Comparison of computational methods for Hi-C data analysis

Hi-C is a genome-wide sequencing technique to investigate the 3D chromatin conformation inside the nucleus. The most studied structures that can be identified from Hi-C - chromatin interactions and topologically associating domains (TADs) - require computational methods to analyze genome-wide contac...

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Autores principales: Forcato, Mattia, Nicoletti, Chiara, Pal, Koustav, Livi, Carmen Maria, Ferrari, Francesco, Bicciato, Silvio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493985/
https://www.ncbi.nlm.nih.gov/pubmed/28604721
http://dx.doi.org/10.1038/nmeth.4325
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author Forcato, Mattia
Nicoletti, Chiara
Pal, Koustav
Livi, Carmen Maria
Ferrari, Francesco
Bicciato, Silvio
author_facet Forcato, Mattia
Nicoletti, Chiara
Pal, Koustav
Livi, Carmen Maria
Ferrari, Francesco
Bicciato, Silvio
author_sort Forcato, Mattia
collection PubMed
description Hi-C is a genome-wide sequencing technique to investigate the 3D chromatin conformation inside the nucleus. The most studied structures that can be identified from Hi-C - chromatin interactions and topologically associating domains (TADs) - require computational methods to analyze genome-wide contact probability maps. We quantitatively compared the performances of 13 algorithms for the analysis of Hi-C data from 6 landmark studies and simulations. The comparison revealed clear differences in the performances of methods to identify chromatin interactions and more comparable results of algorithms for TAD detection.
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spelling pubmed-54939852017-12-12 Comparison of computational methods for Hi-C data analysis Forcato, Mattia Nicoletti, Chiara Pal, Koustav Livi, Carmen Maria Ferrari, Francesco Bicciato, Silvio Nat Methods Article Hi-C is a genome-wide sequencing technique to investigate the 3D chromatin conformation inside the nucleus. The most studied structures that can be identified from Hi-C - chromatin interactions and topologically associating domains (TADs) - require computational methods to analyze genome-wide contact probability maps. We quantitatively compared the performances of 13 algorithms for the analysis of Hi-C data from 6 landmark studies and simulations. The comparison revealed clear differences in the performances of methods to identify chromatin interactions and more comparable results of algorithms for TAD detection. 2017-06-12 2017-07 /pmc/articles/PMC5493985/ /pubmed/28604721 http://dx.doi.org/10.1038/nmeth.4325 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Forcato, Mattia
Nicoletti, Chiara
Pal, Koustav
Livi, Carmen Maria
Ferrari, Francesco
Bicciato, Silvio
Comparison of computational methods for Hi-C data analysis
title Comparison of computational methods for Hi-C data analysis
title_full Comparison of computational methods for Hi-C data analysis
title_fullStr Comparison of computational methods for Hi-C data analysis
title_full_unstemmed Comparison of computational methods for Hi-C data analysis
title_short Comparison of computational methods for Hi-C data analysis
title_sort comparison of computational methods for hi-c data analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5493985/
https://www.ncbi.nlm.nih.gov/pubmed/28604721
http://dx.doi.org/10.1038/nmeth.4325
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