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Signal processing for next generation sequencing data
Date: 2015/4/3             Browse: 756

Speaker:Prof. Kun Huang

Time: Apr. 3, 10:30-11:30am

Location: Room 220, Building 8, Yueyang Road Campus


In the past decade, next generation sequencing (NGS) technologies have revolutionized biomedical research in many aspects. While NGS allows biologists to study the sequences of DNA and RNA genomewide, often the analyses become a problem of studying the patterns of distributions of the sequenced short reads over the genome. For instance, in gene expression regulation and epigenetics, ChIP-seq has become the standard technique given its genome wide coverage and high accuracy. However, the analysis of ChIP-seq data usually involves peak detection and pattern analysis, which are typical signal processing problems. In this talk, I will give an overview of the various signal processing problems and pattern recognition approaches that have been applied to analyze ChIP-seq data which resulted in interesting and important scientific discoveries as well as new biological hypothesis. Nevertheless, similar problems also exist in other types of NGS data such as RNA-seq and whole genome sequencing. In addition to the examples, I will discuss various sources of public data. It is clear that advances signal processing methods can solve challenging biological problems and lead to more important discoveries beyond the current analysis capacity, which is an appealing opportunity for engineering students to engage in exciting interdisciplinary research.


Dr. Kun Huang received his BS degree in Biological Sciences from Tsinghua University in 1996 and his MS degrees in Physiology, Electrical Engineering and Mathematics all from the University of Illinois at Urbana-Champaign (UIUC). He then received his PhD in Electrical and Computer Engineering from UIUC in 2004 with a focus on computer vision and machine learning. Currently he is an Associate Professor in the Department of Biomedical Informatics at The Ohio State University (OSU). His research interests include bioinformatics, computational biology, bioimage informatics, and machine learning. He has co-authored more than 130 papers.                                                                                                                                           

                      SIST-Seminar 15002