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Molecular Genetics and Genomics

, Volume 293, Issue 4, pp 1035–1049 | Cite as

pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines

  • Shanxin Zhang
  • Minjun Chang
  • Zhiping Zhou
  • Xiaofeng Dai
  • Zhenghong Xu
Methods Paper

Abstract

DNase I hypersensitive sites (DHSs) are hallmarks of chromatin zones containing transcriptional regulatory elements, making them critical in understanding the regulatory mechanisms of gene expression. Although large amounts of DHSs in the plant genome have been identified by high-throughput techniques, current DHSs obtained from experimental methods cover only a fraction of plant species and cell processes. Furthermore, these experimental methods are both time-consuming and expensive. Hence, it is urgent to develop automated computational means to efficiently and accurately predict DHSs in the plant genome. Recently, several methods have been proposed to predict the DHSs. However, all these methods took a lot of time to build the model, making them inappropriate for data with massive volume. In the present work, a new ensemble extreme learning machine (ELM)-based model called pDHS-ELM was proposed to predict the DHSs in the plant genome by fusing two different modes of pseudo-nucleotide composition. Here, two kinds of features including reverse complement kmer and pseudo-nucleotide composition were used to represent the DHSs. The ELM model was used to build the base classifiers. Then, an ensemble framework was employed to combine the outputs of these base classifiers. When applied to DHSs in Arabidopsis thaliana and rice (Oryza sativa) genome, the proposed method could obtain accuracies up to 88.48 and 87.58%, respectively. Compared with the state-of-the-art techniques, pDHS-ELM achieved higher sensitivity, specificity, and Matthew’s correlation coefficient with much less training and test time. By employing pDHS-ELM, we identified 42,370 and 103,979 DHSs in A. thaliana and rice genome, respectively. The predicted DHSs were depleted of bulk nucleosomes and were tightly associated with transcription factors. Approximately 90% of the predicted DHSs were overlapped with transcription factors. Meanwhile, we demonstrated that the predicted DHSs were also associated with DNA methylation, nucleosome positioning/occupancy, and histone modification. This result suggests that pDHS-ELM can be considered as a new promising and powerful tool for transcriptional regulatory elements analysis. Our pDHS-ELM tool is available from the following website https://github.com/shanxinzhang/pDHS-ELM/.

Keywords

DNase I hypersensitive sites Extreme learning machine Plant Prediction Ensemble 

Notes

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No. JUSRP115A27).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

438_2018_1436_MOESM1_ESM.docx (5.4 mb)
Supplementary material 1 (DOCX 5499 KB)

References

  1. Cao J, Lin Z et al (2012) Voting based extreme learning machine. Inf Sci 185(1):66–77CrossRefGoogle Scholar
  2. Celniker SE, Dillon LAL et al (2009) Unlocking the secrets of the genome. Nature 459(7249):927–930CrossRefPubMedPubMedCentralGoogle Scholar
  3. Chen W, Zhang X et al (2015) PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions. Bioinformatics 31(1):119–120CrossRefPubMedGoogle Scholar
  4. Chen W, Tang H et al (2016) iRNA-PseU: identifying RNA pseudouridine sites. Mol Ther 5(7):e332.  https://doi.org/10.1038/mtna.2016.37 CrossRefGoogle Scholar
  5. Cheng X, Zhao S-G et al (2017) iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 33(3):341–346PubMedGoogle Scholar
  6. Chou K-C (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273(1):236–247CrossRefPubMedPubMedCentralGoogle Scholar
  7. Fan YX, Shen HB (2014) Predicting pupylation sites in prokaryotic proteins using pseudo-amino acid composition and extreme learning machine. Neurocomputing 128(5):267–272CrossRefGoogle Scholar
  8. Feng P, Jiang N et al (2014) Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions. Sci World J.  https://doi.org/10.1155/2014/740506 CrossRefGoogle Scholar
  9. Freeling M, Subramaniam S (2009) Conserved noncoding sequences (CNSs) in higher plants. Curr Opin Plant Biol 12(2):126–132CrossRefPubMedGoogle Scholar
  10. Henikoff S, Henikoff JG et al (2009) Genome-wide profiling of salt fractions maps physical properties of chromatin. Genome Res 19(3):460–469CrossRefPubMedPubMedCentralGoogle Scholar
  11. Huang GB, Zhu QY et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501CrossRefGoogle Scholar
  12. Huang GB, Wang DH et al (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122CrossRefGoogle Scholar
  13. Huang GB, Zhou H et al (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B 42(2):513–529CrossRefGoogle Scholar
  14. Jia J, Liu Z et al (2016) pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. J Theor Biol 394:223–230CrossRefPubMedGoogle Scholar
  15. Jiang J (2015) The ‘dark matter’ in the plant genomes: non-coding and unannotated DNA sequences associated with open chromatin. Curr Opin Plant Biol 24:17–23CrossRefPubMedGoogle Scholar
  16. Jin C, Zang C et al (2009) H3.3/H2A.Z double variant-containing nucleosomes mark ‘nucleosome-free regions’ of active promoters and other regulatory regions. Nat Genet 41(8):941–945CrossRefPubMedPubMedCentralGoogle Scholar
  17. Jin W, Tang Q et al (2015) Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528(7580):142–146PubMedPubMedCentralGoogle Scholar
  18. Kabir M, Yu D-J (2017) Predicting DNase I hypersensitive sites via un-biased pseudo trinucleotide composition. Chemom Intell Lab Syst 167:78–84CrossRefGoogle Scholar
  19. Lan Y, Soh YC et al (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72(13):3391–3395CrossRefGoogle Scholar
  20. Liu N, Wang H (2010) Ensemble based extreme learning machine. IEEE Signal Process Lett 17(8):754–757CrossRefGoogle Scholar
  21. Liu B, Liu F et al (2015a) Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43(W1):W65–W71CrossRefPubMedPubMedCentralGoogle Scholar
  22. Liu G, Xing Y et al (2015b) Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae. J Theor Biol 382:15–22CrossRefPubMedGoogle Scholar
  23. Liu B, Long R et al (2016) iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework. Bioinformatics 32(16):2411–2418CrossRefPubMedGoogle Scholar
  24. Liu Y, Tian T et al (2017) PCSD: a plant chromatin state database. Nucleic Acids Res 46(D1):D1157–D1167CrossRefPubMedCentralGoogle Scholar
  25. Noble WS, Kuehn S et al (2005) Predicting the in vivo signature of human gene regulatory sequences. Bioinformatics 21(Suppl 1):i338–i343CrossRefPubMedGoogle Scholar
  26. Pajoro A, Madrigal P et al (2014) Dynamics of chromatin accessibility and gene regulation by MADS-domain transcription factors in flower development. Genome Biol 15(3):R41.  https://doi.org/10.1186/gb-2014-15-3-r41 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Qiu W-R, Sun B-Q et al (2016) iPTM-mLys: identifying multiple lysine PTM sites and their different types. Bioinformatics 32(20):3116–3123CrossRefPubMedPubMedCentralGoogle Scholar
  28. Savojardo C, Fariselli P et al (2011) Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines. Bioinformatics 27(22):3123–3128CrossRefPubMedGoogle Scholar
  29. Sullivan AM, Arsovski AA et al (2014) Mapping and dynamics of regulatory DNA and transcription factor networks in A. thaliana. Cell Rep 8(6):2015–2030CrossRefPubMedGoogle Scholar
  30. Turco G, Schnable JC et al (2013) Automated conserved non-coding sequence (CNS) discovery reveals differences in gene content and promoter evolution among grasses. Front Plant Sci 4:170.  https://doi.org/10.3389/fpls.2013.00170 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Wang DD, Wang R et al (2014) Fast prediction of protein–protein interaction sites based on extreme learning machines. Neurocomputing 128(128):258–266CrossRefGoogle Scholar
  32. Xing P, Su R et al (2017) Identifying N(6)-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine. Sci Rep 7:46757.  https://doi.org/10.1038/srep46757 CrossRefPubMedPubMedCentralGoogle Scholar
  33. You Z-H, Lei Y-K et al (2013) Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinform 14(8):S10.  https://doi.org/10.1186/1471-2105-14-s8-s10 CrossRefGoogle Scholar
  34. Zhang W, Wu Y et al (2012a) High-resolution mapping of open chromatin in the rice genome. Genome Res 22(1):151–162CrossRefPubMedPubMedCentralGoogle Scholar
  35. Zhang W, Zhang T et al (2012b) Genome-wide identification of regulatory DNA elements and protein-binding footprints using signatures of open chromatin in Arabidopsis. Plant Cell 24(7):2719–2731CrossRefPubMedPubMedCentralGoogle Scholar
  36. Zhang T, Marand A et al (2015) PlantDHS: a database for DNase I hypersensitive sites in plants. Nucleic Acids Res 44:D1148–D1153CrossRefPubMedPubMedCentralGoogle Scholar
  37. Zhang S, Zhou Z et al (2017) pDHS-SVM: a prediction method for plant DNase I hypersensitive sites based on support vector machine. J Theor Biol 426:126–133CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shanxin Zhang
    • 1
    • 2
  • Minjun Chang
    • 3
  • Zhiping Zhou
    • 1
  • Xiaofeng Dai
    • 4
    • 5
  • Zhenghong Xu
    • 2
    • 4
    • 5
  1. 1.Engineering Research Center of Internet of Things Technology Applications (Ministry of Education), School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  2. 2.School of Medicine and PharmaceuticalsJiangnan UniversityWuxiChina
  3. 3.College of Letters and ScienceUniversity of California, BerkeleyBerkeleyUSA
  4. 4.School of BiotechnologyJiangnan UniversityWuxiChina
  5. 5.National Engineering Laboratory for Cereal Fermentation TechnologyJiangnan UniversityWuxiChina

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