs/combio * Molecular classification of multiple tumor types* Molecular classification of multiple tumor types : http:// geone.wi.mit.edu/MPR: http:// geone.wi.mit.edu/MPR * Centralization: a new method for the normalization of gene expression data* Centralization: a new method for the normalization of gene expression data

Networks and Modeling: July 23Networks and Modeling: July 23

[ Centralization: a new method for the normalization of gene expression data ][ Centralization: a new method for the normalization of gene expression data ]

* Housekeeping approach is questionable. * Housekeeping approach is questionable.

* Basic assumption* Basic assumption

; roughly the gene level expression no preffered direct of regulation ; roughly the gene level expression no preffered direct of regulation

- real data: differently & strongly regulation- real data: differently & strongly regulation

- total RNA vary: different cell state/tissue- total RNA vary: different cell state/tissue

- different factors are summed- different factors are summed

- only subset of all gene measured - only subset of all gene measured

- strongly expressed gene be regulated: main protein product - strongly expressed gene be regulated: main protein product

* Advantage* Advantage

- more robus on real data- more robus on real data

- inexpensive alternative experiment- inexpensive alternative experiment

- Easy - Easy

Keynote- The phenomenon of the web: David Eisenberg, Keynote- The phenomenon of the web: David Eisenberg, University of CaliforUniversity of California, Los Angelesnia, Los Angeles

Networks and Modeling: July 23Networks and Modeling: July 23

[Keynote- The phenomenon of the web][Keynote- The phenomenon of the web]

: David Eisenberg, : David Eisenberg, University of California, Los AngelesUniversity of California, Los Angeles

http: // rana.lbl.govhttp: // rana.lbl.gov

http:// genome-www.standford. edu./microarray http:// genome-www.standford. edu./microarray

Gene structure, Regulation, and Gene structure, Regulation, and Modeling: July 24Modeling: July 24

* Keynote- The Modern RNA world: many genes don’t encode protein* Keynote- The Modern RNA world: many genes don’t encode proteinss

: Sean Eddy, : Sean Eddy, Washington UniversityWashington University* Promoter prediction in the human genome* Promoter prediction in the human genome* Joint modeling of DNA sequence and physical properties to improve * Joint modeling of DNA sequence and physical properties to improve

eukaryotic promoter recognitioneukaryotic promoter recognition* Computational expansion of genetic networks* Computational expansion of genetic networks* GENIES: a natural-language processing systems for the extraction of * GENIES: a natural-language processing systems for the extraction of

molecular pathways from journal articles molecular pathways from journal articles * Designing better phages* Designing better phages* Computational Analysis of RNA splicing* Computational Analysis of RNA splicing* Disambiguating proteins, genes, and RNA in text: a machine learning * Disambiguating proteins, genes, and RNA in text: a machine learning

approachapproach* Gene recognition based on DAG shortest paths* Gene recognition based on DAG shortest paths* An efficient algorithm for finding short approximate non-tandem rep* An efficient algorithm for finding short approximate non-tandem rep

eatseats

Methods: July 25Methods: July 25

* Keynote- Membrane proteins: From the computer to the bench and * Keynote- Membrane proteins: From the computer to the bench and back: Gunnar von Heijne, back: Gunnar von Heijne, Stockholm UniversityStockholm University

* Design of a compartmentalized shotgun assembler for the human gen* Design of a compartmentalized shotgun assembler for the human genomeome

* Probabilistic approaches to the use of higher order clone relationship* Probabilistic approaches to the use of higher order clone relationships in physical map assembly: s in physical map assembly:

* Fragment assembly with double-barreled data* Fragment assembly with double-barreled data

* SCOPE: a probabilistic model for scoring tandem mass spectra again* SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide databasest a peptide database

* Probe selection algorithms with applications in the analysis of microb* Probe selection algorithms with applications in the analysis of microbial communitiesial communities

* Fast optimal leaf ordering for hierarchical clustering* Fast optimal leaf ordering for hierarchical clustering

* Separation of samples into their constituents using gene expression d* Separation of samples into their constituents using gene expression dataata

WEB: July 26WEB: July 26

* Education in Bioinformatics: Current Trends and Issues.* Education in Bioinformatics: Current Trends and Issues.

- Shoba Ranganathan- Shoba Ranganathan

* Opening Addresss: Bioinformatics Education* Opening Addresss: Bioinformatics Education

- Looking to the Future: Russ Altman- Looking to the Future: Russ Altman

* The S* Life Science Informatics Alliance* The S* Life Science Informatics Alliance

- Shoba Ranganathan- Shoba Ranganathan

* Bioinformatics BS at the Univerisity of California, Santa Cruz* Bioinformatics BS at the Univerisity of California, Santa Cruz

- Kevin Karplus- Kevin Karplus

* A Masters Degree in Bioinformatics in Switzerland* A Masters Degree in Bioinformatics in Switzerland

- Patricia Palagi- Patricia Palagi

* Emerging US & UK Standards for Graduate Bioinformatics trainin* Emerging US & UK Standards for Graduate Bioinformatics trainingg

- Linda Ellis- Linda Ellis

WEB: July 26WEB: July 26

* Bioinformatics Course Delivery: Tools and Infrastructure. Siv Ander* Bioinformatics Course Delivery: Tools and Infrastructure. Siv Andersson(Chair)sson(Chair)

* Bioinformatics: Introducing the concept of “ evaluation-based” learn* Bioinformatics: Introducing the concept of “ evaluation-based” learning : Siv Anderssoning : Siv Andersson

* Problem-oriented sequence analysis tool: Ueng-Cheng Yang* Problem-oriented sequence analysis tool: Ueng-Cheng Yang

* EMBER- A European Multimedia Bioinformatics Educational Resou* EMBER- A European Multimedia Bioinformatics Educational Resource: C. Victor Jongeneelrce: C. Victor Jongeneel

* Virtual Reality and Visualization for Bioinformatics Education: YY * Virtual Reality and Visualization for Bioinformatics Education: YY CaiCai

* Starting a new Bioinformatics Program. Phyllis Gardner(Chair)* Starting a new Bioinformatics Program. Phyllis Gardner(Chair)

* Initiating a multi-disciplinary, trans-institutional program: A Dean’* Initiating a multi-disciplinary, trans-institutional program: A Dean’s perspective: Phyllis Gardners perspective: Phyllis Gardner

* Insights into starting a new Multi-disciplinary program: Betty Chen* Insights into starting a new Multi-disciplinary program: Betty Chengg

WEB: July 26WEB: July 26

* Bioinformatics Training. Frederique Galisson(Chair)* Bioinformatics Training. Frederique Galisson(Chair)

* The Canadian Bioinformatics Workshops: Stephen Herst* The Canadian Bioinformatics Workshops: Stephen Herst

* The BioNavigator Education Package- resources for practical instruc* The BioNavigator Education Package- resources for practical instruction in bioinformatics: Bruno A. Gaetation in bioinformatics: Bruno A. Gaeta

* The Human Genome Mapping Project Resources Centre- Encouragi* The Human Genome Mapping Project Resources Centre- Encouraging Bioinformatics Awareness: Lisa Mullanng Bioinformatics Awareness: Lisa Mullan

* Panel Discussion: Betty Cheng(Chair)* Panel Discussion: Betty Cheng(Chair)

The S* Life Science Informatics Alliance: Question Time: S* TeamThe S* Life Science Informatics Alliance: Question Time: S* Team

* Concluding Session: Closing Remarks. Shoba Ranganathan(Chair)* Concluding Session: Closing Remarks. Shoba Ranganathan(Chair)

Bioinformatics Education: Future trends and perspectives: Philip BoBioinformatics Education: Future trends and perspectives: Philip Bourneurne

Free Energy(Free Energy( ∆ G )∆ G )

Thermodynamic constant that gives the amount of energy required fThermodynamic constant that gives the amount of energy required for or released by a reaction or or released by a reaction

- kcal/mol - kcal/mol

- Reaction that- Reaction that require require energy ; energy ; positivepositive

- Reation that - Reation that releaserelease free energy ; free energy ; negative negative

- - Energy must be released overall to form a base-paired structure Energy must be released overall to form a base-paired structure

- The stability of the structure is determined by the amount of energ- The stability of the structure is determined by the amount of energy released y released

Hairpin StructureHairpin Structure

The Overall Free Energy of a double-The Overall Free Energy of a double-stranded structurestranded structure

∆ ∆ G G totaltotal = ∆ G = ∆ G ii + ∑ ∆ G + ∑ ∆ Gxx + ∆ ∑ G + ∆ ∑ Guu

∆ ∆ GGi : i : the free energy for initiation of a double helix the free energy for initiation of a double helix

Positive value: + 3.4 kcal/molPositive value: + 3.4 kcal/mol

It applied to It applied to intermolecular duplex formationintermolecular duplex formation

∑ ∆ ∑ ∆ GGxx:: the sum of the individual reactions involved in propagating the the sum of the individual reactions involved in propagating the

double helix as each base pair is added double helix as each base pair is added the formation of each base pairthe formation of each base pair releases releases energy ; energy ; negative negative

∑∆∑∆GGuu: the sum of individual instances encountered as the double helix is: the sum of individual instances encountered as the double helix is

propagated in which the opposing bases are not complementary propagated in which the opposing bases are not complementary the energy required to hold these bases in an unpaired statethe energy required to hold these bases in an unpaired state ; ; positive positive

The Free Energy of formation for a The Free Energy of formation for a potential base-paired regionpotential base-paired region

Free Energy of Base PairingFree Energy of Base Pairing