生物
拷贝数变化
断点
推论
拷贝数分析
计算生物学
比较基因组杂交
概率逻辑
基因组
计算机科学
进化生物学
遗传学
人工智能
基因
染色体
作者
Magda Markowska,Tomasz Cąkała,Błażej Miasojedow,Bogac Aybey,Dilafruz Juraeva,Johanna Mazur,Edith Ross,Eike Staub,Ewa Szczurek
出处
期刊:Genome Biology
[BioMed Central]
日期:2022-06-09
卷期号:23 (1): 128-128
被引量:43
标识
DOI:10.1186/s13059-022-02693-z
摘要
Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling.
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