Introduction to the course

Bioinformatics aims to understanding biological mechanisms by the statistical and computational processing of biological data and information. Dr. Jake Y. Chen will give five day's lectures, who comes from Indiana University School of Informatics & Purdue University Computer and Information Science Department, Indianapolis, IN, USA.

The purpopose of this short course is to provide students with an overview of modern bioinformatics Fundamental biological concepts such as sequence-structure-function relationship, gene expression and regulation, protein expression, and biological networks will be explained. Modern analytical platforms such as cDNA microarrays, LC-MS/MS mass spectrometry, and yeast 2-hybrid to measure biological states in large scale will be described. Computational techniques to manage and interpret these biological data, including computational optimization algorithms, machine learning methods, network computing, and visualization technqiues, will be explained in the post-genome biology context. Case studies from literature that make use of bioinformatics concepts and techniques to advance bioloigical knowledge discovery will be presented. The format of the course is a mixture of lectures, hands-on lab practices, and discussions.

Target audience

Senior undergraduates with solid biology (BIO), computer science (CS), or other quantitative science background; graduate students working in the area of computational biology, bioinformatics, or biocomputing; professionals or faculty members interested in the field of bioinformatics.

Dates and Location

June 2-6th, 2008, Kun Ming University, Yunan Province, China

Location: coming soon ...

Course structure

3-4 hours of lecture in the morning of each day

2 hours of discussion/lab (instructor-guided or self-guided) in the afternoon.

Preliminary Schedule

Day 1: Overview: Basic molecular biology and biocomputing concepts
BIO	molecular biology basics
CS	Biological Knowledge Discovery framework, sensitivity/specificity
Lab	Bioperl/Oracle basic biological data management
Day 2: Sequence-Structure Analysis and Comparative Genomics
BIO	Sequence, sequence variation, protein structure, genome organization
CS	Dynamic programming, optimization techniques
Lab	DNA Sequence and protein structure analysis
Day 3: Gene Expression Analysis and Functional Genomics
BIO	Gene Expression and Regulation, cDNA microarrays
CS	normalization, multiple hypothesis testing, clustering
Lab	clustering analysis of cDNA microarrays
Day 4: Proteomics and Biomarker Discoveries
BIO	mass-spectrometry LC/MS-MS, panel biomarkers
CS	sensitivity/specificity, classification
Lab	Mass Spectrometry data analysis
Day 5: Network/Pathway Analysis and Systems Biology
BIO	Various biological networks, signaling pathways
CS	Bayesian Methods, network analysis
Lab	Integrated molecular Network Visualization