Differential methylation tests of regulatory regions

IF 0.9 4区 数学 Q3 Mathematics Statistical Applications in Genetics and Molecular Biology Pub Date : 2016-06-01 DOI:10.1515/sagmb-2015-0037
D. Ryu, Hongyang Xu, Varghese George, S. Su, Xiaoling Wang, Huidong Shi, R. Podolsky
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引用次数: 6

Abstract

Abstract Differential methylation of regulatory elements is critical in epigenetic researches and can be statistically tested. We developed a new statistical test, the generalized integrated functional test (GIFT), that tests for regional differences in methylation based on the methylation percent at each CpG site within a genomic region. The GIFT uses estimated subject-specific profiles with smoothing methods, specifically wavelet smoothing, and calculates an ANOVA-like test to compare the average profile of groups. In this way, possibly correlated CpG sites within the regulatory region are compared all together. Simulations and analyses of data obtained from patients with chronic lymphocytic leukemia indicate that GIFT has good statistical properties and is able to identify promising genomic regions. Further, GIFT is likely to work with multiple different types of experiments since different smoothing methods can be used to estimate the profiles of data without noise. Matlab code for GIFT and sample data are available at http://www.augusta.edu/mcg/biostatepi/people/software/gift.html.
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调控区的差异甲基化试验
调控元件的差异甲基化在表观遗传学研究中至关重要,并且可以进行统计学检验。我们开发了一种新的统计测试,即广义综合功能测试(GIFT),该测试基于基因组区域内每个CpG位点的甲基化百分比来测试甲基化的区域差异。GIFT使用平滑方法(特别是小波平滑)估计的受试者特定特征,并计算类似anova的检验来比较各组的平均特征。通过这种方式,将调控区域内可能相关的CpG位点全部进行比较。对慢性淋巴细胞白血病患者数据的模拟和分析表明,GIFT具有良好的统计特性,能够识别有希望的基因组区域。此外,GIFT可能适用于多种不同类型的实验,因为可以使用不同的平滑方法来估计无噪声数据的轮廓。GIFT的Matlab代码和样本数据可在http://www.augusta.edu/mcg/biostatepi/people/software/gift.html获得。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
期刊最新文献
Empirically adjusted fixed-effects meta-analysis methods in genomic studies. A CNN-CBAM-BIGRU model for protein function prediction. A heavy-tailed model for analyzing miRNA-seq raw read counts. Flexible model-based non-negative matrix factorization with application to mutational signatures. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.
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