IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0233630.html
   My bibliography  Save this article

Characterization of histone modification patterns and prediction of novel promoters using functional principal component analysis

Author

Listed:
  • Mijeong Kim
  • Shili Lin

Abstract

Characterization of distinct histone methylation and acetylation binding patterns in promoters and prediction of novel regulatory regions remains an important area of genomic research, as it is hypothesized that distinct chromatin signatures may specify unique genomic functions. However, methods that have been proposed in the literature are either descriptive in nature or are fully parametric and hence more restrictive in pattern discovery. In this article, we propose a two-step non-parametric statistical inference procedure to characterize unique histone modification patterns and apply it to analyzing the binding patterns of four histone marks, H3K4me2, H3K4me3, H3K9ac, and H4K20me1, in human B-lymphoblastoid cells. In the first step, we used a functional principal component analysis method to represent the concatenated binding patterns of these four histone marks around the transcription start sites as smooth curves. In the second step, we clustered these curves to reveal several unique classes of binding patterns. These uncovered patterns were used in turn to scan the whole-genome to predict novel and alternative promoters. Our analyses show that there are three distinct promoter binding patterns of active genes. Further, 19654 regions not within known gene promoters were found to overlap with human ESTs, CpG islands, or common SNPs, indicative of their potential role in gene regulation, including being potential novel promoter regions.

Suggested Citation

  • Mijeong Kim & Shili Lin, 2020. "Characterization of histone modification patterns and prediction of novel promoters using functional principal component analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
  • Handle: RePEc:plo:pone00:0233630
    DOI: 10.1371/journal.pone.0233630
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0233630
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0233630&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0233630?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. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    2. Jason Ernst & Pouya Kheradpour & Tarjei S. Mikkelsen & Noam Shoresh & Lucas D. Ward & Charles B. Epstein & Xiaolan Zhang & Li Wang & Robbyn Issner & Michael Coyne & Manching Ku & Timothy Durham & Mano, 2011. "Mapping and analysis of chromatin state dynamics in nine human cell types," Nature, Nature, vol. 473(7345), pages 43-49, May.
    3. Erik Engelen & Johannes H. Brandsma & Maaike J. Moen & Luca Signorile & Dick H. W. Dekkers & Jeroen Demmers & Christel E. M. Kockx & Zehila Ozgür & Wilfred F. J. van IJcken & Debbie L. C. van den Berg, 2015. "Proteins that bind regulatory regions identified by histone modification chromatin immunoprecipitations and mass spectrometry," Nature Communications, Nature, vol. 6(1), pages 1-12, November.
    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. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    2. Seungsoo Hahn & Dongsup Kim, 2015. "Identifying and Reducing Systematic Errors in Chromosome Conformation Capture Data," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-17, December.
    3. Chirag Nepal & Jesper B. Andersen, 2023. "Alternative promoters in CpG depleted regions are prevalently associated with epigenetic misregulation of liver cancer transcriptomes," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    4. Haoxi Chai & Harianto Tjong & Peng Li & Wei Liao & Ping Wang & Chee Hong Wong & Chew Yee Ngan & Warren J. Leonard & Chia-Lin Wei & Yijun Ruan, 2023. "ChIATAC is an efficient strategy for multi-omics mapping of 3D epigenomes from low-cell inputs," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    5. Zhangyuan Pan & Yuelin Yao & Hongwei Yin & Zexi Cai & Ying Wang & Lijing Bai & Colin Kern & Michelle Halstead & Ganrea Chanthavixay & Nares Trakooljul & Klaus Wimmers & Goutam Sahana & Guosheng Su & M, 2021. "Pig genome functional annotation enhances the biological interpretation of complex traits and human disease," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Carlos Rivera & Hun-Goo Lee & Anna Lappala & Danni Wang & Verónica Noches & Montserrat Olivares-Costa & Marcela Sjöberg-Herrera & Jeannie T. Lee & María Estela Andrés, 2022. "Unveiling RCOR1 as a rheostat at transcriptionally permissive chromatin," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Julia Truch & Damien J. Downes & Caroline Scott & E. Ravza Gür & Jelena M. Telenius & Emmanouela Repapi & Ron Schwessinger & Matthew Gosden & Jill M. Brown & Stephen Taylor & Pak Leng Cheong & Jim R. , 2022. "The chromatin remodeller ATRX facilitates diverse nuclear processes, in a stochastic manner, in both heterochromatin and euchromatin," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Fang, Kuangnan & Chen, Yuanxing & Ma, Shuangge & Zhang, Qingzhao, 2022. "Biclustering analysis of functionals via penalized fusion," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    9. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.
    10. Boudreault, Jeremie & Bergeron, Normand E & St-Hilaire, Andre & Chebana, Fateh, 2022. "A new look at habitat suitability curves through functional data analysis," Ecological Modelling, Elsevier, vol. 467(C).
    11. Ja‐Yoon Jang & Hee‐Seok Oh & Yaeji Lim & Ying Kuen Cheung, 2021. "Ensemble clustering for step data via binning," Biometrics, The International Biometric Society, vol. 77(1), pages 293-304, March.
    12. Rachel K. Lex & Weiqiang Zhou & Zhicheng Ji & Kristin N. Falkenstein & Kaleigh E. Schuler & Kathryn E. Windsor & Joseph D. Kim & Hongkai Ji & Steven A. Vokes, 2022. "GLI transcriptional repression is inert prior to Hedgehog pathway activation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    13. Alessandro Casa & Charles Bouveyron & Elena Erosheva & Giovanna Menardi, 2021. "Co-clustering of Time-Dependent Data via the Shape Invariant Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 626-649, October.
    14. Shan Zhong & David B. Hitchcock, 2024. "Functional clustering of fictional narratives using Vonnegut curves," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(4), pages 1045-1066, December.
    15. Snježana Majstorović & Kristian Sabo & Johannes Jung & Matija Klarić, 2018. "Spectral methods for growth curve clustering," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 715-737, September.
    16. C. Denis & E. Lebarbier & C. Lévy‐Leduc & O. Martin & L. Sansonnet, 2020. "A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 623-640, June.
    17. Ramón Giraldo & William Caballero & Jesús Camacho-Tamayo, 2018. "Mantel test for spatial functional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 21-39, January.
    18. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    19. Cameron Cordero & Kavi P. M. Mehta & Tyler M. Weaver & Justin A. Ling & Bret D. Freudenthal & David Cortez & Steven A. Roberts, 2024. "Contributing factors to the oxidation-induced mutational landscape in human cells," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    20. Jennifer P. Nguyen & Timothy D. Arthur & Kyohei Fujita & Bianca M. Salgado & Margaret K. R. Donovan & Hiroko Matsui & Ji Hyun Kim & Agnieszka D’Antonio-Chronowska & Matteo D’Antonio & Kelly A. Frazer, 2023. "eQTL mapping in fetal-like pancreatic progenitor cells reveals early developmental insights into diabetes risk," Nature Communications, Nature, vol. 14(1), pages 1-22, December.

    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:pone00:0233630. 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.