Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information.
Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information.
复制标题
DOI：
10.1186/1471-2164-15-608
复制
发表时间：
2014-07-18
期刊：
影响因子：
4.4
通讯作者：
Yi SV
中科院分区：
文献类型：
--
作者：
Huh I;Yang X;Park T;Yi SV
亚硫酸氢盐转化DNA的全基因组测序(‘甲基C-seq’)方法提供了DNA甲基化的全面信息。这些全基因组甲基化图谱的一个重要应用是将每个位置归类为甲基化核苷酸和非甲基化核苷酸。目前广泛使用的一种方法，即所谓的二项式方法，是直观和直接的，但在序列覆盖率和全基因组甲基化水平较低时缺乏能力。在分析稀疏甲基化的基因组时，这些问题是一个特别的挑战，例如许多无脊椎动物和植物的基因组。我们证明了从甲基C-seq数据中每个位置读取的序列数量显示出很大的方差，并且可以被建模为移位的负二项分布。我们还表明相邻CpG位点的DNA甲基化水平是相关的，并且这种在局部DNA甲基化水平上的相似性延伸了几千碱基。考虑到这些观察结果，我们提出了一种基于贝叶斯分类的新方法来推断DNA甲基化状态，同时考虑到特定位点的邻域DNA甲基化水平。通过计算模拟、曲线下面积(AUC)分析和生物复制一致性分析等多种分析表明，我们的方法比二项式方法具有更高的灵敏度和更好的分类性能。这种方法在低覆盖率的稀疏甲基化基因组的分析中特别有利。我们的方法通过利用后验优势框架并结合局部甲基化信息，改进了现有的二叉树二叉树方法。该方法可广泛应用于不同稀疏甲基化基因组的甲基化C-seq数据分析。BIS级和示例数据在专用网站(http://bibs.snu.ac.kr/software/Bisclass).)上提供本文的在线版本(DOI：10.1186/1471-2164-15-608)包含补充材料，授权用户可以使用。
Whole genome sequencing of bisulfite converted DNA (‘methylC-seq’) method provides comprehensive information of DNA methylation. An important application of these whole genome methylation maps is classifying each position as a methylated versus non-methylated nucleotide. A widely used current method for this purpose, the so-called binomial method, is intuitive and straightforward, but lacks power when the sequence coverage and the genome-wide methylation level are low. These problems present a particular challenge when analyzing sparsely methylated genomes, such as those of many invertebrates and plants. We demonstrate that the number of sequence reads per position from methylC-seq data displays a large variance and can be modeled as a shifted negative binomial distribution. We also show that DNA methylation levels of adjacent CpG sites are correlated, and this similarity in local DNA methylation levels extends several kilobases. Taking these observations into account, we propose a new method based on Bayesian classification to infer DNA methylation status while considering the neighborhood DNA methylation levels of a specific site. We show that our approach has higher sensitivity and better classification performance than the binomial method via multiple analyses, including computational simulations, Area Under Curve (AUC) analyses, and improved consistencies across biological replicates. This method is especially advantageous in the analyses of sparsely methylated genomes with low coverage. Our method improves the existing binomial method for binary methylation calls by utilizing a posterior odds framework and incorporating local methylation information. This method should be widely applicable to the analyses of methylC-seq data from diverse sparsely methylated genomes. Bis-Class and example data are provided at a dedicated website (http://bibs.snu.ac.kr/software/Bisclass). The online version of this article (doi:10.1186/1471-2164-15-608) contains supplementary material, which is available to authorized users.
登录
查看更多内容
寄生线虫旋毛虫离散发育阶段的差异 DNA 甲基化
影响因子：
12.3
作者：
Gao F;Liu X;Wu XP;Wang XL;Gong D;Lu H;Xia Y;Song Y;Wang J;Du J;Liu S;Han X;Tang Y;Yang H;Jin Q;Zhang X;Liu M
通讯作者：
Liu M
影响因子：
64.5
作者：
Calarco JP;Borges F;Donoghue MT;Van Ex F;Jullien PE;Lopes T;Gardner R;Berger F;Feijó JA;Becker JD;Martienssen RA
通讯作者：
Martienssen RA
影响因子：
2.7
作者：
Gavery MR;Roberts SB
通讯作者：
Roberts SB
影响因子：
3.7
作者：
Zeng J;Nagrajan HK;Yi SV
通讯作者：
Yi SV
影响因子：
8
作者：
Bradley, AP
通讯作者：
Bradley, AP