AM1- Introduction to Evolutionary and Functional Genomic Analysis
Cristian Castillo-Davis
In this tutorial we cover some basic concepts involved in comparative and functional genomic analysis and learn techniques for analyzing data from a genomic perspective. Attention will be paid to different methods of measuring protein evolution, building evolutionary trees, & determining homology (orthology & paralogy). Integration of evolutionary & genome-scale data with gene nnotations, & microarray & EST data in a statistical framework will be a major focus. In particular, how to use the genomic tool GeneMerge). No programming or biological background is assumed.

AM2- Tandem Mass Spectrometry in Proteomics
Ming Li
Applications of mass spectrometry & tandem mass spectrometry in proteomics, including protein identification, & quantitative analysis will be introduced. The tutorial starts with an intro to different types of mass & tandem mass spectrometers. Then different computational methods for protein identification, including database search & de novo sequencing will be discussed, as well as the Isotope-Coded Affinity Tag (ICAT) analysis for protein quantitation.

AM3- How to Use the Genome Browsers to Get the Most Out of Public Genomes
Daryl Thomas
Bioinformatics is playing an increasingly large role in the study of fundamental biomedical problems due to the explosion of sequence, structural, and functional information available. The challenge will be to analyze such information to reveal previously unknown relationships with respect to gene & protein structure & function. The primary aim of this session is to expose scientists with minimal background in bioinformatics to the methods used for browsing and analyzing the vast amount of publicly available data. The session includes case-driven demonstrations of free, online browsers & the development of custom tracks to display in-house data in the same context. The practical use of these resources will be emphasized through live demonstrations.

AM4- Computational Genetics: Haplotype Inference & Applications in Human Disease Gene Mapping
Tianhua Niu
With the advent of the international HapMap project, there has been a surging interest in statistical challenges in haplotype phasing using genotype data of multiple linked single nucleotide polymorphisms. This tutorial covers: (a) Haplotype inference- using partition-ligation (PL) approach, using both Bayesian & Expectation-Maximization frameworks. Simulated & real-world data compare the performances of various statistical haplotype inferences algorithms. GenoSpectrum (based on probabilistic genotype calls), a new genotype clustering algorithm based on a t-mixture model called GeneScore, as well as a new phasing algorithm, GS-EM that can handle ambiguous genotype data. (b) Linkage Disequilibrium (LD) Analysis- The use of permutation & likelihood ratio tests, logistic regression models, & Bayesian statistical models in performing haplotype-based LD analyses, with applications in preterm delivery, Alzheimer disease, & secondary hyperparathyroidism.

AM5- Introduction to Dynamic Programming (DP) & Its Applications to Bioinformatics
Robert Edgar
DP is a widely used technique in computational biology & is an essential skill for anyone with an interest in algorithm development or maintenance. DP is fundamental to most sequence comparison algorithms, including BLAST, CLUSTALW, whole-genome aligners, hidden Markov models, etc. This tutorial introduces the mathematical & practical aspects of DP with a focus on understanding of the basic concepts. Starting with the most fundamental sequence comparison algorithm, computing the edit distance of a pair of strings, the course will cover global & local alignment (Needleman-Wunsch & Smith-Waterman algorithms), affine gap penalties, hidden Markov models (Viterbi algorithm), profile-profile & multiple alignment, & whole-genome alignment. For each type of algorithm, examples of well-known bioinformatics programs will be discussed, such as BLAST, CLUSTALW, & HMMER, for which source code is available. C programming is required.

PM6- Bioinformatics: The Machine Learning Approach
Pierre Baldi
Machine learning approaches play a significant role in bioinformatics due to the abundance of highly variable data & the lack of comprehensive theories. We will provide an overview of approaches including: Bayesian statistical framework for modeling & induction as the common foundation for all machine learning & data mining algorithms; some of the main model classes- neural networks, hidden Markov models, Bayesian networks & graphical models, stochastic context-free grammars. Examples of specific applications such as: neural networks for the prediction of protein functional sites & secondary & tertiary structure; hidden Markov models of biological sequences for data base searches, multiple alignments, pattern discover, & gene finding.

PM7- Using dChip for Microarray & SNP Chip Data Analysis
Yu Guo
DNA-Chip Analyzer (dChip) is a software package implementing model-based expression analysis of oligonucleotide arrays (Li & Wong 2001a) & several high-level analysis procedures. The model-based approach allows probe-level analysis on multiple arrays. By pooling information across multiple arrays, it’s possible to assess standard errors for the expression indexes. This approach also allows automatic probe selection in the analysis stage to reduce errors due to cross-hybridizing probes & image contamination. High-level analysis in dChip includes comparative analysis & hierarchical clustering. Also see the comparison with Affy MAS software. Topics include brief tutorials on the functions of dchip, & using dchip for microarray & SNP analysis.

PM8- Discovering regulatory networks from gene expression & promoter sequence
Eran Segal
Genomic datasets are rapidly being produced, creating new opportunities for understanding the molecular mechanisms underlying human disease, & studying complex biological processes on a global scale. Transforming these immense amounts of data into biological information is challenging. This tutorial presents a statistical modeling language that addresses this challenge based on Bayesian networks, represents heterogeneous biological entities, & models the mechanism by which they interact. It also covers statistical learning approaches in order to learn the details of these models (structure & parameters) automatically from raw genomic data. Biological insights are derived directly from the learned model.

PM 9- Computational Methods in Phylogenetics
Tandy Warnow
The international systematic biology community is attempting to infer the "Tree of Life", an evolutionary tree (or network) which will contain millions of leaves. An accurate estimation of this history will require novel algorithms since current approaches for phylogenetic reconstruction are not able to provide good analyses on datasets containing thousands of sequences in reasonable time periods. This tutorial will address issues involved in developing approaches that enable highly accurate phylogenetic reconstructions. Specific topics include: Stochastic models of evolution, issues & statistical estimation under these models; major optimization problem- maximum likelihood & parsimony; evaluating methods on real & simulated data; new approaches for getting better solutions to hard optimization problems; open problems.

PM10- From Sequence to Structure: Protein Structure Prediction
Juntao Guo
Knowledge of a protein’s detailed structure holds the key to understanding its biological function. In the post-genomics era the gap between the number of solved protein structures & that of known protein sequences continues to expand due to the long, expensive process required to experimentally determine structures. Computational prediction from amino acid sequences has been successful in providing information for the biological research community & is playing a key role in bridging the gap. This tutorial introduces basic aspects of protein structures & techniques for protein structure prediction. There are 3 major methods for prediction: Comparative modeling, fold recognition, & ab initio prediction. We introduce these methods with an emphasis on threading techniques including: templates, energy functions, threading algorithms & assessments.