ISMB'98 Schedule




Saturday, JUNE 27

18:00 - 22:00 Ontologies for MB tutorial

Sunday, JUNE 28

 8:30 - 12:30 Tutorials (including coffee break)
              McClure: Molecular Phylogenetics (4h)
              Guex, Peitsch: Comparative protein modelling (4h)
              Baldi, Brunak: Bioinformatics: The Machine Learning Approach (4h)
              Brazma, Jonassen: Sequence Pattern Discovery Methods (3.5h)
              Bueschking, Schleiermacher: WWW-Based Sequence Analysis (3.5h)

12:30 - 13:30 Lunch (for those who registered to at least one tutorial)

13:30 - 17:30 Tutorials (including coffee break)
              Pearson: Protein Evolution (4h)
              Sankoff: Comparative Genomics (3h)
              Lawrence: Bayesian Inference Algorithms (4h)
              Baldi: Hidden Markov Models (3h)
              Brusic, Zeleznikow: Knowledge Discovery and Data Mining (4h)

18:00 - 20:00 Welcome reception


Monday, JUNE 29

 8:00 -  8:30  Coffee/muffins/fruits

 8:30 -  9:00  Welcome and announcements

 9:00 - 10:00  Invited Speaker Session (Chair J. Glasgow)
               Shoshana Wodak

               Database Derived Potentials For Prediction of Protein Structure And Stability

               Understanding how the amino-acid sequence of proteins determines their
               3D structure, and how both sequence and structure determine function,
               is of far reaching fundamental importance in molecular biology, and
               its many applications, ranging from genomics, to drug and protein
               design, disease control, and many other areas.  Substantial progress
               has been achieved over the last decade in describing the factors that
               govern protein stability and in unraveling the mechanism of protein
               folding. This has renewed the interest in developing methods for
               predicting the 3D structure of protein from their amino acid sequence
               and for simulating protein folding or unfolding in the computer. To
               perform these tasks reliably the methods must embody at least some, if
               not all, of the key features that underlie the true physical
               phenomena. This clearly needs the links between energetics and
               structure to be established. Such links are typically provided by
               molecular mechanics force-fields, which are firmly based on the
               principles of physics. But such force-fields can presently not be used
               to fold proteins on the computer, because they require a detailed
               atomic representation of the system that involves an astronomical
               number of degrees of freedom and present day computers are unable to
               explore those efficiently enough.  Devising force-fields in terms of
               reduced descriptions of the protein conformation has therefore been a
               recurring theme. It received new impetus recently with the realization
               that the body of protein sequences and structure data has probably
               reached a sufficient size to derive from it 'effective potentials',
               which provide an intermediate description between those given by
               detailed atomic force-fields on the one hand, and single residue
               specific secondary structure propensities on the other. In recent
               years an impressive number of studies has been devoted to this issue.
               Here we will present a brief overview of these developments with
               emphasis on potentials derived from statistical analyses of known
               protein structures. We will start by examining some of the current
               thinking about the links between protein energetics and structure, in
               order to define the key issues that the derived potentials should be
               able to address. To follow, some of the most common types of
               potentials will be described and their performance will be illustrated
               using examples from work performed in our laboratory. These examples
               will include the structure prediction of short peptides and early
               folding regions in proteins, fold recognition procedures using
               sequence-structure screening methods, and the evaluation of changes in
               protein stability caused by mutations. Factors that may limit the
               performance of the potentials and perspectives for future development
               will be discussed.


10:00 - 10:30  Coffee Break

10:30 - 12:30  Session 1 - Structure and Folding (Chair J. Glasgow)

               Genetic Algorithms for Protein Threading
	         J. Yadgari, A. Amir, R. Unger

               Hierarchical Minimization with Distance and Angle Constraints
                 J.R. Gunn

               Modeling Protein Homopolymeric Repeats: Possible Poly Glutamine
               Structural Motifs for Huntington's Disease
	         R.H. Lathrop, M. Casale, D.J. Tobias, J.L. Marsh, L.M. Thompson

	       A Surface Measure for Probabilistic Structural Computations
	         J.P. Schmidt, C.C. Chen, J.L. Cooper and R.B. Altman

12:30 - 14:00  Lunch

14:00 - 16:00  Session 2 - Hidden Markov Models (Chair F. Major)

	       A Hidden Markov Model for Predicting Transmembrane Helices
	       in Protein Sequences
	         E.L.L. Sonnhammer, G. von Heijne, A. Krogh

               Prediction of Signal Peptides and Signal Anchors by
	       a Hidden Markov Model
	         H. Nielsen and A. Krogh

               Identification of Divergent Functions in Homologous
	       Proteins by Induction over Conserved Modules
	         I. Shah, L. Hunter

               Computational Applications of DNA Physical Scales
	         P. Baldi, S. Brunak, Y, Chauvin, A.G. Pedersen

16:30 - 18:00  Poster and Demo Session/Reception


Tuesday, JUNE 30

 8:30 -  9:00  Coffee/muffins/fruits

 9:00 - 10:00  Invited Talk Session (Chair R. Lathrop)
               Michael Waterman

	       Constructing Restriction Maps

               The now-classical double-digest approach for mapping a
               region of DNA is a source of interesting mathematical and statistical
               problems.  The problem is NP-Hard even if there are no measurement
               errors.  Never-the-less by 1987 the entire genome of {\it E. coli} was
               mapped with eight enzymes.  A fundamentally new molecular biology
               approach to constructing restriction maps, {\em Optical Mapping}, has
               been developed by Schwartz et al. (1993), which can rapidly produce
               ordered restriction maps of single DNA molecules by fluorescence
               microscopy.  This approach to restriction mapping also suggests
               interesting computational problems.  However, it is difficult to
               estimate directly the restriction site locations of single DNA
               molecules based on these optical mapping data because of the precision
               of length measurements and the unknown number of true restriction
               sites in the data.  Thus this approach to restriction mapping also
               suggests interesting computational problems.  We discuss the use of a
               hierarchical Bayes model based on a mixture model with normals and
               random noise.  In this model we explicitly consider the missing
               observation structure of the data, such as the orientations of
               molecules, the allocations of cutting sites to restriction sites, and
               the indicator variables of whether observed cut sites are true or
               false.  Because of the complexity of the model, the large number of
               missing data, and the unknown number of restriction sites, we use
               Reversible-Jump Markov Chain Monte Carlo (MCMC) to estimate the number
               and the locations of the restriction sites.  Since there exists a high
               multimodality due to unknown orientations of molecules, we also use a
               combination of our MCMC approach and the flipping algorithm suggested
               by our earlier E-M maximum likelihood methods.  The study is highly
               computer-intensive and the development of an efficient algorithm is
               required.

10:00 - 10:30 Coffee break

10:30 - 12:30 Session 3  Bioinformatic Systems (Chair R. Lathrop)

              The LabFlow System for Workflow Management in Large Scale
              Biology Research Laboratories
	        N. Goodman, S. Rozen, L.D. Stein

	      BioSim - A New Qualitative Simulation Environment for Molecular
              Biology
                K.R. Heidtke, S. Schulze-Kremer

              TAMBIS: Transparent Access to Multiple Bioinformatics
              Information Sources. An Overview
                P.G. Baker, A. Brass, S. Bechhofer, C. Goble, N. Paton,
                R. Stevens
  
              A Computational System for Modelling Flexible
              Protein-protein and Protein-DNA Docking
	        M.J.E. Sternberg, P. Aloy, H.A. Gabb, R.M. Jackson, G. Moont,
                E. Querol, F.X. Aviles

12:30 - 14:00  Lunch (on your own)

14:00 - 15:30  Session 4  Sequence Alignment and Analysis  (Chair C. Sensen)

               Sequence Assembly Validation by Multiple Restriction
               Digest Fragment Coverage Analysis
                 E.C. Rouchka, D.J. States

               A Map of the Proteins Space - An automatic hierarchical
               classification of all known proteins
	         G. Yona, N. Linial, N. Tishby, M. Linial

               A Statistical Theory of Sequence Alignment with Gaps
                 D. Drasdo, T. Hwa and M. Lassig

15:30 - 16:00  Coffee break

16:00 - 17:30  Session 5  Biological Databases (Chair T. Gaasterland)

               Advanced Query Mechanisms for Biological Databases
                 I-M. A. Chen, A.S. Kosky, V.M. Markowitz, E. Szeto,
                 T. Topaloglou

               IMGT/LIGM-DB: A Systematized Approach for ImMunoGeneTics
               Database Coherence and Data Distribution Improvement
                 V. Giudicelli, D. Chaume, M-P. Lefranc

               Automated clustering and assembly of large EST collections
                 D.P. Yee, D. Conklin

18:30 -        Boat Cruise/Banquet
               (Presentation of Best Paper and Poster Presentation Awards)
                 M. Peitsch


Wednesday, JULY 1

 8:30 -  9:00  Coffee/muffins/fruits

 9:00 - 10:00  Invited Talk Session (Chair D. Sankoff)
               Robert Cedergren

               Fishing for Function in RNA Form and Features

               The recognition, definition and assignment of RNA structure and
               function have been enigmatic in genomic data, since 1) there is no
               method to identify RNA genes, 2) the RNA four-letter alphabet renders
               the prediction of RNA structures from sequences highly degenerate and
               3) the three-dimensional structure and function of few RNA molecules
               and/or families have been fully characterized.  Using RNAMOT, a search
               engine developed in our laboratory to integrate secondary and tertiary
               structural information and other tools, we have scanned the GenBank
               database to ascertain the distribution of some well-known RNA
               functional motifs.  Among these, are those involved in protein binding
               (Tat and Rev), those having a chemical activity (the catalytic
               hammerhead and leadzyme motif, the UV-loop) and aptamers binding small
               molecules (amino glycosides, etc).  All motifs have occurrences
               approaching their probability of existence in random sequences
               suggesting that the origin of RNA motifs in the database is due to
               evolutionary drift of sequences.  Occasional, unusual distributions
               may reflect the opportunistic use of the motif for a particular
               function in a new context.  Such may be the case where viruses
               associated with the etiology of HIV have a Tat-binding in their RNA
               which could provide a means of communication between these viruses.
               Whether a given RNA motif can have function within the context of
               database (and out of the known context) has been evaluated in the case
               of a hammerhead motif found in the repetitive DNA of Schistosomes.
               Here, we show that indeed this hammerhead motif is active in vitro and
               in vivo.  Of particular interest is the fact that the candidate,
               substrate gene for this catalytic motif is potentially a critical gene
               whose modulation by the ribozyme could have major effects on these
               cells.  (supported by Natural Science and Engineering Research Council
               of CANADA).

10:00 - 10:30  Coffee break

10:30 - 12:30  Session 6  Sequence Alignment and Analysis  (Chair T. Littlejohn)

               Bayesian Protein Family Classifier
                 K. Qu. L.A. McCue, C.E. Lawrence

               Compression of Strings with Approximate Repeats
                 L. Allison, T. Edgoose, T.I. Dix

               Segment-based Scores for Pairwise and Multiple Sequence
               Alignments
                 B. Morgenstern, W.R. Atchley, K. Hahn, A. Dress

               Calculating the Exact Probability of Language-like
               Patterns in Biomolecular Sequences
                 K. Atteson

12:30 - 14:00  Lunch (on your own)

14:00 - 15:30  Session 7  Translation and Evolution (Chair M. Pietsch)

               Genexpress: A Computer System for Description, ANalysis and
               Recognition of Regulatory Sequences in Eukaryotic Genome
                 N.A. Kolchanov, M.P. Ponomarenko, A.E. Kel, Yu.V. Kondrakhin,
                 A.S. Frolov, F.A. Kolpakov, O.V. Kel, E.A. Ananko,
                 E.V. Ignatieva, O.A. Podkolodnaya, I.L. Stepanenko,
                 T.I. Merkulova, V.N. Babenko, D.G. Vorobiev,
                 S.V. Lavyushev, Yu.V. Ponomarenko, A.V. Kochetov,
                 G.V. Kolesov, N.L. Podkolodny, L. Milanesi,
                 E. Wingender, T. Heinemeyer, V.V. Solovyev

               Phylogenetic Inference in Protein Superfamilies: Analysis
               of SH2 Domains
                 K. Sjolander

               The Ribosome Scanning Model for Translation Initiation:
               Implications for Gene Prediction and Full-Length cDNA
               Detection
                 P. Agarwal and V. Bafna

15:30 -        Closing Remarks



--- July 1 to 12 ---
Festival International de Jazz de Montreal


** Best paper contest