RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data.
RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data.
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DOI：
10.1007/s40484-020-0225-2
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发表时间：
2020-12-24
期刊：
影响因子：
--
通讯作者：
Peng W
中科院分区：
文献类型：
--
作者：
Zang C;Wang Y;Peng W
组蛋白修饰是决定染色质状态的主要因素，在真核细胞中具有调节基因表达的功能。染色质免疫沉淀结合高通量测序（ChIP-seq）技术已被广泛用于分析染色质相关蛋白因子的全基因组分布。一些组蛋白修饰，如H3 K27 me 3和H3 K9 me 3，通常标记基因组中从DNA酶（kb）到兆碱基（Mb）长的广泛结构域，导致ChIP-seq数据中的扩散模式，这对信号分离具有挑战性。虽然大多数现有的ChIP-seq峰调用算法都是基于局部统计模型，而没有考虑多尺度特征，但一直缺乏识别无尺度板域的原则性方法。在这里，我们提出了识别（递归粗粒化识别ChIP-seq富集区域），一种计算方法，用于识别大范围尺度上的ChIP-seq富集域。该算法是基于一个粗粒度的方法，它使用递归块变换，以确定跨多个长度尺度的局部富集元素的空间聚类。我们应用RECOGNICER从ChIP-seq数据中调用H3 K27 me 3结构域，并基于H3 K27 me 3与抑制性基因表达的关联来验证结果。我们表明，在识别更多的整体域比单独的片段，识别识别识别比现有的ChIP-seq广泛的域调用工具。RECOGNICER可以成为表观基因组学研究中下一代测序数据分析的有用生物信息学工具。组蛋白修饰在确定染色质状态和调控基因表达方面起着重要作用。许多组蛋白修饰和其他染色质结合蛋白因子可以标记基因组中跨多个尺度的广泛结构域。从ChIP-seq数据来看，这样的宽域比尖锐的峰更难识别。在这项工作中，我们提出了识别，一种创新的计算方法，用于识别跨尺度的广泛领域，使用粗粒度的方法。RECOGNICER可以成为ChIP-seq数据分析的有用工具。
Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors. Some histone modifications, such as H3K27me3 and H3K9me3, usually mark broad domains in the genome ranging from kilobases (kb) to megabases (Mb) long, resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation. While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features, a principled method to identify scale-free board domains has been lacking. Here we present RECOGNICER (Recursive coarse-graining identification for ChIP-seq enriched regions), a computational method for identifying ChIP-seq enriched domains on a large range of scales. The algorithm is based on a coarse-graining approach, which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales. We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data, and validate the results based on H3K27me3’s association with repressive gene expression. We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces. RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research. Histone modifications play an important role in defining chromatin states and regulating gene expression. Many histone modifications and other chromatin-binding protein factors can mark broad domains across multiple scales in the genome. From ChIP-seq data, such broad domains are more challenging to identify than sharp peaks. In this work, we present RECOGNICER, an innovative computational method for identifying cross-scale broad domains using a coarse-graining approach. RECOGNICER can be a useful tool for ChIP-seq data analysis.
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