期刊
BIOINFORMATICS
卷 29, 期 23, 页码 2964-2970出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btt521
关键词
-
类别
资金
- NIH/NHGRI [R01HG006465]
Motivation: The accurate detection of copy number alterations (CNAs) in human genomes is important for understanding susceptibility to cancer and mechanisms of tumor progression. CNA detection in tumors from single nucleotide polymorphism (SNP) genotyping arrays is a challenging problem due to phenomena such as aneuploidy, stromal contamination, genomic waves and intra-tumor heterogeneity, issues that leading methods do not optimally address. Results: Here we introduce methods and software (PennCNV-tumor) for fast and accurate CNA detection using signal intensity data from SNP genotyping arrays. We estimate stromal contamination by applying a maximum likelihood approach over multiple discrete genomic intervals. By conditioning on signal intensity across the genome, our method accounts for both aneuploidy and genomic waves. Finally, our method uses a hidden Markov model to integrate multiple sources of information, including total and allele-specific signal intensity at each SNP, as well as physical maps to make posterior inferences of CNAs. Using real data from cancer cell-lines and patient tumors, we demonstrate substantial improvements in accuracy and computational efficiency compared with existing methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据