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Workshop

Short Course I: 8:30-11:50am, 2:00-5:20pm, Saturday, June 9, 2007

Analyzing Microarray Gene Expression Data

Instructor: Zhaoying Xiang, M.D., Director of Microarray Core Facility, Associate Research Professor, Weill Medical College of Cornell University, New York, NY, USA

Associate Instructor: Yaning Yang, Ph.D., Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui, China

Course Descriptions

The study of gene expression utilizing microarrays allows scientists to survey the expression of thousands of genes simultaneously under different conditions. Microarray technologies support genetic mapping studies and mutational analyses as well as genome-wide monitoring of gene expressions, and are becoming increasingly more common tools in research and clinical applications. In the past few years, this high resolution and high through-put technology has made a significant impact on biomedical research field, and has offered considerable biological insights into the function of biological system underlying pathological conditions.

This Microarray Data Analysis workshop presents a comprehensive overview of the methods and tools available for high-throughput microarray data analysis, and introduces biomedical researchers to microarray 1-color and 2-color workflows using a representative software, GeneSpring, for gene expression applications. Topics covered include: data import, normalization, quality control methods, statistics, fold-change analyses and biological reference. Participants of this course will learn about platform-specific normalizations, sample and gene level quality control, when and how to use statistical analyses and how to integrate gene expression data with known biological annotations from GO and public and proprietary pathway databases.

Short Course II: 8:30-11:50am, 2:00-5:20pm, Sunday, June 10, 2007

A Tutorial on Computational Biology

Instructor: Jun Liu, Professor, Department of Statistics, Harvard University

Outline

1. Basic molecular biology, mostly around Central Dogma

  1. Basic building blocks: DNA, RNA and Protein
  2. Cell, cell division, and cell growth
  3. Genetic code, splicing and protein structure
  4. Transcription regulation and gene expression

2. Biological Sequence analysis

  1. Pairwise: Smith-Waterman, BLAST, BLAT, FASTA, etc.
  2. Multiple sequence alignment: ClustalW, HMM, etc.
  3. Motif-based sequence alignment: Gibbs sampler, BLOCKS
  4. Gene structure and prediction
  5. Statistical models used in sequence analysis

3. Transcription regulation

  1. TF motif finding: word / EM / Gibbs sampler
  2. Basic statistical modeling and computation
  3. Markov chain Monte Carlo and other computational methods
  4. ChIP-on-chip, MDscan and Motif Regressor
  5. Methods for inferring transcription networks and modules
  6. Statistical transcription module model
  7. Comparative genomics approach

4. Predicting gene expression from sequence information

  1. Gene expression and Microarrays
  2. Supervised learning and unsupervised learning
  3. Sequence feature extractions and variable selections
  4. Accommodating nonlinear relationships

5. Proteins

  1. Protein structure prediction: homology modeling, threading, ab initio
  2. HP models for protein folding and Monte Carlo strategies

6. Genetic Epidemiology

  1. Genotypes and Haplotype Inference
  2. Linkage Analysis (Parametric and Non-parametric)
  3. Association Analyses (Family-Based and Population based)
  4. Gene-Gene, Gene-Environment Interaction