基于深度学习的miRNA靶位点预测研究综述

doi:10.11896/jsjkx.191200111

摘要/Abstract

摘要： MicroRNAs(miRNAs)是一类长约22~23碱基(nt)的单链非编码RNA,在生物进化方面有着重要意义。成熟的miRNA会通过其种子序列(5'第2-8位核苷酸)与message RNAs(mRNAs)的3'UTR区域靶位点进行完全或不完全配对,实现切割mRNA及抑制mRNA翻译等功能。由于miRNA结合mRNA靶位点的机制仍未明确,因此预测miRNA靶位点的工作一直是miRNA研究领域的一大挑战和难题。实验方法虽然准确,但耗时长且昂贵。在生物信息领域,基于规则匹配的常规计算方法虽然能进行靶位点的预测,但存在着准确率偏低的问题。随着深度学习的兴起及实验验证数据及具体靶位点信息的丰富,基于深度学习的方法成为了miRNA靶位点预测领域的研究热点。首先介绍了常用的miRNA预测数据集、预测类型和常见特征;之后对预测研究中常用的深度学习模型进行阐述;接着介绍了常规的预测方法及基于深度学习的预测方法,并对这些方法进行了分类总结和性能的对比分析;最后对使用深度学习的预测工作当前存在的问题及未来的发展进行了探讨。

关键词: miRNA, 靶位点预测, 卷积神经网络, 循环神经网络, 自动编码器

Abstract: MicroRNAs(miRNAs) are 22~23 nt small non-coding RNAs that play an important role in biological evolution.Mature miRNA will completely or incompletely pair with the target site in 3'UTR region of message RNAs(mRNAs) through its seed region,to achieve the function of cleavage and translational repression so on.As the mechanism of miRNA binding to mRNA target sites is still unclear,the prediction of miRNA target sites has been a major challenge and problem in the field of miRNA research.Although the experimental method is accurate,it is time-consuming and expensive.In Bioinformatics,although the calculation method based on rule matching can predict the target site,it has the problem of low accuracy.With the development of deep learning and the abundance of experimental data,the method based on deep learning has become a research hotspot in the field of miRNA target prediction.Firstly,this paper introduces the commonly used data sets,prediction types and common feature of miRNA prediction,then explains the commonly used deep learning model in prediction research.Next,the conventional prediction methods and prediction methods based on deep learning are introduced.Meanwhile,these methods are classified and summarized.Finally,the current problems and future development of using deep learning to predict miRNA target are discussed.

Key words: Autoencoder, Convolutional neural network, miRNA, Recurrent neural network, Target site prediction

中图分类号:

TP181

李亚男, 胡宇佳, 甘伟, 朱敏. 基于深度学习的miRNA靶位点预测研究综述[J]. 计算机科学, 2021, 48(1): 209-216. https://doi.org/10.11896/jsjkx.191200111

LI Ya-nan, HU Yu-jia, GAN Wei, ZHU Min. Survey on Target Site Prediction of Human miRNA Based on Deep Learning[J]. Computer Science, 2021, 48(1): 209-216. https://doi.org/10.11896/jsjkx.191200111

参考文献

[1] KIM,NARRY V.MicroRNA biogenesis:coordinated croppingand dicing[J].Nature Reviews Molecular Cell Biology,2005,6(5):376-385.
[2] IBÁÑEZ-VENTOSO C,VORA M,DRISCOLL M.Sequence relationships among C.elegans,D.melanogaster and human microRNAs highlight the extensive conservation of microRNAs in biology[J].PloS one,2008,3(7):e2818-e2818.
[3] KOTA J,CHIVUKULA R R,O'DONNELL K A,et al.Therapeutic microRNA delivery suppresses tumorigenesis in a murine liver cancer model[J].Cell,2009,137(6):1005-1017.
[4] MA L,REINHARDT F,PAN E,et al.Therapeutic silencing of miR-10b inhibits metastasis in a mouse mammary tumor model[J].Nature Biotechnology,2010,28(4):341-347.
[5] MA L,TERUYA-FELDSTEIN J,WEINBERG R A.Tumourinvasion and metastasis initiated by microRNA-10b in breast cancer[J].Nature,2007,449(7163):682-688.
[6] THOMAS M,LIEBERMAN J,LAL A.Desperately seeking microRNA targets[J].Nature Structural & Molecular Biology,2010,17(10):1169-1174.
[7] BARTEL D P.MicroRNAs:genomics,biogenesis,mechanism,and function[J].Cell,2004,116(2):281-297.
[8] HUANG J C,BABAK T,CORSON T W,et al.Using expression profiling data to identify human microRNA targets[J].Nature Methods,2007,4(12):1045-1049.
[9] ALIPANAHI B,DELONG A,WEIRAUCH M T,et al.Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning[J].Nature Biotechnology,2015,33(8):831-838.
[10] ESTEVA A,KUPREL B,NOVOA R A,et al.Dermatologist-level classification of skin cancer with deep neural networks[J].Nature,2017,542(7639):115-118.
[11] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[12] ZHOU J,TROYANSKAYA O G.Predicting effects of noncoding variants with deep learning-based sequence model[J].Nature Methods,2015,12(10):931-934.
[13] KARAGKOUNI D,PARASKEVOPOULOU M D,CHATZOPOULOS S,et al.DIANA-TarBase v8:a decade-long collection of experimentally supported miRNA-gene interactions[J].Nucleic Acids Research,2017,46(D1):D239-D245.
[14] CHOU C H,SHRESTHA S,YANG C D,et al.miRTarBase update 2018:a resource for experimentally validated microRNA-target interactions[J].Nucleic Acids Research,2017,46(D1):D296-D302.
[15] HELWAK A,KUDLA G,DUDNAKOVA T,et al.Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding[J].Cell,2013,153(3):654-665.
[16] BRENNECKE J,STARK A,RUSSELL R B,et al.Principles of microRNA-target recognition[J].PLoS Biology,2005,3(3):e85.
[17] LORENZ R,BERNHART S H,ZUSIEDERDISSEN C H,et al.ViennaRNA Package 2.0[J].Algorithms for Molecular Bio-logy,2011,6(1):26.
[18] KENT W J,SUGNET C W,FUREY T S,et al.The human genome browser at UCSC[J].Genome Research,2002,12(6):996-1006.
[19] ALTSCHUL S F,GISH W,MILLER W,et al.Basic local alignment search tool[J].Journal of Molecular Biology,1990,215(3):403-410.
[20] JOHN B,ENRIGHT A J,ARAVIN A,et al.Human microRNA targets[J].PLoS Biology,2004,2(11):e363.
[21] MARAGKAKIS M,ALEXIOU P,PAPADOPOULOS G L,et al.Accurate microRNA target prediction correlates with protein repression levels[J].BMC Bioinformatics,2009,10(1):295.
[22] MENOR M,CHING T,ZHU X,et al.mirMark:a site-level and UTR-level classifier for miRNA target prediction[J].Genome Biology,2014,15(10):500.
[23] FISHER R A,YATES F.Statistical tables for biological,agricultural and medical research[M].London:Oliver and Boyd Ltd,1943.
[24] HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[25] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.2012:1097-1105.
[26] COLLOBERT R,WESTON J.A unified architecture for natural language processing:Deep neural networks with multitask learning[C]//Proceedings of the 25th International Conference on Machine Learning.ACM,2008:160-167.
[27] PAN X,SHEN H B.Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks[J].Bioinformatics,2018,34(20):3427-3436.
[28] CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(3):273-297.
[29] BREIMAN L.Random forests[J].Machine Learning,2001,45(1):5-32.
[30] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[31] WILLIAMS R J,ZIPSER D.A learning algorithm for continually running fully recurrent neural networks[J].Neural Computation,1989,1(2):270-280.
[32] VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders:Learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(12):3371-3408.
[33] ZHANG L,CHEN X,YIN J.Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder[J].Cells,2019,8(9):1040.
[34] HENDERSON J,LY V,OLICHWIER S,et al.Accurate prediction of boundaries of high resolution topologically associated domains(TADs) in fruit flies using deep learning[J].Nucleic Acids Research,2019,47(13):e78-e78.
[35] YANG Y,ZHOU M,FANG Q,et al.AnnoFly:annotating Drosophila embryonic images based on an attention-enhanced RNN model[J].Bioinformatics,2019,35(16):2834-2842.
[36] NG A.Sparse autoencoder[J].CS294A Lecture Notes,2011,72(2011):1-19.
[37] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[38] CHO K,VAN MERRIËNBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014.
[39] HE K,ZHANG X,REN S,et al.Deep residual learning for ima-ge recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[40] SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958.
[41] IOFFE S,SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv:1502.03167,2015.
[42] ABADI M,BARHAM P,CHEN J,et al.Tensorflow:A system for large-scale machine learning[C]//12th {USENIX} Symposium on Operating Systems Design and Implementation(OSDI 16).2016:265-283.
[43] PASZKE A,GROSS S,MASSA F,et al.Pytorch:An imperative style,high-performance deep learning library[C]//Advances in neural information processing systems.2019:8026-8037.
[44] TEAM T T D,AL-RFOU R,ALAIN G,et al.Theano:A Python framework for fast computation of mathematical expressions[J].arXiv:1605.02688,2016.
[45] FAN X,KURGAN L.Comprehensive overview and assessment of computational prediction of microRNA targets in animals[J].Briefings in Bioinformatics,2014,16(5):780-794.
[46] LEWIS B P,SHIH I,JONES-RHOADES M W,et al.Prediction of mammalian microRNA targets[J].Cell,2003,115(7):787-798.
[47] KRÜGER J,REHMSMEIER M.RNAhybrid:microRNA target prediction easy,fast and flexible[J].Nucleic Acids Research,2006,34(suppl 2):W451-W454.
[48] KERTESZ M,IOVINO N,UNNERSTALL U,et al.The role of site accessibility in microRNA target recognition[J].Nature Genetics,2007,39(10):1278-1284.
[49] STURM M,HACKENBERG M,LANGENBERGER D,et al.TargetSpy:a supervised machine learning approach for microRNA target prediction[J].BMC Bioinformatics,2010,11(1):292.
[50] BANDYOPADHYAY S,MITRA R.TargetMiner:microRNAtarget prediction with systematic identification of tissue-specific negative examples[J].Bioinformatics,2009,25(20):2625-2631.
[51] DING J,LI X,HU H.TarPmiR:a new approach for microRNA target site prediction[J].Bioinformatics,2016,32(18):2768-2775.
[52] CHENG S,GUO M,WANG C,et al.MiRTDL:A Deep Learning Approach for miRNA Target Prediction[J].IEEE/ACM Transactions on Computational Biology & Bioinformatics,2016,13(6):1161-1169.
[53] LEE B,BAEK J,PARK S,et al.deepTarget:end-to-end learning framework for microRNA target prediction using deep recurrent neural networks[C]//Proceedings of the 7th ACM International Conference on Bioinformatics,Computational Biology,and Health Informatics.2016:434-442.
[54] WEN M,CONG P,ZHANG Z,et al.DeepMirTar:a deep-learning approach for predicting human miRNA targets[J].Bioinformatics,2018,34(22):3781-3787.
[55] PLA A,ZHONG X,RAYNER S.miRAW:A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts[J].PLoS Computational Biology,2018,14(7):e1006185.
[56] GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680.
[57] GUPTA A,ZOU J.Feedback GAN for DNA optimizes protein functions[J].Nature Machine Intelligence,2019,1(2):105-111.
[58] LIU Q,LV H,JIANG R.hicGAN infers super resolution Hi-C data with generative adversarial networks[J].Bioinformatics,2019,35(14):i99-i107.
[59] TARGONSKI C,SHEALY B T,SMITH M C,et al.Cellular State Transformations using Generative Adversarial Networks[J].arXiv:1907.00118,2019.
[60] YU L,ZHANG W,WANG J,et al.Seqgan:Sequence generative adversarial nets with policy gradient[C]//Thirty-First AAAI Conference on Artificial Intelligence.2017.
[61] MOORE M J,SCHEEL T K H,LUNA J M,et al.miRNA-target chimeras reveal miRNA 3’-end pairing as a major determinant of Argonaute target specificity[J].Nature Communications,2015,6(1):1-17.
[62] KLUM S M,CHANDRADOSS S D,SCHIRLE N T,et al.Helix-7in Argonaute2 shapes the microRNA seed region for rapid target recognition[J].The EMBO Journal,2018,37(1):75-88.

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