Hi-C is a genome-wide sequencing technique used to investigate 3D chromatin conformation inside the nucleus. Computational methods are required to analyze Hi-C data and identify chromatin interactions and topologically associating domains (TADs) from genome-wide contact probability maps. We quantitatively compared the performance of 13 algorithms in their analyses of Hi-C data from six landmark studies and simulations. This comparison revealed differences in the performance of methods for chromatin interaction identification, but more comparable results for TAD detection between algorithms.
Comparison of computational methods for Hi-C data analysis / Forcato, Mattia; Nicoletti, Chiara; Pal, Koustav; Livi, Carmen Maria; Ferrari, Francesco; Bicciato, Silvio. - In: NATURE METHODS. - ISSN 1548-7091. - 14:7(2017), pp. 679-685. [10.1038/nmeth.4325]
Comparison of computational methods for Hi-C data analysis
FORCATO, Mattia;NICOLETTI, CHIARA;BICCIATO, Silvio
2017
Abstract
Hi-C is a genome-wide sequencing technique used to investigate 3D chromatin conformation inside the nucleus. Computational methods are required to analyze Hi-C data and identify chromatin interactions and topologically associating domains (TADs) from genome-wide contact probability maps. We quantitatively compared the performance of 13 algorithms in their analyses of Hi-C data from six landmark studies and simulations. This comparison revealed differences in the performance of methods for chromatin interaction identification, but more comparable results for TAD detection between algorithms.File | Dimensione | Formato | |
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