Resolving Spatial Inconsistencies in Chromosome Conformation Data

  • Geet Duggal
  • Rob Patro
  • Emre Sefer
  • Hao Wang
  • Darya Filippova
  • Samir Khuller
  • Carl Kingsford
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7534)

Abstract

We introduce a new method for filtering noisy 3C interactions that selects subsets of interactions that obey metric constraints of various strictness. We demonstrate that, although the problem is computationally hard, near-optimal results are often attainable in practice using well-designed heuristics and approximation algorithms. Further, we show that, compared with a standard technique, this metric filtering approach leads to (a) subgraphs with higher total statistical significance, (b) lower embedding error, (c) lower sensitivity to initial conditions of the embedding algorithm, and (d)  structures with better agreement with light microscopy measurements.

Keywords

metric subgraph chromosome conformation 

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References

  1. 1.
    Baù, D., et al.: The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules. Nat. Struct. & Mol. Biol. 18(1), 107–114 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Dekker, J., et al.: Capturing chromosome conformation. Science 295(5558), 1306–1311 (2002)CrossRefGoogle Scholar
  3. 3.
    Duan, Z., et al.: A three-dimensional model of the yeast genome. Nature 465(7296), 363–367 (2010)CrossRefGoogle Scholar
  4. 4.
    Fudenberg, G., et al.: High-order chromatin architecture determines the landscape of chromosomal alterations in cancer. Nat. Biotechnol. 29(12), 1109–1113 (2011), http://www.ncbi.nlm.nih.gov/pubmed/22101486, doi:10.1038/nbt.2049CrossRefGoogle Scholar
  5. 5.
    Gomes, C., Williams, R.: Approximation algorithms. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, ch. 18. Springer (2005)Google Scholar
  6. 6.
    Hochbaum, D.S. (ed.): Approximation algorithms for NP-hard problems. PWS Publishing Co, Boston (1997)Google Scholar
  7. 7.
    Kalhor, R., et al.: Genome architectures revealed by tethered chromosome conformation capture and population-based modeling. Nat. Biotechnol. 30(1), 90–98 (2012)CrossRefGoogle Scholar
  8. 8.
    Lieberman-Aiden, E., et al.: Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326(5950), 289–293 (2009)CrossRefGoogle Scholar
  9. 9.
    Marti-Renom, M.A., Mirny, L.A.: Bridging the resolution gap in structural modeling of 3D genome organization. PLoS Comput. Biol. 7(7), 1002125 (2011)CrossRefGoogle Scholar
  10. 10.
    Niedermeier, R., Rossmanith, P.: An efficient fixed-parameter algorithm for 3-hitting set. Journal of Discrete Algorithms 1(1), 89–102 (2003)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Rousseau, M., et al.: Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling. BMC Bioinformatics 12(1), 414 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Saxe, J.: Embeddability of weighted graphs in k-space is strongly NP-hard. In: 17th Allerton Conference in Communications, Control and Computing, pp. 480–489 (1979)Google Scholar
  13. 13.
    Sexton, T., et al.: Three-dimensional folding and functional organization principles of the Drosophila genome. Cell 148(3), 458–472 (2012)CrossRefGoogle Scholar
  14. 14.
    Tanizawa, H., et al.: Mapping of long-range associations throughout the fission yeast genome reveals global genome organization linked to transcriptional regulation. Nuc. Acids Res. 38(22), 8164–8177 (2010)CrossRefGoogle Scholar
  15. 15.
    Theobald, D.L., Wuttke, D.S.: THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures. Bioinformatics 22(17), 2171–2172 (2006)CrossRefGoogle Scholar
  16. 16.
    Therizols, P., et al.: Chromosome arm length and nuclear constraints determine the dynamic relationship of yeast subtelomeres. Proc. Natl. Acad. Sci. USA 107(5), 2025–2030 (2010)CrossRefGoogle Scholar
  17. 17.
    Umbarger, M.A., et al.: The three-dimensional architecture of a bacterial genome and its alteration by genetic perturbation. Mol. Cell 44(2), 252–264 (2011)CrossRefGoogle Scholar
  18. 18.
    Yaffe, E., Tanay, A.: Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nature Genetics 43(11), 1059–1065 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geet Duggal
    • 1
  • Rob Patro
    • 1
  • Emre Sefer
    • 1
  • Hao Wang
    • 2
  • Darya Filippova
    • 1
  • Samir Khuller
    • 1
  • Carl Kingsford
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of MarylandCollege ParkUSA

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