Selfish: discovery of differential chromatin interactions via a self-similarity measure
Selfish: discovery of differential chromatin interactions via a self-similarity measure
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DOI：
10.1093/bioinformatics/btz362
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发表时间：
2019-07-15
期刊：
影响因子：
5.8
通讯作者：
Lonardi, Stefano
中科院分区：
文献类型：
--
作者：
Ardakany, Abbas Roayaei;Ay, Ferhat;Lonardi, Stefano
动机高通量构象捕获实验，如Hi-C，提供了染色质相互作用的全基因组图谱，使生命科学家能够研究基因组三维结构在基因调控和其他基本细胞功能中的作用。Hi-C数据分析中的一个基本问题是如何比较由Hi-C实验得到的两个接触图。检测接触图之间的相似性和差异性对于评估重复实验的重复性和识别具有生物学意义的差异基因组区域至关重要。由于染色质构象的复杂性以及技术驱动和序列特异性偏差的存在，Hi-C数据的比较分析在分析和计算上都是具有挑战性的。结果利用接触图的结构自相似性，我们提出了一种新的Hi-C数据比较分析方法--SelFish。我们定义了一种新的自相似度量来设计算法，用于(I)测量Hi-C重复实验的重复性和(Ii)寻找两个接触图之间的差异染色质相互作用。在模拟数据和真实数据上的大量实验结果表明，与最先进的方法相比，SELLISH方法具有更高的准确性和健壮性。可用性和implementationhttps://github.com/ucrbioinfo/Selfish
Motivation High-throughput conformation capture experiments, such as Hi-C provide genome-wide maps of chromatin interactions, enabling life scientists to investigate the role of the three-dimensional structure of genomes in gene regulation and other essential cellular functions. A fundamental problem in the analysis of Hi-C data is how to compare two contact maps derived from Hi-C experiments. Detecting similarities and differences between contact maps are critical in evaluating the reproducibility of replicate experiments and for identifying differential genomic regions with biological significance. Due to the complexity of chromatin conformations and the presence of technology-driven and sequence-specific biases, the comparative analysis of Hi-C data is analytically and computationally challenging.Results We present a novel method called Selfish for the comparative analysis of Hi-C data that takes advantage of the structural self-similarity in contact maps. We define a novel self-similarity measure to design algorithms for (i) measuring reproducibility for Hi-C replicate experiments and (ii) finding differential chromatin interactions between two contact maps. Extensive experimental results on simulated and real data show that Selfish is more accurate and robust than state-of-the-art methods.Availability and implementationhttps://github.com/ucrbioinfo/Selfish