NuChart-II: A Graph-Based Approach for Analysis and Interpretation of Hi-C Data

  • Fabio Tordini
  • Maurizio Drocco
  • Ivan Merelli
  • Luciano Milanesi
  • Pietro Liò
  • Marco Aldinucci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8623)

Abstract

Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expressions and DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing techniques (Hi-C) and molecular dynamics studies show that there is a large correlation between co-localization and co-regulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we present NuChart-II, a software that allows the user to annotate and visualize a list of input genes with information relying on Hi-C data, integrating knowledge data about genomic features that are involved in the chromosome spatial organization. This software works directly with sequenced reads to identify related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. NuChart-II is a highly optimized implementation of a previous prototype developed in R, in which the graph-based representation of Hi-C data was tested. The prototype showed inevitable problems of scalability while working genome-wide on large datasets: particular attention has been paid in order to obtain an efficient parallel implementation of the software. The normalization of Hi-C data has been modified and improved, in order to provide a reliable estimation of proximity likelihood for the genes.

Keywords

Systems Biology Parallel Computing Hi-C data Neighbourhood Graph Chromosome Conformation Capture 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ling, J.Q., Hoffman, A.R.: Epigenetics of Long-Range Chromatin Interactions. Pediatric Research 61, 11R–16R (2007)Google Scholar
  2. 2.
    Dekker, J., Rippe, K., Dekker, M., Kleckner, N.: Capturing chromosome conformation. Science 295, 1306–1311 (2002)CrossRefGoogle Scholar
  3. 3.
    Simonis, M., Klous, P., Splinter, E., Moshkin, Y., Willemsen, R., et al.: Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nature Genetics 38, 1348–1354 (2006)CrossRefGoogle Scholar
  4. 4.
    Dostie, J., Richmond, T.A., Arnaout, R.A., Selzer, R.R., Lee, W.L., Honan, T.A., Rubio, E.D., Krumm, A., Lamb, J., Nusbaum, C., et al.: Chromosome conformation capture carbon copy (5C): A massively parallel solution for mapping interactions between genomic elements. Genome Res. 16, 1299–1309 (2006)CrossRefGoogle Scholar
  5. 5.
    Duan, Z., Andronescu, M., Schultz, K., Lee, C., Shendure, J., et al.: A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes. Methods 58(3), 277–288 (2012)CrossRefGoogle Scholar
  6. 6.
    Merelli, I., Liò, P., Milanesi, L.: NuChart: an R package to study gene spatial neighbourhoods with multi-omics annotations. PLoS One 8(9), e75146 (2013)Google Scholar
  7. 7.
    Yaffe, E., Tanay, A.: Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nature Genetics 43, 1059–1065 (2011)CrossRefGoogle Scholar
  8. 8.
    Hu, M., Deng, K., Selvaraj, S., Qin, Z., Ren, B., Liu, J.S.: HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28(23), 3131–3133 (2012)CrossRefGoogle Scholar
  9. 9.
    Lieberman-Aiden, E., van Berkum, N.L., Williams, L., Imakaev, M., Ragoczy, T., et al.: Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009)CrossRefGoogle Scholar
  10. 10.
    Aldinucci, M., Danelutto, M., Kilpatrick, P., Torquati, M.: Fastflow: high-level and efficient streaming on multi-core. In: Pllana, S., Xhafa, F. (eds.) Programming Multi-core and Many-core Computing Systems. Parallel and Distributed Computing, ch. 13. Wiley (2014)Google Scholar
  11. 11.
    Danelutto, M., Torquati, M.: Loop parallelism: a new skeleton perspective on data parallel patterns. In: Aldinucci, M., D’Agostino, D., Kilpatrick, P. (eds.) Proc. of Intl. Euromicro PDP 2014: Parallel Distributed and network-based Processing, IEEE, Torino (2014), http://calvados.di.unipi.it/storage/paper_files/2014_ff_looppar_pdp.pdf Google Scholar
  12. 12.
    “Intel Threading Building Blocks”, project site (2013), http://threadingbuildingblocks.org
  13. 13.
    Dagum, L., Menon, R.: OpenMP: An industry-standard api for shared-memory programming. IEEE Comput. Sci. Eng. 5(1), 46–55 (1998)CrossRefGoogle Scholar
  14. 14.
    Hong, S., Oguntebi, T., Olukotun, K.: Efficient parallel graph exploration on multi-core cpu and gpu. In: Proceedings of the 2011 International Conference on Parallel Architectures and Compilation Techniques, PACT 2011, pp. 78–88. IEEE Computer Society, Washington, DC (2011), http://dx.doi.org/10.1109/PACT.2011.14
  15. 15.
    Nelder, J.A., Wedderburn, R.W.M.: Generalized linear models. Journal of the Royal Statistical Society, Series A, General 135, 370–384 (1972)CrossRefGoogle Scholar
  16. 16.
    Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Booth, M., Rossi, F.: Gnu Scientific Library: Reference Manual. Network Theory Ltd., February 2003, http://www.worldcat.org/isbn/0954161734

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fabio Tordini
    • 1
  • Maurizio Drocco
    • 1
  • Ivan Merelli
    • 3
  • Luciano Milanesi
    • 3
  • Pietro Liò
    • 2
  • Marco Aldinucci
    • 1
  1. 1.Computer Science DepartmentUniversity of TurinTorinoItaly
  2. 2.Computer LaboratoryUniversity of CambridgeCambridgeUK
  3. 3.Institute for Biomedical TechnologiesItalian National Research CouncilSegrateItaly

Personalised recommendations