Prediction of Transcription Factor Binding Sites Using Deep Learning Combined with DNA Sequences and Shape Feature Data
Authors: 
Yangyang Li, Jie Liu, Hao LiuAuthors Info & Claims
Yangyang Li, Jie Liu, Hao LiuAuthors Info & ClaimsArticle No.: 81, Pages 1 - 6
Abstract
Knowing transcription factor binding sites (TFBS) is essential to model underlying binding mechanisms and cellular functions. Studies have shown that in addition to the DNA sequence, the shape information of DNA is also an important factor affecting its activity. Here, we developed a CNN model to integrate 3D DNA shape information derived using a high-throughput method for predicting TF binding sites (TFBSs). We identify the best performing architectures by varying CNN window size, kernels, hidden nodes and hidden layers. The performance of the two types of data and their combination was evaluated using 69 different ChIP-seq [1] experiments. Our results showed that the model integrating shape information and sequence information compared favorably to the sequence-based model This work combines knowledge from structural biology and genomics, and DNA shape features improved the description of TF binding specificity.
References
[1]
[1]Karimzadeh M, Hoffman M M. Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome. bioRxiv, 2019, 168419.
[2]
[2]Lee T I, Young R A. Transcriptional regulation and its misregulation in disease. Cell, 2013, 152(6): 1237–1251
[3]
[3]Vaquerizas J M, Kummerfeld S K, Teichmann S A, Luscombe N M. A census of human transcription factors: function, expression and evolution. Nature Reviews. Genetics, 2009, 10(4): 252–263
[4]
[4] Junion G, Spivakov M, Girardot C, Braun M, Gustafson E H, Birney E, Furlong E E. A transcription factor collective defines cardiac cell fate and reflects lineage history. Cell, 2012, 148(3): 473–486
[5]
[5]Neph S, Vierstra J, Stergachis A B, et al. An expansive human regulatory lexicon encoded in transcription factor footprints[J]. Nature, 2012, 489(7414):83-90.
[6]
[6]Gilfillan GD, Hughes T, Sheng Y, Hjorthaug H S, Straub T, Gervin K, Harris J R, Undlien D E, Lyle R. Limitations and possibilities of low cell number ChIP-seq. BMC Genomics, 2012, 13: 645
[7]
[7]Park P J. ChIP–seq: advantages and challenges of a maturing technology. Nature Reviews. Genetics, 2009, 10(10): 669–680
[8]
[8]Slattery M, Zhou T, Yang L, et al. Absence of a simple code: how transcription factors read the genome[J]. Trends in Biochemical Sciences, 2014, 39(9).
[9]
[9]Stormo G D, Zhao Y. Determining the specificity of protein–DNA interactions[J]. NATURE REVIEWS GENETICS, 2010, 11(11):751—760.
[10]
[10]NN GD Stormo. Modeling the specificity of protein-DNA interactions[J]. Quant. Biol., 2013, 1(2):115-130.
[11]
[11]Rohs R, Jin X, West S M, et al. Origins of specificity in protein-DNA recognition.[J]. Annual Review of Biochemistry, 2010, 79(1):233-269.
[12]
[12] Kim, Erik, et al. Probing Allostery Through DNA[C]// NCBPC2, 2013.
[13]
[13]Watson L C, Kuchen Be Cker K M, Schiller B J, et al. The glucocorticoid receptor dimer interface allosterically transmits sequence-specific DNA signals[J]. Nature Structural & Molecular Biology, 2013, 20(7):876-83.
[14]
[14]Joshi R, Passner J M, Rohs R, et al. Functional Specificity of a Hox Protein Mediated by the Recognition of Minor Groove Structure[J]. Cell, 2007, 131(3):530-543.
[15]
[15]Rohs R, West S M, Sosinsky A, et al. The role of DNA shape in protein-DNA recognition.[J]. Nature, 2009, 461(7268):1248-1253.
[16]
[16]White M A, CA Myers, Corbo J C, et al. Massively parallel in vivo enhancer assay reveals that highly local features determine the cis-regulatory function of ChIP-seq peaks.[J]. Pnas, 2013, 110(29):11952-11957.
[17]
[17]Zhou T, Shen N, Yang L, Abe N, Horton J, Mann R S, Bussemaker H J, Gordân R, Rohs R. Quantitative modeling of transcription factor binding specificities using DNA shape. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(15): 4654–4659.
[18]
[18]Zhou T, Yang L, Lu Y, Dror I, Dantas Machado A C, Ghane T, Di Felice R, Rohs R. DNAshape: a method for the high-throughput prediction of DNA structural features on a genomic scale. Nucleic Acids Research, 2013, 41(web server issue): W56–W62.
[19]
[19]Gordân R, Shen N, Dror I, Zhou T, Horton J, Rohs R, Bulyk M L. Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape. Cell Reports, 2013, 3(4): 1093–1104.
[20]
[20]Lazarovici A, Zhou T, Shafer A, Dantas Machado A C, Riley T R, Sandstrom R, Sabo P J, Lu Y, Rohs R, Stamatoyannopoulos J A, Bussemaker H J. Probing DNA shape and methylation state on a genomic scale with DNase I. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(16): 6376–6381.
[21]
[21]Chen Y, Zhang X, Dantas Machado A C, Ding Y, Chen Z, Qin P Z, Rohs R, Chen L. Structure of p53 binding to the BAX response element reveals DNA unwinding and compression to accommodate base-pair insertion. Nucleic Acids Research, 2013, 41(17): 8368–8376.
[22]
[22]Chang Y P, Xu M, Machado A C, Yu X J, Rohs R, Chen X S. Mechanism of origin DNA recognition and assembly of an initiator-helicase complex by SV40 large tumor antigen. Cell Reports, 2013, 3(4): 1117–1127.
[23]
[23]Warner J B, Philippakis A A, Jaeger S A, He F S, Lin J, Bulyk M L. Systematic identification of mammalian regulatory motifs’ target genes and functions. Nature Methods, 2008, 5(4): 347–353
[24]
[24]Ghandi M, Lee D, Mohammad-Noori M, Beer M A. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Computational Biology, 2014, 10(7): e1003711
[25]
[25]LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436–444.
[26]
[26]Angermueller C, Lee H J, Reik W, Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biology, 2017, 18(1): 67.
[27]
[27]Qin Q, Feng J. Imputation for transcription factor binding predictions based on deep learning. PLoS Computational Biology, 2017, 13(2): e1005403.
[28]
[28]Yang B, Liu F, Ren C, Ouyang Z, Xie Z, Bo X, Shu W. BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone. Bioinformatics, 2017, 33(13): 1930–1936.
[29]
[29]Kelley D R, Snoek J, Rinn J L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research, 2016, 26(7): 990–999.
[30]
[30]Zeng H, Edwards M D, Liu G, Gifford D K. Convolutional neural network architectures for predicting DNA–protein binding. Bioinformatics, 2016, 32(12): i121–i127.
[31]
[31]Jurtz V I, Johansen A R, Nielsen M, Almagro Armenteros J J, Nielsen H, Sønderby C K, Winther O, Sønderby S K. An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics, 2017, 33(22): 3685–3690.
[32]
[32]Liu Q, Xia F, Yin Q, Jiang R. Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics, 2017, 34(5): 732–738.
[33]
[33]Min X, Zeng W, Chen N, Chen T, Jiang R. Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding. Bioinformatics, 2017, 33(14): i92–i101.
[34]
[34]Bu H, Gan Y, Wang Y, Zhou S, Guan J. A new method for enhancer prediction based on deep belief network. BMC Bioinformatics, 2017, 18(Suppl 12): 418.
[35]
[35]Zhang J, Peng W, Wang L. LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks. Bioinformatics, 2018, 34(10): 1705–1712.
[36]
[36]Inukai S, Kock K H, Bulyk M L. Transcription factor-DNA binding: beyond binding site motifs. Current Opinion in Genetics & Development, 2017, 43: 110–119.
[37]
[37]Siggers T, Gordân R. Protein-DNA binding: complexities and multi-protein codes. Nucleic Acids Research, 2014, 42: 2099–2111.
[38]
[38] Pique-Regi R, Degner J F, Pai A A, Gaffney D J, Gilad Y, Pritchard J K. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Research, 2011, 21(3): 447–455.
[39]
[39]Gusmao E G, Allhoff M, Zenke M, Costa I G. Analysis of computational footprinting methods for DNase sequencing experiments. Nature Methods, 2016, 13(4): 303–309.
[40]
[40]Guo W L, Huang D S. An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency. Molecular BioSystems, 2017, 13(9): 1827–1837.
[41]
[41]Jing F, Zhang S W, Cao Z, Zhang S. Combining sequence and epigenomic data to predict transcription factor binding sites using deep learning. In: International Symposium on Bioinformatics Research and Applications, 2018, 241–252.
[42]
[42]Yang L, Zhou T, Dror I, Mathelier A, Wasserman W W, Gordân R, Rohs R. TFBSshape: a motif database for DNA shape features of transcription factor binding sites. Nucleic Acids Research, 2014, 42(Database issue): D148–D155.
[43]
[43]Qinhu, Zhang, Zhen, et al. Predicting in-vitro transcription factor binding sites using DNA sequence + shape.[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2019.
[44]
[44]Wang S, Shen Z, Y He, et al. A New Method Combining DNA Shape Features to Improve the Prediction Accuracy of Transcription Factor Binding Sites[M]. Springer, Cham, 2020.
[45]
[45]Min X, Zeng W, Chen S, Chen N, Chen T, Jiang R. Predicting enhancers with deep convolutional neural networks. BMC Bioinformatics, 2017, 18(Suppl 13): 478.
[46]
[46]Zhou J, Troyanskaya O G. Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 2015, 12(10): 931–934.
[47]
[47]Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M. TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016, 265–283.
[48]
[48]Bernstein B E, et al. An integrated encyclopedia of DNA elements in the human genome[J]. Nature, 2012, 489(7414):p.57-74.
[49]
[49]Srivastava N, Hinton G E, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15(56): 1929–1958.
[50]
[50]Zeiler M D. ADADELTA: an adaptive learning rate method. arXiv, 2012, arXiv:1212.5701.
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Published In
December 2021
508 pages
ISBN:9781450386074
DOI:10.1145/3469877
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Published: 10 January 2022
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- National Key Research and Development Program of China
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- Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)
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