Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

PLoS ONE

Volumn 12, Issue 2, 2017, Pages

  Umarov, Ramzan Kh ^a   Solovyev, Victor V ^b  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b SOFTBERRY INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TATA BINDING PROTEIN ASSOCIATED FACTOR;

ACCURACY; ARABIDOPSIS; ARTICLE; BACILLUS SUBTILIS; CONTROLLED STUDY; ESCHERICHIA COLI; EUKARYOTE; GENE EXPRESSION; GENETIC TRANSCRIPTION; LEARNING; NERVE CELL NETWORK; NONHUMAN; PROKARYOTE; PROMOTER REGION; RECOGNITION; TRANSCRIPTION INITIATION; TRANSCRIPTION REGULATION; ANIMAL; ARTIFICIAL NEURAL NETWORK; BIOLOGY; DNA SEQUENCE; EUKARYOTIC CELL; GENETICS; HUMAN; METABOLISM; PROCEDURES; PROKARYOTIC CELL;

ANIMALS; COMPUTATIONAL BIOLOGY; EUKARYOTIC CELLS; HUMANS; NEURAL NETWORKS (COMPUTER); PROKARYOTIC CELLS; PROMOTER REGIONS, GENETIC; SEQUENCE ANALYSIS, DNA;

EID: 85011697811     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0171410     Document Type: Article

References (43)

1. Sandelin A, Carninci P, Lenhard B, Ponjavic J, Hayashizaki Y, Hume D. Mammalian RNA polymerase II core promoters: insights from genome-wide studies. Nat Rev Genet. 2007; 8(6):424-436. doi: 10.1038/nrg2026 PMID: 17486122
2. Solovyev V, Shahmuradov I, Salamov A. Identification of promoter regions and regulatory sites. Methods Mol Biol. 2010; 674:57-83. doi: 10.1007/978-1-60761-854-6-5 PMID: 20827586
3. Harley CB, Reynolds RP. Analysis of E.coli promoter sequences. Nucleic Acids Res. 1987; 15:2343-2361. doi: 10.1093/nar/15.5.2343 PMID: 3550697
4. Lisser S, Margalit H. Compilation of e.coli mrna promoter sequences. Nucleic Acids Res. 1993; 21:1507-1516. doi: 10.1093/nar/21.7.1507 PMID: 8479900
5. Jacques P, Rodrigue S, Gaudreau L, Goulet J, Brzezinski R. Detection of prokaryotic promoters from the genomic distribution of hexanucleotide pairs. BMC Bioinformatics. 2006; 7:423. doi: 10.1186/1471-2105-7-423 PMID: 17014715
6. Meysman P, Collado-Vides J, Morett E, Viola R, Engelen Kea. Structural properties of prokaryotic promoter regions correlate with functional features. PLoS ONE. 2014;9 doi: 10.1371/journal.pone. 0088717 PMID: 24516674
7. Fickett J, Hatzigeorgiou A. Eukaryotic Promoter Recognition. Genome Res. 1997; 7:861-878. PMID: 9314492
8. Bajic V, Brent M, Brown R, Frankish A, Harrow J, Ohler U, et al. Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment. Genome Biol. 2006; 7:1-13. doi: 10.1186/gb-2006-7-s1-s3 PMID: 16925837
9. Shahmuradov I, Solovyev V, Gammerman A. Plant promoter prediction with confidence estimation. Nucleic Acids Research. 2005; 33(3):1069-1076. doi: 10.1093/nar/gki247 PMID: 15722481
10. Azad A, Shahid S, Noman N, Lee H. Prediction of plant promoters based on hexamers and random triplet pair analysis. Algorithms Mol Biol. 2011; 6:19. doi: 10.1186/1748-7188-6-19 PMID: 21711543
11. Reese M. Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput Chem. 2001; 26(1):51-56. doi: 10.1016/S0097-8485(01)00099-7 PMID: 11765852
12. Prestridge D. Predicting Pol II promoter sequences using transcription factor binding sites. J Mol Biol. 1995; 249(5):923-932. doi: 10.1006/jmbi.1995.0349 PMID: 7791218
13. Knudsen S. Promoter2.0: for the recognition of PolII promoter sequences. Bioinformatics. 1999; 15 (5):356-361. doi: 10.1093/bioinformatics/15.5.356 PMID: 10366655
14. Anwar F, Baker M, Jabid T, Hasan M, Shoyaib M, Khan H, et al. Pol II promoter prediction using characteristic 4-mer motifs: A machine learning approach. BMC Bioinformatics. 2008; 9:414. doi: 10.1186/1471-2105-9-414 PMID: 18834544
15. Gordon L, Chervonenkis A, Gammerman A, Shahmuradov I, Solovyev V. Sequence alignment kernel for recognition of promoter regions. Bioinformatics. 2003; 19(15):1964-1971. doi: 10.1093/bioinformatics/btg265 PMID: 14555630
16. Solovyev V, Salamov A. Automatic Annotation of Microbial Genomes and Metagenomic Sequences. In: Metagenomics and its Applications in Agriculture. Nova Science Publishers, p: Biomedicine and Environmental Studies (Ed. Li R.W.); 2011. p. 61-78.
17. Wang H, Benham C. Promoter prediction and annotation of microbial genomes based on DNA sequence and structural responses to superhelical stress. BMC Bioinformatics. 2006; 7:248. doi: 10.1186/1471-2105-7-248 PMID: 16677393
18. Yamamoto Y, Ichida H, Abe T, Suzuki Y, Sugano S, Obokata J. Differentiation of core promoter architecture between plants and mammals revealed by LDSS analysis. Nucleic Acids Res. 2007; 35 (18):6219-6226. doi: 10.1093/nar/gkm685 PMID: 17855401
19. Civan P, Svec M. Genome-wide analysis of rice (Oryza sativa L. subsp. japonica) TATA box and Y Patch promoter elements. Genome. 2009; 52(3):294-297. doi: 10.1139/G09-001 PMID: 19234558
20. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 2012; p. 1097-1105.
21. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going Deeper With Convolutions. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015; p. 1-9.
22. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521:436-444. doi: 10.1038/nature14539 PMID: 26017442
23. Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. 2015; 61:85-117. doi: 10.1016/j.neunet.2014.09.003 PMID: 25462637
24. Zhou J T O. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015; 12(10):931-934. doi: 10.1038/nmeth.3547 PMID: 26301843
25. Quang DXX. DanQ:a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016;. doi: 10.1093/nar/gkw226 PMID: 27084946
26. Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Net. 2005; 18:602-610. doi: 10.1016/j.neunet.2005.06.042 PMID: 16112549
27. Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics. 2016; 2(12):1832-1839. doi: 10.1093/bioinformatics/btw074 PMID: 26873929
28. Alipanahi B, Delong A, Weirauch M, Frey BJ. Predicting the sequence specificities of DNA-and RNAbinding proteins by deep learning. Nat Biotechnol. 2015; 33:831-838. doi: 10.1038/nbt.3300 PMID: 26213851
29. Gama-Castro S, Salgado H, Santos-Zavaleta A, et al LT D. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res. 2016; 44:133-143. doi: 10.1093/nar/gkv1156
30. Ishii T, Yoshida Ki, Terai G, Fujita Y, Nakai K. DBTBS: A database of Bacillus subtilis promoters and transcription factors. Nucleic acids research. 2001; 29(1):278-280. doi: 10.1093/nar/29.1.278 PMID: 11125112
31. Dreos R, Ambrosini G, Perier R, Bucher P. EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era. Nucleic Acids Research. 2013; 41(Database issue)(D157-64). doi: 10.1093/nar/gks1233 PMID: 23193273
32. Chollet F. Keras: Deep Learning library for Theano and TensorFlow. GitHub; 2015. Available from: https://github.com/fchollet/keras.
33. Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow IJ, Bergeron A, et al. Theano: new features and speed improvements; 2012. Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop.
34. Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints. 2016;abs/1605.02688.
35. Nickolls J, Buck I, Garland M, Skadron K. Scalable Parallel Programming with CUDA. ACM Queue. 2008; 6(2):40-53. doi: 10.1145/1365490.1365500
36. Kingma D, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014;.
37. Burset M, Guigo R. Evaluation of gene structure prediction programs. Genomics. 1996; 34(3):353-367. doi: 10.1006/geno.1996.0298 PMID: 8786136
38. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. BiochemBiophysActa. 1975; 405:442-451. doi: 10.1016/0005-2795(75)90109-9 PMID: 1180967
39. Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, et al. The Ensembl gene annotation system. Database. 2016; 2016:baw093. doi: 10.1093/database/baw093 PMID: 27337980
40. Suzuki A, Wakaguri H, Yamashita R, Kawano S, Tsuchihara K, Sugano S, et al. DBTSS as an integrative platform for transcriptome, epigenome and genome sequence variation data. Nucleic acids research. 2015; 43(D1):D87-D91. doi: 10.1093/nar/gku1080 PMID: 25378318
41. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; 12:2825-2830.
42. Schneider TD, Stephens RM. Sequence logos: A new way to display consensus sequences. Nucleic acids research. 1990; 18(20):6097-6100. doi: 10.1093/nar/18.20.6097 PMID: 2172928
43. Tatarinova T, Kryshchenko A, Triska M, Hassan M, Murphy D, Neely M, et al. NPEST: A nonparametric method and a database for Transcription Start Site prediction. Quantitative biology. 2013; 1(4):261-271. doi: 10.1007/s40484-013-0022-2 PMID: 25197613