Predicting enhancers with deep convolutional neural networks

BMC Bioinformatics

Volumn 18, Issue , 2017, Pages

  Min, Xu ^a,b   Zeng, Wanwen ^a,b   Chen, Shengquan ^a,b   Chen, Ning ^a,b   Chen, Ting ^a,b,c   Jiang, Rui ^a,b  

^a TSINGHUA NATIONAL LABORATORY FOR INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b TSINGHUA UNIVERSITY   (China)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELL CULTURE; CLASSIFICATION (OF INFORMATION); CONVOLUTION; DEEP LEARNING; DNA SEQUENCES; GENES; LEARNING SYSTEMS; NEURAL NETWORKS;

COMPUTATIONAL FRAMEWORK; COMPUTATIONAL IDENTIFICATION; CONVOLUTIONAL KERNEL; CONVOLUTIONAL NEURAL NETWORK; EFFECTIVENESS AND EFFICIENCIES; EXPERIMENTAL APPROACHES; MACHINE LEARNING METHODS; TRANSFER LEARNING;

DEEP NEURAL NETWORKS;

DNA;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BIOLOGY; CHEMISTRY; ENHANCER REGION; FACTUAL DATABASE; GENETICS; GENOMICS; HUMAN; HUMAN GENOME; MACHINE LEARNING;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DNA; ENHANCER ELEMENTS, GENETIC; GENOME, HUMAN; GENOMICS; HUMANS; MACHINE LEARNING; MODELS, GENETIC; NEURAL NETWORKS (COMPUTER);

EID: 85036450976     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-017-1878-3     Document Type: Article

References (39)

1. Blackwood EM, Kadonaga JT. Going the distance: a current view of enhancer action. Science. 1998;281(5373):60-3.
2. Pennacchio LA, Bickmore W, Dean A, Nobrega MA, Bejerano G. Enhancers: five essential questions. Nat Rev Genet. 2013;14(4):288-95.
3. Maston GA, Evans SK, Green MR. Transcriptional regulatory elements in the human genome. Annu Rev Genomics Hum Genet. 2006;7:29-59.
4. Heintzman ND, Ren B. Finding distal regulatory elements in the human genome. Curr Opin Genet Dev. 2009;19(6):541-9.
5. May D, Blow MJ, Kaplan T, McCulley DJ, Jensen BC, Akiyama JA, Holt A, Plajzer-Frick I, Shoukry M, Wright C, et al. Large-scale discovery of enhancers from human heart tissue. Nat Genet. 2012;44(1):89-93.
6. Boyle AP, Song L, Lee B-K, London D, Keefe D, Birney E, Iyer VR, Crawford GE, Furey TS. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res. 2011;21(3):456-64.
7. Koch CM, Andrews RM, Flicek P, Dillon SC, Karaöz U, Clelland GK, Wilcox S, Beare DM, Fowler JC, Couttet P, et al. The landscape of histone modifications across 1% of the human genome in five human cell lines. Genome Res. 2007;17(6):691-707.
8. Consortium EP, et al. The encode (encyclopedia of dna elements) project. Science. 2004;306(5696):636-40.
9. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, Chen Y, Zhao X, Schmidl C, Suzuki T, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507(7493):455-61.
10. Lee D, Karchin R, Beer MA. Discriminative prediction of mammalian enhancers from dna sequence. Genome Res. 2011;21(12):2167-80.
11. Ghandi M, Lee D, Mohammad-Noori M, Beer MA. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol. 2014;10(7):1003711.
12. Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31(5):761.
13. Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46(3):310.
14. Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of dna-and rna-binding proteins by deep learning. Nature biotechnology (2015).
15. Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12(10):931-4.
16. Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 2016;26(7):990-9.
17. Ernst J, Kellis M. Chromhmm: automating chromatin-state discovery and characterization. Nat Methods. 2012;9(3):215-6.
18. Lasange. https://github.com/Lasagne/Lasagne. Accessed: 8 Jan 2016.
19. Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., Bengio, Y.: Theano: new features and speed improvements. arXiv preprint arXiv:1211.5590 (2012).
20. Theano. http://deeplearning.net/software/theano. Accessed 08 Jan 2016.
21. Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
22. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems; 2012. p. 1097-105.
23. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013).
24. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015).
25. Lanchantin, J., Singh, R., Lin, Z., Qi, Y.: Deep motif: Visualizing genomic sequence classifications. arXiv preprint arXiv:1605.01133 (2016).
26. Mathelier A, Fornes O, Arenillas DJ, et al. JASPAR 2016: a major expansion and update of the openaccess database of transcription factor binding profiles. Nucl. Acids Res. 2016;44(D1):D110.
27. Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS. MEME SUITE: tools for motif discovery and searching. Nucl. Acids Res. 2009;37:W202-W208.
28. Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS. Quantifying similarity between motifs. Genome Biol. 2007;8(2):1.
29. Zhao B, Barrera LA, Ersing I, Willox B, Schmidt SC, Greenfeld H, Zhou H, Mollo SB, Shi TT, Takasaki K, et al. The nf-Κb genomic landscape in lymphoblastoid b cells. Cell Rep. 2014;8(5):1595-606.
30. Besaratinia A, Tommasi S. Epigenetics of human melanoma: promises and challenges. J Mol Cell Biol. 2014;6(5):356-67.
31. Lahtz C, Pfeifer GP. Epigenetic changes of DNA repair genes in cancer. J Mol Cell Biol. 2011;3(1):51-8.
32. Li J, Shou J, Guo Y, Tang Y, Wu Y, Jia Z, Zhai Y, Chen Z, Xu Q, Wu Q. Efficient inversions and duplications of mammalian regulatory DNA elements and gene clusters by CRISPR/Cas9. J Mol Cell Biol. 2015;7(4):284-98.
33. Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. EMNLP. 2014;14:1532-43.
34. 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.
35. Consortium TF, et al. A promoter-level mammalian expression atlas. Nature. 2014;507(7493):462-70.
36. PrESSTo. http://pressto.binf.ku.dk/about.php. Accessed 8 Jan 2016.
37. Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research. 2016;44(11):e107.
38. Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015).
39. Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929-58.