ゲノム情報科学研究教育機構  アブストラクト
Date December 10, 2009
Speaker Dr. Ling-Yun Wu, Institute of Applied Mathematics, Chinese Academy of Sciences, China
Title Conditional random patternal gorithm for LOH inference and segmentation
Abstract Loss of heterozygosity (LOH) is one of the most important mechanisms in the tumor evolution. LOH can be detected from the genotypes of the tumor samples with or without paired normal samples. In paired sample cases, LOH detection for informative single nucleotide polymorphisms (SNPs) is straightforward if there is no genotyping error. But genotyping errors are always unavoidable, and there are about 70% non-informative SNPs whose LOH status can only be inferred from the neighboring informative SNPs. We developed a novel LOH inference and segmentation algorithm based on the conditional random pattern (CRP) model. The new model explicitly considers the distance between two neighboring SNPs, as well as the genotyping error rate and the heterozygous rate. This new method is tested on the simulated and real data of the Affymetrix HumanMapping 500K SNParrays. The experimental results show that the CRP method outperforms the conventional methods based on the hidden Markov model (HMM).
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