Title: Next Generation Sequencing Data AnalysisShort abstract: The advent of next-generation sequencing (NGS) provides great opportunities for many biological applications, including resequencing, variation discovery and typing, transcriptome and protein-DNA interaction. However, analysing NGS data is computationally challenging. This tutorial will cover several important topics in NGS data analysis: mapping, peak detection, SNP and structure variation discovery.
Co- presenter:Professor Kun Huang
The Ohio State University, Department of Biomedical Informatics
Rm.3190 Graves Hall, 333 West 10th Avenue, Columbus, OH43210
1-614-292-5607 (work) 1-614-596-2471 (cell)
5+ years teaching experience, courses taught: Introduction to
Bioinformatics, Biomedical Informatics I, Biomedical Informatics II Invited talks on 'Comparative Analysis of ChIP-seq Data and Parallel Computing Strategies' for Mapping Massive Sequences to Genome at the conference of Critical Assessment of Massive Data Analysis (CAMDA), Chicago, 2009.
Profile of Prof Kun Huang
Prof. Kun Huang's major research interests include systems biology with a focus on developing new computational tools and algorithms for analysing large biomedical data including gene expression microarray, ChIP-seq and RNA-seq data. His group has developed a data processing pipeline for the Next Generation sequencing data including parallel sequence tag mapping, data visualization, comparison, peak detection and management. Prof. Kun Huang has more than years of teaching experience and has taught graduate level course of bioinformatics at different levels. He has given Invited talks on 'Comparative Analysis of ChIP-seq Data' and 'Parallel Computing Strategies for Mapping Massive Sequences to Genome' at CAMDA in 2009. Professor Jing Li
Case Western Reserve University
10900 Euclid Ave, Cleveland, OH 44106
5+ years teaching experience, courses taught: introduction to bioinformatics and computational biology, advanced topics in computational biology, algorithms and data structure, theoretical computer science
Profile of Prof Jing Li
Dr. Jing Li currently is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Case Western Reserve University. He received a B.S. in Statistics from Peking University, Beijing, China in July 1995, and Ph.D. in Computer Science from University of California - Riverside in June 2004. Prof. Jing Li's major research interests include computational analysis of human population variation data. He has extensive experiences in analysing SNP data, structure variation data. Currently, his research group is actively working on next generation sequencing data analysis. Prof. Li has more than 5 years teaching experiences. He has taught both graduate and undergraduate level courses, including introduction to bioinformatics and computational biology, advanced topics in computational biology, algorithms and data structure, theoretical computer science.