计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 70-74.doi: 10.11896/JsJkx.190900065
程哲, 白茜, 张浩, 王世普, 梁宇
CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu
摘要: Hi-C技术是一种测量整个基因组中所有成对交互的频率的技术,已成为研究基因组3D结构最流行的工具之一。通常情况下,基于Hi-C数据的研究需要测序大量的染色体数据,而测序深度较低的Hi-C数据虽然成本较低,但不足以提供充足的生物学信息给后续研究。由于Hi-C数据包含了类似的子模式,且一定区域内具有数据连续性,因此可以被预测。文中探究了基于卷积神经网络模型的改进方法,该模型以更大的范围预测核心的Hi-C数值,并扩展卷积神经网络的深度和感受野,通过1/16的原始测序读数,预测出Hi-C数据的原始测序读数。实验结果以皮尔森相关系数和斯皮尔曼相关系数衡量,并使用Fit-Hi-C分析明显的相互作用对,以及通过调用ChromHMM标记的染色质状态区域进行染色质状态分析。实验结果表明,预测结果不仅在数值分布规律上接近,而且在位点互作信息和染色质状态等方面也比低分辨率Hi-C数据更加可靠。
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