FourCSeq: analysis of 4C sequencing data
FourCSeq: analysis of 4C sequencing data
复制标题
DOI：
10.1093/bioinformatics/btv335
复制
发表时间：
2015-10-01
期刊：
影响因子：
5.8
通讯作者：
Huber, Wolfgang
中科院分区：
文献类型：
--
作者：
Klein, Felix A.;Pakozdi, Tibor;Huber, Wolfgang
动机：环状染色体构象捕获 (4C) 是一种强大的技术，用于研究称为“视点”的特定基因组区域与基因组其余部分的空间相互作用，无论是在单一条件下还是比较不同的实验条件或细胞类型。观察到的连接频率通常显示出对距视点的基因组距离的强烈、规律的依赖性，在其之上叠加了特定的相互作用峰。在这里，我们解决计算任务，以找到这些特定的峰值并检测不同生物条件之间的变化。结果：我们通过将平滑单调递减函数拟合到适当转换的计数数据，对相互作用频率随基因组距离降低的总体趋势进行建模。根据拟合，根据残差计算 z 分数，高 z 分数被解释为峰值，为特定相互作用提供证据。为了比较不同的条件，我们对样本之间的片段计数进行标准化，并使用改编自 RNA-Seq 分析的统计方法 DESeq2 来计算差异接触频率。
Motivation: Circularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the 'viewpoint' with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies typically show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific peaks and to detect changes between different biological conditions.Results: We model the overall trend of decreasing interaction frequency with genomic distance by fitting a smooth monotonically decreasing function to suitably transformed count data. Based on the fit, z-scores are calculated from the residuals, and high z-scores are interpreted as peaks providing evidence for specific interactions. To compare different conditions, we normalize fragment counts between samples, and call for differential contact frequencies using the statistical method DESeq2 adapted from RNA-Seq analysis.