学术报告
报告题目：Copy number variation detection via normalizing high-throughput sequencing data at a nucleotide level
报告人：席瑞斌 研究员(北京大学数学科学学院）
报告时间：2012年9月25日（星期二）下午13:00 PM
报告地点：北京大学定量生物学中心（原理论生物学中心）老化学楼东配楼101报告厅（理教路西/老光华楼北侧）
摘要：
Copy number variation (CNV) is a major class of variations in the human genome, which has been associated with a wide spectrum of human diseases such as cancer, schizophrenia and autoimmune diseases. In recent years, the advancement of high-throughput sequencing (HTS) technologies has provided an opportunity for CNV detection with unprecedented resolution. Based on HTS data, a number of CNV detection algorithms have been developed. Since HTS data contains various types of biases, these algorithms usually have a bias correction step. However, these bias correction methods are often large bin-based and the resolution of these algorithms is heavily restricted by the bin size. Here, we developed an algorithm that can normalize HTS data at a nucleotide level as well as a CNV detection algorithm based on the normalized HTS data that can detect CNVs with base-pair level resolution. Simulation and real data analysis shows that this algorithm can effectively remove the biases and accurately call CNVs.
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