IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0163491.html
   My bibliography  Save this article

LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines

Author

Listed:
  • Jingting Xu
  • Hong Hu
  • Yang Dai

Abstract

Background: The identification of enhancers is a challenging task. Various types of epigenetic information including histone modification have been utilized in the construction of enhancer prediction models based on a diverse panel of machine learning schemes. However, DNA methylation profiles generated from the whole genome bisulfite sequencing (WGBS) have not been fully explored for their potential in enhancer prediction despite the fact that low methylated regions (LMRs) have been implied to be distal active regulatory regions. Method: In this work, we propose a prediction framework, LMethyR-SVM, using LMRs identified from cell-type-specific WGBS DNA methylation profiles and a weighted support vector machine learning framework. In LMethyR-SVM, the set of cell-type-specific LMRs is further divided into three sets: reliable positive, like positive and likely negative, according to their resemblance to a small set of experimentally validated enhancers in the VISTA database based on an estimated non-parametric density distribution. Then, the prediction model is obtained by solving a weighted support vector machine. Results: We demonstrate the performance of LMethyR-SVM by using the WGBS DNA methylation profiles derived from the human embryonic stem cell type (H1) and the fetal lung fibroblast cell type (IMR90). The predicted enhancers are highly conserved with a reasonable validation rate based on a set of commonly used positive markers including transcription factors, p300 binding and DNase-I hypersensitive sites. In addition, we show evidence that the large fraction of the LMethyR-SVM predicted enhancers are not predicted by ChromHMM in H1 cell type and they are more enriched for the FANTOM5 enhancers. Conclusion: Our work suggests that low methylated regions detected from the WGBS data are useful as complementary resources to histone modification marks in developing models for the prediction of cell-type-specific enhancers.

Suggested Citation

  • Jingting Xu & Hong Hu & Yang Dai, 2016. "LMethyR-SVM: Predict Human Enhancers Using Low Methylated Regions based on Weighted Support Vector Machines," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-18, September.
  • Handle: RePEc:plo:pone00:0163491
    DOI: 10.1371/journal.pone.0163491
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163491
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0163491&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0163491?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ryan Lister & Mattia Pelizzola & Yasuyuki S. Kida & R. David Hawkins & Joseph R. Nery & Gary Hon & Jessica Antosiewicz-Bourget & Ronan O’Malley & Rosa Castanon & Sarit Klugman & Michael Downes & Ruth , 2011. "Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells," Nature, Nature, vol. 471(7336), pages 68-73, March.
    2. Azzalini, Adelchi & Menardi, Giovanna, 2014. "Clustering via Nonparametric Density Estimation: The R Package pdfCluster," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i11).
    3. Nathaniel D. Heintzman & Gary C. Hon & R. David Hawkins & Pouya Kheradpour & Alexander Stark & Lindsey F. Harp & Zhen Ye & Leonard K. Lee & Rhona K. Stuart & Christina W. Ching & Keith A. Ching & Jess, 2009. "Histone modifications at human enhancers reflect global cell-type-specific gene expression," Nature, Nature, vol. 459(7243), pages 108-112, May.
    4. Yiming Lu & Wubin Qu & Guangyu Shan & Chenggang Zhang, 2015. "DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.
    5. Michael B. Stadler & Rabih Murr & Lukas Burger & Robert Ivanek & Florian Lienert & Anne Schöler & Erik van Nimwegen & Christiane Wirbelauer & Edward J. Oakeley & Dimos Gaidatzis & Vijay K. Tiwari & Di, 2011. "DNA-binding factors shape the mouse methylome at distal regulatory regions," Nature, Nature, vol. 480(7378), pages 490-495, December.
    6. Susan E. Celniker & Laura A. L. Dillon & Mark B. Gerstein & Kristin C. Gunsalus & Steven Henikoff & Gary H. Karpen & Manolis Kellis & Eric C. Lai & Jason D. Lieb & David M. MacAlpine & Gos Micklem & F, 2009. "Unlocking the secrets of the genome," Nature, Nature, vol. 459(7249), pages 927-930, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:plo:pone00:0031414 is not listed on IDEAS
    2. Anyou Wang & Ying Du & Qianchuan He & Chunxiao Zhou, 2013. "A Quantitative System for Discriminating Induced Pluripotent Stem Cells, Embryonic Stem Cells and Somatic Cells," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-10, February.
    3. Dafne Ibarra-Morales & Michael Rauer & Piergiuseppe Quarato & Leily Rabbani & Fides Zenk & Mariana Schulte-Sasse & Francesco Cardamone & Alejandro Gomez-Auli & Germano Cecere & Nicola Iovino, 2021. "Histone variant H2A.Z regulates zygotic genome activation," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. Grigorios Fanourgakis & Laura Gaspa-Toneu & Pavel A. Komarov & Panagiotis Papasaikas & Evgeniy A. Ozonov & Sebastien A. Smallwood & Antoine H. F. M. Peters, 2025. "DNA methylation modulates nucleosome retention in sperm and H3K4 methylation deposition in early mouse embryos," Nature Communications, Nature, vol. 16(1), pages 1-22, December.
    5. Dahong Chen & Catherine E. McManus & Behram Radmanesh & Leah H. Matzat & Elissa P. Lei, 2021. "Temporal inhibition of chromatin looping and enhancer accessibility during neuronal remodeling," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    6. Lakhal-Chaieb Lajmi & Greenwood Celia M.T. & Ouhourane Mohamed & Zhao Kaiqiong & Abdous Belkacem & Oualkacha Karim, 2017. "A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 313-331, December.
    7. repec:plo:pcbi00:1002968 is not listed on IDEAS
    8. Shijia Zhu & Guohua Wang & Bo Liu & Yadong Wang, 2013. "Modeling Exon Expression Using Histone Modifications," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-15, June.
    9. Graeme J. Thorn & Christopher T. Clarkson & Anne Rademacher & Hulkar Mamayusupova & Gunnar Schotta & Karsten Rippe & Vladimir B. Teif, 2022. "DNA sequence-dependent formation of heterochromatin nanodomains," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    10. Yanting Luo & Jianlin He & Xiguang Xu & Ming-an Sun & Xiaowei Wu & Xuemei Lu & Hehuang Xie, 2018. "Integrative single-cell omics analyses reveal epigenetic heterogeneity in mouse embryonic stem cells," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-21, March.
    11. Tayma Handal & Sarah Juster & Manar Abu Diab & Shira Yanovsky-Dagan & Fouad Zahdeh & Uria Aviel & Roni Sarel-Gallily & Shir Michael & Ester Bnaya & Shulamit Sebban & Yosef Buganim & Yotam Drier & Vinc, 2024. "Differentiation shifts from a reversible to an irreversible heterochromatin state at the DM1 locus," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    12. Hao Wu & Hongkai Ji, 2014. "PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-9, March.
    13. Anne Senabouth & Maciej Daniszewski & Grace E. Lidgerwood & Helena H. Liang & Damián Hernández & Mehdi Mirzaei & Stacey N. Keenan & Ran Zhang & Xikun Han & Drew Neavin & Louise Rooney & Maria Isabel G, 2022. "Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    14. Sun Shuying & Yu Xiaoqing, 2016. "HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 55-67, March.
    15. Yu Xiaoqing & Sun Shuying, 2016. "HMM-DM: identifying differentially methylated regions using a hidden Markov model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 69-81, March.
    16. Christopher G Bell & Sarah Finer & Cecilia M Lindgren & Gareth A Wilson & Vardhman K Rakyan & Andrew E Teschendorff & Pelin Akan & Elia Stupka & Thomas A Down & Inga Prokopenko & Ian M Morison & Jonat, 2010. "Integrated Genetic and Epigenetic Analysis Identifies Haplotype-Specific Methylation in the FTO Type 2 Diabetes and Obesity Susceptibility Locus," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-12, November.
    17. Maria Arez & Melanie Eckersley-Maslin & Tajda Klobučar & João Gilsa Lopes & Felix Krueger & Annalisa Mupo & Ana Cláudia Raposo & David Oxley & Samantha Mancino & Anne-Valerie Gendrel & Bruno Bernardes, 2022. "Imprinting fidelity in mouse iPSCs depends on sex of donor cell and medium formulation," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    18. Yi Jin & Yulin He & Defa Huang, 2021. "An Improved Variable Kernel Density Estimator Based on L 2 Regularization," Mathematics, MDPI, vol. 9(16), pages 1-12, August.
    19. repec:plo:pone00:0060240 is not listed on IDEAS
    20. Tonellato, Stefano F., 2020. "Bayesian nonparametric clustering as a community detection problem," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    21. Sammy Villa & Pankaj Dwivedi & Aaron Stahl & Trent Hinkle & Christopher M. Rose & Donald S. Kirkpatrick & Seth M. Tomchik & Vishva M. Dixit & Fred W. Wolf, 2024. "OTUD6 deubiquitination of RPS7/eS7 on the free 40 S ribosome regulates global protein translation and stress," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    22. Giovanna Menardi, 2016. "A Review on Modal Clustering," International Statistical Review, International Statistical Institute, vol. 84(3), pages 413-433, December.
    23. Fasil Tekola-Ayele & Cuilin Zhang & Jing Wu & Katherine L Grantz & Mohammad L Rahman & Deepika Shrestha & Marion Ouidir & Tsegaselassie Workalemahu & Michael Y Tsai, 2020. "Trans-ethnic meta-analysis of genome-wide association studies identifies maternal ITPR1 as a novel locus influencing fetal growth during sensitive periods in pregnancy," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-20, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0163491. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.