使用深层卷积神经网络提高Hi-C 数据分辨率

doi:10.11896/JsJkx.190900065

摘要/Abstract

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。

关键词: Hi-C技术, 超分辨率, 卷积神经网络, 生物信息学, 深度学习

Abstract: Hi-C technology measures the frequency of all paired-interaction in the entire genome.It has become one of the most popular tools for studying the 3D structure of genomes.In general,Hi-C data-based studies require sequencing of a large number of Chromosome data,while Hi-C data with lower sequencing depth,although less expensive,is not sufficient to provide sufficient biological information for subsequent studies.Since the Hi-C data contains similar sub-patterns and has data continuity within a certain area,it can be predicted.This paper explored an improved method based on convolutional neural network model.It predicts the core Hi-C values in a larger range and extends the depth and receptive field of the convolutional neural network,predicts the original sequencing reading of Hi-C by 1/16 of the original sequencing readings.The experimental results were measured by the Pearson correlation coefficient and the Spearman correlation coefficient,and the apparent interaction pairs were analyzed using Fit-Hi-C,and the state analyses of 12 chrom HMM-marked chromatin with ChromHMM were called.The experimental results show that the prediction results are not only close to the numerical distribution,but also more reliable than the low-resolution Hi-C data in terms of site interaction information and chromatin state.

Key words: Hi-C technology, Super-resolution, Convolutional neural network, Bioinformatics, Deep learning

中图分类号:

TP391

程哲, 白茜, 张浩, 王世普, 梁宇. 使用深层卷积神经网络提高Hi-C 数据分辨率[J]. 计算机科学, 2020, 47(6A): 70-74.

CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu. Improving Hi-C Data Resolution with Deep Convolutional Neural Networks[J]. Computer Science, 2020, 47(6A): 70-74.

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