NuChart-II: The road to a fast and scalable tool for Hi-C data analysis

Abstract

Recent advances in molecular biology and bioinformatic techniques have brought about an explosion of information about the spatial organisation of the DNA in the nucleus of a cell. High-throughput molecular biology techniques provide a genome-wide capture of the spatial organisation of chromosomes at unprecedented scales, which permit one to identify physical interactions between genetic elements located throughout a genome. This important information is, however, hampered by the lack of biologist-friendly analysis and visualisation software: these disciplines are literally caught in a flood of data and are now facing many of the scale-out issues that high-performance computing has been addressing for years. Data must be managed, analysed and integrated, with substantial requirements of speed (in terms of execution time), application scalability and data representation. In this work, we present NuChart-II, an efficient and highly optimised tool for genomic data analysis that provides a gene-centric, graph-based representation of genomic information and which proposes an ex-post normalisation technique for Hi-C data. While designing NuChart-II, we addressed several common issues in the parallelisation of memory-bound algorithms for shared-memory systems.

Keywords

High-performance computing bioinformatics Hi-C data analysis parallel computing memory-bound algorithms

Get full access to this article

View all access options for this article.

References

Aldinucci

Meneghin

Torquati

(2010) Efficient Smith–Waterman on multi-core with fastflow. In: Proceedings of 18th Euromicro conference on parallel distributed and network-based processing (PDP 2010) (eds M

Danelutto

Gross

Bourgeois

) Pisa, Italy, 17–19 February 2010, pp.195–199. Piscataway, NJ: IEEE.

Aldinucci

Campa

Danelutto

. (2013) Targeting distributed systems in fastflow. In: Euro-Par 2012: proceedings of the CoreGrid workshop on grids, clouds and P2P computing, Rhodes Islands, Greece, August 27–31 2013. pp.47–56. Berlin, Heidelberg: Springer.

Aldinucci

Campa

Danelutto

. (2014) Design patterns percolating to parallel programming framework implementation. International Journal of Parallel Programming 42(6): 1012–1031.

Asanovic

Bodik

Demmel

. (2009) A view of the parallel computing landscape. Communications of the ACM 52(10): 56–67.

Bailey

Noble

(2014) Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Research 24(6): 999–1011.

Dagum

Menon

(1998) OpenMP: an industry-standard API for shared-memory programming. IEEE Computational Science and Engineering 5(1): 46–55.

Danelutto

Torquati

(2014) Loop parallelism: a new skeleton perspective on data parallel patterns. In: Proceedings of 22nd Euromicro conference on parallel distributed and network-based processing (PDP 2014) (eds M

Aldinucci

D’Agostino

Kilpatrick

), Torino, Italy, 12–14 February 2014, pp.52–59. Piscataway, NJ: IEEE.

Dekker

Rippe

Dekker

. (2001) Capturing chromosome conformation. Science 295(5558): 1306–1311.

De Wit

De Laat

(2012) A decade of 3C technologies: insights into nuclear organization. Genes & Development 26(1): 11–24.

10.

Dixon

Selvaraj

Yue

. (2012) Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions. Nature 485(7398): 376–380. doi:10.1038/nature11082.

11.

Hong

Oguntebi

Olukotun

(2011) Efficient parallel graph exploration on multi-core CPU and GPU. In: Proceedings of the 22nd international conference on parallel architectures and compilation techniques, Galveston, TX, 10–14 October 2011, pp.78–88. Washington, DC: IEEE Computer Society.

12.

Deng

Selvaraj

.(2012) HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28(23): 3131–3133. doi:10.1093/bioinformatics/bts570

13.

Intel Threading Building Blocks, project site (2013). Available at: http://threadingbuildingblocks.org (accessed 8 September 2016).

14.

Lieberman-Aiden

Van Berkum

Williams

. (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326(595): 289–293.

15.

Merelli

Liò

Milanesi

(2013) NuChart: An R package to study gene spatial neighbourhoods with multi-omics annotations. PLoS ONE 8(9): e75146. doi:10.1371/journal.pone.0075146.

16.

Nelder

Wedderburn

RWM

(1972) Generalized linear models. Journal of the Royal Statistical Society, Series A, General 135: 370–384.

17.

Seitan

Faure

Zhan

. (2013) Cohesin-based chromatin interactions enable regulated gene expression within preexisting architectural compartments. Genome Research 23(12): 2066–2077.

18.

Servant

Lajoie

Nora

. (2012) HiTC: exploration of high-throughput ‘C’ experiments. Bioinformatics 28(21): 2843–2844.

19.

Shavit

Hamey

Liò

(2014) FisHiCal: An R package for iterative FISH-based calibration of Hi-C data. Bioinformatics 30(21): 3120–3122. doi: 10.1093/bioinformatics/btu491.

20.

Soroushnia

Daneshtalab

Plosila

. (2014) High-performance pattern matching on heterogeneous platform. Journal of Integrative Bioinformatics 11(3): JIB (ISSN 1613–4516).

21.

Winterbach

Mieghem

Reinders

MJT

. (2013) Topology of molecular interaction networks. BMC Systems Biology 7: 90.

22.

Yaffe

Tanay

(2011) Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nature Genetics 43: 1059–1065.