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Unsupervised pattern discovery in human chromatin structure through genomic segmentation


We trained Segway, a dynamic Bayesian network method, simultaneously on chromatin data from multiple experiments, including positions of histone modifications, transcription-factor binding and open chromatin, all derived from a human chronic myeloid leukemia cell line. In an unsupervised fashion, we identified patterns associated with transcription start sites, gene ends, enhancers, transcriptional regulator CTCF-binding regions and repressed regions. Software and genome browser tracks are at

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Figure 1: Heat map of discovered Gaussian parameters in an unsupervised 25-label segmentation trained on 31 tracks of histone modification, transcription-factor binding and open chromatin signal data in 1% of the human genome.
Figure 2: Gene structure in Segway labels.

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  1. ENCODE Project Consortium. PLoS Biol. 9, e1001046 (2011).

  2. Day, N., Hemmaplardh, A., Thurman, R.E., Stamatoyannopoulos, J.A. & Noble, W.S. Bioinformatics 23, 1424–1426 (2007).

    Article  CAS  Google Scholar 

  3. Erdman, C. & Emerson, J.W. Bioinformatics 24, 2143–2148 (2008).

    Article  CAS  Google Scholar 

  4. Jaschek, R. & Tanay, A. in Research in Computational Molecular Biology, Lecture Notes in Computer Science Vol. 5541 (ed. Batzoglou, S.) 170–183 (Springer, Berlin, 2009).

  5. Ernst, J. & Kellis, M. Nat. Biotechnol. 28, 817–825 (2010).

    Article  CAS  Google Scholar 

  6. Filion, G.J. et al. Cell 143, 212–224 (2010).

    Article  CAS  Google Scholar 

  7. Kharchenko, P.V. et al. Nature 471, 480–485 (2011).

    Article  CAS  Google Scholar 

  8. Bilmes, J. & Bartels, C. IEEE Signal Process. Mag. 22, 89–100 (2005).

    Article  Google Scholar 

  9. Reynolds, S.M., Käll, L., Riffle, M.E., Bilmes, J.A. & Noble, W.S. PLOS Comput. Biol. 4, e1000213 (2008).

    Article  Google Scholar 

  10. Wang, Z., Schones, D.E. & Zhao, K. Curr. Opin. Genet. Dev. 19, 127–134 (2009).

    Article  CAS  Google Scholar 

  11. Hon, G., Ren, B. & Wang, W. PLOS Comput. Biol. 4, e1000201 (2008).

    Article  Google Scholar 

  12. Raney, B.J. et al. Nucleic Acids Res. 39, D871–D875 (2011).

    Article  CAS  Google Scholar 

  13. Hoffman, M.M., Buske, O.J. & Noble, W.S. Bioinformatics 26, 1458–1459 (2010).

    Article  CAS  Google Scholar 

  14. Johnson, N.L. Biometrika 36, 149–176 (1949).

    Article  CAS  Google Scholar 

  15. Bilmes, J. in UAI '00: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (eds. Boutilier, C. & Goldszmidt, M.) 38–45 (Morgan Kaufmann, San Francisco, 2000).

  16. Grundy, W.N., Bailey, T.L., Elkan, C.P. & Baker, M.E. Comput. Appl. Biosci. 13, 397–406 (1997).

    CAS  PubMed  Google Scholar 

  17. Bilmes, J. & Bartels, C. in UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence (eds. Meek, C. & Kjærulff, U.) 47–56 (Morgan Kaufmann Publishers, San Francisco, 2003).

  18. Dempster, A.P., Laird, N.M. & Rubin, D.B. J. Royal Stat. Soc. B 39, 1–22 (1977).

    Google Scholar 

  19. Viterbi, A.J. IEEE Trans. Inf. Theory 13, 260–269 (1967).

    Article  Google Scholar 

  20. Fujita, P.A. et al. Nucleic Acids Res. 39, D876–D882 (2011).

    Article  CAS  Google Scholar 

  21. Harrow, J. et al. Genome Biol. 7, S4.1–S4.9 (2006).

    Article  Google Scholar 

  22. Takahashi, H., Kato, S., Murata, M. & Carninci, P. Methods Mol. Biol. 786, 181–200 (2012).

    Article  CAS  Google Scholar 

  23. Siepel, A. et al. Genome Res. 15, 1034–1050 (2005).

    Article  CAS  Google Scholar 

  24. Buske, O.J., Hoffman, M.M., Ponts, N., Roch, K.G.L. & Noble, W.S. BMC Bioinformatics 12, 415 (2011).

    Article  CAS  Google Scholar 

  25. Davis, J. & Goadrich, M. in Proceedings of the 23rd International Conference on Machine Learning 233–240 (ACM, New York, 2006).

  26. Flicek, P. et al. Nucleic Acids Res. 39, D800–D806 (2011).

    Article  CAS  Google Scholar 

  27. UniProt Consortium. Nucleic Acids Res. 39, D214–D219 (2011).

  28. Berriz, G.F., Beaver, J.E., Cenik, C., Tasan, M. & Roth, F.P. Bioinformatics 25, 3043–3044 (2009).

    Article  CAS  Google Scholar 

  29. Wingender, E. et al. Nucleic Acids Res. 28, 316–319 (2000).

    Article  CAS  Google Scholar 

  30. Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W. & Lenhard, B. Nucleic Acids Res. 32, D91–D94 (2004).

    Article  CAS  Google Scholar 

  31. Grant, C.E., Bailey, T.L. & Noble, W.S. Bioinformatics 27, 1017–1018 (2011).

    Article  CAS  Google Scholar 

  32. Bickel, P.J., Boley, N., Brown, J.B., Huang, H. & Zhang, N.R. Ann. Appl. Stat. 4, 1660–1697 (2010).

    Article  Google Scholar 

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We thank P.J. Collins for assistance with transient transfection assays, S. Djebali for processing data, C.E. Grant for motif analysis, A. Kundaje for helpful suggestions, and members of the ENCODE Project Consortium, the ENCODE Data Coordination Center and the US National Human Genome Research Institute for providing early public access to the unpublished data used in this work. This work used data produced in the laboratories of B.E. Bernstein (Broad Institute of the Massachusetts Institute of Technology and Harvard University), M.P. Snyder (Stanford University), R.M. Myers (HudsonAlpha Institute for Biotechnology), P.J. Farnham (University of Southern California), V.R. Iyer (University of Texas at Austin), G.E. Crawford (Duke University), J.D. Lieb and T.S. Furey (University of North Carolina at Chapel Hill), J.A. Stamatoyannopoulos (University of Washington), P. Carninci (RIKEN), T.R. Gingeras (Cold Spring Harbor Laboratory), and A. Sidow (Stanford University). This publication was made possible by grants 004695, 004561 and 006259 from National Human Genome Research Institute.

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M.M.H., W.S.N. and J.A.B. conceived of the project; M.M.H., W.S.N. and Z.W. designed computational and biological experiments. M.M.H., J.A.B., O.J.B. and J.W. developed software used in this work; M.M.H., O.J.B. and J.W. conducted computational experiments and analyzed data; and M.M.H., W.S.N., Z.W., J.A.B., O.J.B. and J.W. wrote the manuscript.

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Correspondence to William Stafford Noble.

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The authors declare no competing financial interests.

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Supplementary Figures 1–11, Supplementary Tables 1–4, Supplementary Results, Supplementary Discussion (PDF 1780 kb)

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Hoffman, M., Buske, O., Wang, J. et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9, 473–476 (2012).

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