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dChipSNP:基于SNP阵列的杂合性缺失数据的显著性曲线和聚类

dChipSNP: significance curve and clustering of SNP-array-based loss-of-heterozygosity data.

作者信息

Lin Ming, Wei Lee-Jen, Sellers William R, Lieberfarb Marshall, Wong Wing Hung, Li Cheng

机构信息

Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.

出版信息

Bioinformatics. 2004 May 22;20(8):1233-40. doi: 10.1093/bioinformatics/bth069. Epub 2004 Feb 10.

Abstract

MOTIVATION

Oligonucleotide microarrays allow genotyping of thousands of single-nucleotide polymorphisms (SNPs) in parallel. Recently, this technology has been applied to loss-of-heterozygosity (LOH) analysis of paired normal and tumor samples. However, methods and software for analyzing such data are not fully developed.

RESULT

Here, we report automated methods for pooling SNP array replicates to make LOH calls, visualizing SNP and LOH data along chromosomes in the context of genes and cytobands, making statistical inference to identify shared LOH regions, clustering samples based on LOH profiles and correlating the clustering results to clinical variables. Application of these methods to prostate and breast cancer datasets generates biologically important results.

AVAILABILITY

The software module dChipSNP implementing these methods is available at http://biosun1.harvard.edu/complab/dchip/snp/

SUPPLEMENTARY INFORMATION

The breast cancer data are provided by Andrea L. Richardson, Zhigang C. Wang and James D. Iglehart.

摘要

动机

寡核苷酸微阵列可并行对数千个单核苷酸多态性(SNP)进行基因分型。最近,这项技术已应用于配对的正常和肿瘤样本的杂合性缺失(LOH)分析。然而,用于分析此类数据的方法和软件尚未完全开发出来。

结果

在此,我们报告了用于合并SNP阵列重复数据以进行LOH调用的自动化方法,在基因和细胞带的背景下沿染色体可视化SNP和LOH数据,进行统计推断以识别共享的LOH区域,根据LOH谱对样本进行聚类,并将聚类结果与临床变量相关联。将这些方法应用于前列腺癌和乳腺癌数据集产生了具有生物学重要性的结果。

可用性

实现这些方法的软件模块dChipSNP可在http://biosun1.harvard.edu/complab/dchip/snp/获取。

补充信息

乳腺癌数据由安德里亚·L·理查森、王志刚和詹姆斯·D·伊格尔哈特提供。

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