Literature DB >> 26433013

How computer science can help in understanding the 3D genome architecture.

Yoli Shavit, Ivan Merelli, Luciano Milanesi, Pietro Lio'.   

Abstract

Chromosome conformation capture techniques are producing a huge amount of data about the architecture of our genome. These data can provide us with a better understanding of the events that induce critical regulations of the cellular function from small changes in the three-dimensional genome architecture. Generating a unified view of spatial, temporal, genetic and epigenetic properties poses various challenges of data analysis, visualization, integration and mining, as well as of high performance computing and big data management. Here, we describe the critical issues of this new branch of bioinformatics, oriented at the comprehension of the three-dimensional genome architecture, which we call 'Nucleome Bioinformatics', looking beyond the currently available tools and methods, and highlight yet unaddressed challenges and the potential approaches that could be applied for tackling them. Our review provides a map for researchers interested in using computer science for studying 'Nucleome Bioinformatics', to achieve a better understanding of the biological processes that occur inside the nucleus.
© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Keywords:  Nucleome Bioinformatics; big data analysis; chromosome conformation capture; genome architecture; high performance computing; multi-omics data integration

Mesh:

Year:  2015        PMID: 26433013     DOI: 10.1093/bib/bbv085

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  3 in total

Review 1.  4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?

Authors:  Monika Sekelja; Jonas Paulsen; Philippe Collas
Journal:  Genome Biol       Date:  2016-04-07       Impact factor: 13.583

2.  Comparison of computational methods for Hi-C data analysis.

Authors:  Francesco Ferrari; Silvio Bicciato; Mattia Forcato; Chiara Nicoletti; Koustav Pal; Carmen Maria Livi
Journal:  Nat Methods       Date:  2017-06-12       Impact factor: 28.547

3.  Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors.

Authors:  François Serra; Davide Baù; Mike Goodstadt; David Castillo; Guillaume J Filion; Marc A Marti-Renom
Journal:  PLoS Comput Biol       Date:  2017-07-19       Impact factor: 4.475

  3 in total

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