Chromatyping: Reconstructing Nucleosome Profiles from NOMe Sequencing Data

  • Shounak Chakraborty
  • Stefan Canzar
  • Tobias MarschallEmail author
  • Marcel H. SchulzEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10812)


Measuring nucleosome positioning in cells is crucial for the analysis of epigenetic gene regulation. Reconstruction of nucleosome profiles of individual cells or subpopulations of cells remains challenging because most genome-wide assays measure nucleosome positioning and DNA accessibility for thousands of cells using bulk sequencing. Here we use characteristics of the NOMe-sequencing assay to derive a new approach, called ChromaClique, for deconvolution of different nucleosome profiles (chromatypes) from cell subpopulations of one NOMe-seq measurement. ChromaClique uses a maximal clique enumeration algorithm on a newly defined NOMe read graph that is able to group reads according to their nucleosome profiles. We show that the edge probabilities of that graph can be efficiently computed using Hidden Markov Models. We demonstrate using simulated data that ChromaClique is more accurate than a related method and scales favorably, allowing genome-wide analyses of chromatypes in cell subpopulations. Software is available at under MIT license.


NOMe-seq Max clique enumeration Epigenetics HMMs 



We thank Karl Nordström, Gilles Gasparoni and Jörn Walter for providing access to the HepG2 NOMe-seq data.


  1. 1.
    Thurman, R.E., et al.: The accessible chromatin landscape of the human genome. Nature 489(7414), 75–82 (2012)CrossRefGoogle Scholar
  2. 2.
    Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y., Greenleaf, W.J.: Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10(12), 1213–1218 (2013)CrossRefGoogle Scholar
  3. 3.
    Kelly, T.K., Liu, Y., Lay, F.D., Liang, G., Berman, B.P., Jones, P.A.: Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res. 22(12), 2497–2506 (2012)CrossRefGoogle Scholar
  4. 4.
    Taberlay, P.C., Statham, A.L., Kelly, T.K., Clark, S.J., Jones, P.A.: Reconfiguration of nucleosome-depleted regions at distal regulatory elements accompanies DNA methylation of enhancers and insulators in cancer. Genome Res. 24(9), 1421–1432 (2014)CrossRefGoogle Scholar
  5. 5.
    Durek, P., et al.: Epigenomic profiling of human CD4\(^{+}\) T cells supports a linear differentiation model and highlights molecular regulators of memory development. Immunity 45(5), 1148–1161 (2016)CrossRefGoogle Scholar
  6. 6.
    Guo, H., et al.: DNA methylation and chromatin accessibility profiling of mouse and human fetal germ cells. Cell Res. 27(2), 165–183 (2017)CrossRefGoogle Scholar
  7. 7.
    Schmidt, F., et al.: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction. Nucleic Acids Res. 45(1), 54–66 (2017)CrossRefGoogle Scholar
  8. 8.
    Pott, S.: Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, e23203 (2017)CrossRefGoogle Scholar
  9. 9.
    Vincent, M., et al.: epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data. Genome Biol. 18(1), 38 (2017)CrossRefGoogle Scholar
  10. 10.
    Domingo, E., Sheldon, J., Perales, C.: Viral quasispecies evolution. Microbiol. Mol. Biol. Rev. 76(2), 159–216 (2012)CrossRefGoogle Scholar
  11. 11.
    Beerenwinkel, N., Günthard, H.F., Roth, V., Metzner, K.J.: Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data. Front. Microbiol. 3, 329 (2012)CrossRefGoogle Scholar
  12. 12.
    Posada-Cespedes, S., Seifert, D., Beerenwinkel, N.: Recent advances in inferring viral diversity from high-throughput sequencing data. Virus Res. 239, 17–32 (2017)CrossRefGoogle Scholar
  13. 13.
    Töpfer, A., Marschall, T., Bull, R.A., Luciani, F., Schönhuth, A., Beerenwinkel, N.: Viral quasispecies assembly via maximal clique enumeration. PLoS Comput. Biol. 10(3), e1003515 (2014)CrossRefGoogle Scholar
  14. 14.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  15. 15.
    Huang, W., Li, L., Myers, J.R., Marth, G.T.: ART: a next-generation sequencing read simulator. Bioinformatics 28(4), 593–594 (2012)CrossRefGoogle Scholar
  16. 16.
    Krueger, F., Andrews, S.R.: Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27(11), 1571–1572 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Cluster of Excellence for Multimodal Computing and InteractionSaarland University, Saarland Informatics Campus E1.7SaarbrückenGermany
  2. 2.Max Planck Institute for InformaticsSaarland Informatics Campus E1.4SaarbrückenGermany
  3. 3.Center for BioinformaticsSaarland University, Saarland Informatics Campus E2.1SaarbrückenGermany
  4. 4.Gene CenterLudwig-Maximilians-Universität MünchenMunichGermany

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