Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 70-74.doi: 10.11896/JsJkx.190900065

• Artificial Intelligence • Previous Articles     Next Articles

Improving Hi-C Data Resolution with Deep Convolutional Neural Networks

CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu   

  1. School of Software,Yunnan University,Kunming 650000,China
  • Published:2020-07-07
  • About author:CHENG Zhe, born in 1994, postgra-duate.His main research interests include deep learning, computer vision and bioinformatics.

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

CLC Number: 

  • TP391
[1] LIEBERMAN-AIDEN E,VAN BERKUM N L,LOUISE V,et al.Comprehensive mapping of long-range interactions reveals folding principles of the human genome.Science,2009,326:289-293.
[2] HU M,DENG K,QIN Z H,et al.Bayesian inference of spatial organizations of chromosomes.PLoS computational biology,2013,9(1):e1002893.
[3] VAROQUAUX N,FERHAT A,STAFFORD N W,et al.A statistical approach for inferring the 3D structure of the genome.Bioinformatics,2014,30(12):i26-i31.
[4] SCHMITT A D,HU M,JUNG I,et al.A compendium of chromatin contact maps reveals spatially active regions in the human genome.Cell Rep.,2016,17:2042-2059.
[5] RAO S S,HUNTLEY M H,DURAND N C,et al.A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping.Cell,2014,159(7):1665-1680.
[6] DIXON J R,SIDDARTH S,YUE F,et al.Topological domains in mammalian genomes identified by analysis of chromatin interactions.Nature,2012,485(7398):376-380.
[7] HAYAT K.Multimedia super-resolution via deep learning:Asurvey.Digital Signal Processing,2018.
[8] WANG Y H,LIU T,XU D,et al.Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.Scientific Reports,2016,6:19598.
[9] DUCHON C E.Lanczos Filtering in One and Two Dimensions.Journal of Applied Meteorology,1979,18(8):1016-1022.
[10] FREEMAN W T,PASZTOR E C,OWEN T,et al.Learning Low-Level Vision.International Journal of Computer Vision,2000,40:2000.
[11] FREEMAN W T,JONES T R,PASZTOR E C.Example-based superresolution.Computer Graphics and Applications,2002,22(2):56-65.
[12] SCHULTER S,LEISTNER C,BLSCHOF H.Fast and accurate image upscaling with super-resolution forests//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2015:3791-3799.
[13] DAI D,TIMOFTE R,VAN GOOL L.Jointly optimized regressors for image super-resolution//Eurographics.2015:8.
[14] DONG C,LOY C C,HE K,et al.Learning a Deep Convolutional Network for Image Super-Resolution.Cham:Springer International Publishing,2014:184-199.
[15] ZHANG Y,AN L,XU J,et al.Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus.Nature Communications,2018,9(1):750.
[16] AY F,BAILEY T L,NOBLE W S.Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts.Genome Res.,2014,24:999-1011.
[17] ERNST J,KELLIS M.ChromHMM:automating chromatinstate discovery and characterization.Nat.Methods,2012,9:215-216.
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