LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome

Xingyu Tang; Peijie Zheng; Yuewu Liu; Yuhua Yao; Guohua Huang; Xingyu Tang; Peijie Zheng; Yuewu Liu; Yuhua Yao; Guohua Huang

doi:10.3934/mbe.2023048

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 1037-1057. doi: 10.3934/mbe.2023048

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LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome

1.
School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China
2.
College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China
3.
School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China

Academic Editor: Leyi Wei

Received: 22 July 2022 Revised: 12 September 2022 Accepted: 18 September 2022 Published: 24 October 2022

DNase I hypersensitive sites (DHSs) are a specific genomic region, which is critical to detect or understand cis-regulatory elements. Although there are many methods developed to detect DHSs, there is a big gap in practice. We presented a deep learning-based language model for predicting DHSs, named LangMoDHS. The LangMoDHS mainly comprised the convolutional neural network (CNN), the bi-directional long short-term memory (Bi-LSTM) and the feed-forward attention. The CNN and the Bi-LSTM were stacked in a parallel manner, which was helpful to accumulate multiple-view representations from primary DNA sequences. We conducted 5-fold cross-validations and independent tests over 14 tissues and 4 developmental stages. The empirical experiments showed that the LangMoDHS is competitive with or slightly better than the iDHS-Deep, which is the latest method for predicting DHSs. The empirical experiments also implied substantial contribution of the CNN, Bi-LSTM, and attention to DHSs prediction. We implemented the LangMoDHS as a user-friendly web server which is accessible at http:/www.biolscience.cn/LangMoDHS/. We used indices related to information entropy to explore the sequence motif of DHSs. The analysis provided a certain insight into the DHSs.
- DNase I hypersensitive site,
- genome,
- CNN,
- Bi-LSTM,
- deep learning
Citation: Xingyu Tang, Peijie Zheng, Yuewu Liu, Yuhua Yao, Guohua Huang. LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 1037-1057. doi: 10.3934/mbe.2023048

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Abstract

http:/www.biolscience.cn/LangMoDHS/. We used indices related to information entropy to explore the sequence motif of DHSs. The analysis provided a certain insight into the DHSs.]]>

References

[1]	T. Zhang, A. P. Marand, J. Jiang, PlantDHS: A database for DNase I hypersensitive sites in plants, Nucleic. Acids. Res., 44 (2016), D1148–D1153. https://doi.org/10.1093/nar/gkv962 doi: 10.1093/nar/gkv962
[2]	D. S. Gross, W. T. Garrard, Nuclease hypersensitive sites in chromatin, Annu. Rev. Biochem., 57 (1988), 159–197. https://doi.org/10.1146/annurev.bi.57.070188.001111 doi: 10.1146/annurev.bi.57.070188.001111
[3]	G. E. Crawford, I. E. Holt, J. C. Mullikin, D. Tai, E. D. Green, T. G. Wolfsberg, et al., Identifying gene regulatory elements by genome-wide recovery of DNase hypersensitive sites, Proc. Natl. Acad. Sci., 101 (2004), 992–997. https://doi.org/10.1073/pnas.0307540100 doi: 10.1073/pnas.0307540100
[4]	M. M. Carrasquillo, M. Allen, J. D. Burgess, X. Wang, S. L. Strickland, S. Aryal, et al., A candidate regulatory variant at the TREM gene cluster associates with decreased Alzheimer's disease risk and increased TREML1 and TREM2 brain gene expression, Alzheimer's Dementia, 13 (2017), 663–673. https://doi.org/10.1016/j.jalz.2016.10.005 doi: 10.1016/j.jalz.2016.10.005
[5]	W. Meuleman, A. Muratov, E. Rynes, J. Halow, K. Lee, D. Bates, et al., Index and biological spectrum of human DNase I hypersensitive sites, Nature, 584 (2020), 244–251. https://doi.org/10.1038/s41586-020-2559-3 doi: 10.1038/s41586-020-2559-3
[6]	M. T. Maurano, R. Humbert, E. Rynes, R. E. Thurman, E. Haugen, H. Wang, et al., Systematic localization of common disease-associated variation in regulatory DNA, Science, 337 (2012), 1190–1195. https://doi.org/10.1126/science.1222794 doi: 10.1126/science.1222794
[7]	J. Ernst, P. Kheradpour, T. S. Mikkelsen, N. Shoresh, L. D. Ward, C. B. Epstein, et al., Mapping and analysis of chromatin state dynamics in nine human cell types, Nature, 473 (2011), 43–49. https://doi.org/10.1038/nature09906 doi: 10.1038/nature09906
[8]	M. Mokry, M. Harakalova, F. W. Asselbergs, P. I. de Bakker, E. E. Nieuwenhuis, Extensive association of common disease variants with regulatory sequence, PLoS One, 11 (2016), e0165893. https://doi.org/10.1371/journal.pone.0165893 doi: 10.1371/journal.pone.0165893
[9]	D. Weghorn, F. Coulet, K. M. Olson, C. DeBoever, F. Drees, A. Arias, et al., Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer, Nat. Commun., 8 (2017), 1–16. https://doi.org/10.1038/s41467-017-00100-x doi: 10.1038/s41467-017-00100-x
[10]	W. Jin, Q. Tang, M. Wan, K. Cui, Y. Zhang, G. Ren, et al., Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples, Nature, 528 (2015), 142–146. https://doi.org/10.1038/nature15740 doi: 10.1038/nature15740
[11]	G. E. Crawford, S. Davis, P. C. Scacheri, G. Renaud, M. J. Halawi, M. R. Erdos, et al., DNase-chip: A high-resolution method to identify DNase I hypersensitive sites using tiled microarrays, Nat. Methods, 3 (2006), 503–509. https://doi.org/10.1038/nmeth888 doi: 10.1038/nmeth888
[12]	J. Cooper, Y. Ding, J. Song, K. Zhao, Genome-wide mapping of DNase I hypersensitive sites in rare cell populations using single-cell DNase sequencing, Nat. Protoc., 12 (2017), 2342–2354. https://doi.org/10.1038/nprot.2017.099 doi: 10.1038/nprot.2017.099
[13]	G. E. Crawford, I. E. Holt, J. Whittle, B. D. Webb, D. Tai, S. Davis, et al., Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS), Genome Res., 16 (2006), 123–131. https://doi.org/10.1101/gr.4074106 doi: 10.1101/gr.4074106
[14]	L. Song, G. E. Crawford, DNase-seq: A high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells, Cold Spring Harbor Protoc., 2010 (2010), pdb.prot5384. https://doi.org/10.1101/pdb.prot5384 doi: 10.1101/pdb.prot5384
[15]	W. Zhang, J. Jiang, Genome-wide mapping of DNase I hypersensitive sites in plants, in Plant Functional Genomics, Humana Press, 1284 (2015), 71–89. https://doi.org/10.1007/978-1-4939-2444-8_4
[16]	Y. Wang, K. Wang, Genome-wide identification of DNase I hypersensitive sites in plants, Curr. Protoc., 1 (2021), e148. https://doi.org/10.1002/cpz1.148 doi: 10.1002/cpz1.148
[17]	S. Wang, Q. Zhang, Z. Shen, Y. He, Z. Chen, J. Li, et al., Predicting transcription factor binding sites using DNA shape features based on shared hybrid deep learning architecture, Mol. Ther. Nucleic Acids, 24 (2021), 154–163. https://doi.org/10.1016/j.omtn.2021.02.014 doi: 10.1016/j.omtn.2021.02.014
[18]	Q. Zhang, Y. He, S. Wang, Z. Chen, Z. Guo, Z. Cui, et al., Base-resolution prediction of transcription factor binding signals by a deep learning framework, PLoS Comp. Biol., 18 (2022), e1009941. https://doi.org/10.1371/journal.pcbi.1009941 doi: 10.1371/journal.pcbi.1009941
[19]	S. Wang, Y. He, Z. Chen, Q. Zhang, FCNGRU: Locating transcription factor binding sites by combing fully convolutional neural network with gated recurrent unit, IEEE J. Biomed. Health. Inf., 26 (2021), 1883–1890. https://doi.org/10.1109/JBHI.2021.3117616 doi: 10.1109/JBHI.2021.3117616
[20]	Q. Zhang, Z. Shen, D. S. Huang, Predicting in-vitro transcription factor binding sites using DNA sequence+ shape, IEEE/ACM Trans. Comput. Biol. Bioinf., 18 (2019), 667–676. https://doi.org/10.1109/TCBB.2019.2947461 doi: 10.1109/TCBB.2019.2947461
[21]	Q. Zhang, S. Wang, Z. Chen, Y. He, Q. Liu, D. S. Huang, Locating transcription factor binding sites by fully convolutional neural network, Briefings Bioinf., 22 (2021), bbaa435. https://doi.org/10.1093/bib/bbaa435 doi: 10.1093/bib/bbaa435
[22]	Y. Zhang, Z. Wang, Y. Zeng, Y. Liu, S. Xiong, M. Wang, et al., A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape, Briefings Bioinf., 23 (2022), bbab525. https://doi.org/10.1093/bib/bbab525 doi: 10.1093/bib/bbab525
[23]	Y. Zhang, Z. Wang, Y. Zeng, J. Zhou, Q. Zou, High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method, Briefings Bioinf., 22 (2021), bbab273. https://doi.org/10.1093/bib/bbab273 doi: 10.1093/bib/bbab273
[24]	Y. He, Z. Shen, Q. Zhang, S. Wang, D. S. Huang, A survey on deep learning in DNA/RNA motif mining, Briefings Bioinf., 22 (2021), bbaa229. https://doi.org/10.1093/bib/bbaa229 doi: 10.1093/bib/bbaa229
[25]	W. S. Noble, S. Kuehn, R. Thurman, M. Yu, J. Stamatoyannopoulos, Predicting the in vivo signature of human gene regulatory sequences, Bioinformatics, 21 (2005), i338–i343. https://doi.org/10.1093/bioinformatics/bti1047 doi: 10.1093/bioinformatics/bti1047
[26]	B. Manavalan, T. H. Shin, G. Lee, DHSpred: Support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest, Oncotarget, 9 (2018), 1944. https://doi.org/10.18632/oncotarget.23099 doi: 10.18632/oncotarget.23099
[27]	S. Zhang, W. Zhuang, Z. Xu, Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components, Anal. Biochem., 549 (2018), 149–156. https://doi.org/10.1016/j.ab.2018.03.025 doi: 10.1016/j.ab.2018.03.025
[28]	Y. Liang, S. Zhang, IDHS-DMCAC: Identifying DNase I hypersensitive sites with balanced dinucleotide-based detrending moving-average cross-correlation coefficient, SAR QSAR Environ. Res., 30 (2019), 429–445. https://doi.org/10.1080/1062936X.2019.1615546 doi: 10.1080/1062936X.2019.1615546
[29]	S. Zhang, Z. Duan, W. Yang, C. Qian, Y. You, IDHS-DASTS: Identifying DNase I hypersensitive sites based on LASSO and stacking learning, Mol. Omics, 17 (2021), 130–141. https://doi.org/10.1039/D0MO00115E doi: 10.1039/D0MO00115E
[30]	B. Liu, R. Long, K. C. Chou, IDHS-EL: Identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework, Bioinformatics, 32 (2016), 2411–2418. https://doi.org/10.1093/bioinformatics/btw186 doi: 10.1093/bioinformatics/btw186
[31]	S. Zhang, J. Lin, L. Su, Z. Zhou, PDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory, Anal. Biochem., 564 (2019), 54–63. https://doi.org/10.1016/j.ab.2018.10.018 doi: 10.1016/j.ab.2018.10.018
[32]	Y. Zheng, H. Wang, Y. Ding, F. Guo, CEPZ: A novel predictor for identification of DNase I hypersensitive sites, IEEE/ACM Trans. Comput. Biol. Bioinf., 18 (2021), 2768–2774. https://doi.org/10.1109/TCBB.2021.3053661 doi: 10.1109/TCBB.2021.3053661
[33]	S. Zhang, Q. Yu, H. He, F. Zhu, P. Wu, L. Gu, et al., IDHS-DSAMS: Identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree, Genomics, 112 (2020), 1282–1289. https://doi.org/10.1016/j.ygeno.2019.07.017 doi: 10.1016/j.ygeno.2019.07.017
[34]	S. Zhang, T. Xue, Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting, Mol. Genet. Genomics, 295 (2020), 1431–1442. https://doi.org/10.1007/s00438-020-01711-8 doi: 10.1007/s00438-020-01711-8
[35]	Z. C. Xu, S. Y. Jiang, W. R. Qiu, Y. C. Liu, X. Xiao, IDHSs-PseTNC: Identifying DNase I hypersensitive sites with pseuo trinucleotide component by deep sparse auto-encoder, Lett. Org. Chem., 14 (2017), 655–664. https://doi.org/10.2174/1570178614666170213102455 doi: 10.2174/1570178614666170213102455
[36]	C. Lyu, L. Wang, J. Zhang, Deep learning for DNase I hypersensitive sites identification, BMC genomics, 19 (2018), 155–165. https://doi.org/10.1186/s12864-018-5283-8 doi: 10.1186/s12864-018-5283-8
[37]	P. Feng, N. Jiang, N. Liu, Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions, Sci. World J., 2014 (2014), 740506. https://doi.org/10.1155/2014/740506 doi: 10.1155/2014/740506
[38]	W. Chen, T. Y. Lei, D. C. Jin, H. Lin, K. C. Chou, PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition, Anal. Biochem., 456 (2014), 53–60. https://doi.org/10.1016/j.ab.2014.04.001 doi: 10.1016/j.ab.2014.04.001
[39]	W. Chen, H. Lin, K. C. Chou, Pseudo nucleotide composition or PseKNC: An effective formulation for analyzing genomic sequences, Mol. Biosyst., 11 (2015), 2620–2634. https://doi.org/10.1039/C5MB00155B doi: 10.1039/C5MB00155B
[40]	B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, K. C. Chou, Pse-in-One: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nucleic Acids Res., 43 (2015), W65–W71. https://doi.org/10.1093/nar/gkv458 doi: 10.1093/nar/gkv458
[41]	S. Zhang, Z. Zhou, X. Chen, Y. Hu, L. Yang, PDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine, J. Theor. Biol., 426 (2017), 126–133. https://doi.org/10.1016/j.jtbi.2017.05.030 doi: 10.1016/j.jtbi.2017.05.030
[42]	K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell., 37 (2015), 1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824 doi: 10.1109/TPAMI.2015.2389824
[43]	F. Y. Dao, H. Lv, W. Su, Z. J. Sun, Q. L. Huang, H. Lin, IDHS-deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network, Briefings Bioinf., 22 (2021), bbab047. https://doi.org/10.1093/bib/bbab047 doi: 10.1093/bib/bbab047
[44]	C. E. Breeze, J. Lazar, T. Mercer, J. Halow, I. Washington, K. Lee, et al., Atlas and developmental dynamics of mouse DNase I hypersensitive sites, bioRxiv, 2020 (2020). https://doi.org/10.1101/2020.06.26.172718 doi: 10.1101/2020.06.26.172718
[45]	W. Li, A. Godzik, Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences, Bioinformatics, 22 (2006), 1658–1659. https://doi.org/10.1093/bioinformatics/btl158 doi: 10.1093/bioinformatics/btl158
[46]	L. Fu, B. Niu, Z. Zhu, S. Wu, W. Li, CD-HIT: Accelerated for clustering the next-generation sequencing data, Bioinformatics, 28 (2012), 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 doi: 10.1093/bioinformatics/bts565
[47]	X. Tang, P. Zheng, X. Li, H. Wu, D. Q. Wei, Y. Liu, et al., Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species, Methods, 204 (2022), 142–150. https://doi.org/10.1016/j.ymeth.2022.04.011 doi: 10.1016/j.ymeth.2022.04.011
[48]	T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, preprint, arXiv: 1301.3781.
[49]	T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, in Advances in neural information processing systems, 26 (2013), 3111–3119.
[50]	K. Fukushima, S. Miyake, Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Recognt., 15 (1982), 455–469. https://doi.org/10.1016/0031-3203(82)90024-3 doi: 10.1016/0031-3203(82)90024-3
[51]	D. H. Hubel, T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, J. Physiol., 160 (1962), 106. https://doi.org/10.1113/jphysiol.1962.sp006837 doi: 10.1113/jphysiol.1962.sp006837
[52]	Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, et al., Handwritten digit recognition with a back-propagation network, in Advances in neural information processing systems, Morgan Kaufmann, 2 (1989), 396–404.
[53]	S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput., 9 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 doi: 10.1162/neco.1997.9.8.1735
[54]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, et al., Attention is all you need, in Advances in neural information processing systems, 30 (2017), 6000–6010.
[55]	C. Raffel, D. P. Ellis, Feed-forward networks with attention can solve some long-term memory problems, preprint, arXiv: 1512.08756.

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