>Motivation: 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.>Results: This article presents 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 Human Mapping 500K SNP arrays. The experimental results show that the CRP method outperforms the conventional methods based on the hidden Markov model (HMM).>Availability: Software is available upon request.>Contact: >Supplementary information: are available at Bioinformatics online.
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机译：>动机：杂合性缺失（LOH）是肿瘤进化中最重要的机制之一。可以从有或没有配对正常样品的肿瘤样品的基因型中检测出LOH。在成对的样本中，如果不存在基因分型错误，则LOH检测可用于信息性单核苷酸多态性（SNP）。但是基因分型错误总是不可避免的，并且大约有70％的非信息性SNP只能从邻近的信息性SNP推断出LOH状态。>结果：本文提出了一种新颖的基于LOH推理和分割的算法在条件随机模式（CRP）模型上。新模型明确考虑了两个相邻SNP之间的距离，以及基因分型错误率和杂合率。此新方法已在Affymetrix Human Mapping 500K SNP阵列的模拟和真实数据上进行了测试。实验结果表明，CRP方法优于基于隐马尔可夫模型（HMM）的常规方法。>可用性：可根据要求提供软件。>联系方式： >补充信息：可从生物信息学在线获得。
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