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Morning Session
Tutorial 1: Strategies for Discovering and Interpreting Regulatory DNA Sequence Motifs
Ernest Fraenkel, MIT, USA

Tutorial 2:
CANCELLED

Afternoon Session
Tutorial 3: Gene and Protein Networks
Debra Goldberg and Todd Gibson, University of Colorado, USA

Tutorial 4: Immunological Bioinformatics, Epitope Discovery and Vaccine Design
Morten Nielsen and Claus Lundegaard, Center for Biological Sequence Analysis, Technical University of Denmark


Tutorial 1: Strategies for Discovering and Interpreting Regulatory DNA Sequence Motifs
Organizer:
Ernest Fraenkel, MIT, USA


The differential expression of genes depends on regulatory proteins that recognize and bind to short DNA sequences. Many computational strategies have been developed to discover sequence features, called motifs, that represent the binding preferences of these proteins. This tutorial reviews general strategies for regulatory motif discovery and describes how these strategies are best applied to real data. The tutorial will present recent advances in motif discovery technology that use multiple sources of information to aid the motif search. In addition, it will discuss techniques for associating motifs with particular regulatory proteins.


Tutorial 2: Association Mapping: Fundamental Principles and Applications
Organizer:
Jotun Hein, Oxford University, UK
Thomas Mailund, Oxford University, UK, and University of Aarhus, DK


The goal of this tutorial is to introduce the field of Association Mapping/Linkage Disequilibrium (LD) mapping, useful for the genetic mapping of disease risk factors using association studies of case-control design. Participants will be introduced to modern theories of genealogical processes and models of genome evolution which, coupled with the development of powerful statistical tools, have significantly enhanced our abilities to detect signals in this type of studies. Coalescent-based methods of data analysis will be compared to standard statistical tools.

Concerns regarding putative pitfalls such as population structure will be addressed and solutions proposed. Design issues will also be discussed. The motivation for this tutorial is the growing interest in large-scale association studies, both from academic institutions, hospitals and industry, resulting from the increased knowledge about human genomic polymorphisms and technological advances allowing effective typing of polymorphisms in large numbers of individuals.


Tutorial 3: Gene and Protein Networks
Organizers:
Debra Goldberg and Todd Gibson, University of Colorado, USA


Large-scale experimental data identifying metabolic, regulatory, and other relationships between genes and proteins are pervasive. These relationships are increasingly modeled as networks. Evaluating the importance of various evolutionary mechanisms such as gene duplication, identifying conserved gene functions across multiple species, and inferring the function of unlabeled genes are just a few of the tasks benefiting from network analysis. This tutorial introduces the concepts, models, methods and pitfalls of genomic network analysis, and will benefit anyone interested in using molecular relationships to model evolution, predict protein function, or answer other bioinformatic questions.


Tutorial 4: Immunological Bioinformatics, Epitope Discovery and Vaccine Design
Organizers:
Morten Nielsen and Claus Lundegaard, Center for Biological Sequence Analysis, Technical University of Denmark


Immunological bioinformatics is a relatively novel research field, highly relevant in investigations of the complex behavior of the immune system. The immune system reacts to foreign protein segments called epitopes, and a major task is to develop methods identifying epitopes in pathogen proteomes. Predicted epitopes can eventually be verified and used in designing a vaccine. The tutorial will demonstrate how bioinformatics and high throughput screenings are used in the search of vaccine candidates against pathogens like Flu, Tuberculosis and HIV. The methods used are artificial neural networks, Gibbs sampling, weight matrices and hidden Markov models as well as integrative models and protein structural analysis.