In the epigenetics field, large-scale functional genomics datasets of ever-increasing size and complexity have been produced using experimental techniques based on high-throughput sequencing. In particular, the study of the 3D organization of chromatin has raised increasing interest, thanks to the development of advanced experimental techniques. In this context, Hi-C has been widely adopted as a high-throughput method to measure pairwise contacts between virtually any pair of genomic loci, thus yielding unprecedented challenges for analyzing and handling the resulting complex datasets. In this review, we focus on the increasing complexity of available Hi-C datasets, which parallels the adoption of novel protocol variants. We also review the complexity of the multiple data analysis steps required to preprocess Hi-C sequencing reads and extract biologically meaningful information. Finally, we discuss solutions for handling and visualizing such large genomics datasets.

Hi-C analysis: from data generation to integration / Pal, Koustav; Forcato, Mattia; Ferrari, Francesco. - In: BIOPHYSICAL REVIEWS. - ISSN 1867-2450. - 11:1(2019), pp. 67-78. [10.1007/s12551-018-0489-1]

Hi-C analysis: from data generation to integration

Forcato, Mattia;
2019

Abstract

In the epigenetics field, large-scale functional genomics datasets of ever-increasing size and complexity have been produced using experimental techniques based on high-throughput sequencing. In particular, the study of the 3D organization of chromatin has raised increasing interest, thanks to the development of advanced experimental techniques. In this context, Hi-C has been widely adopted as a high-throughput method to measure pairwise contacts between virtually any pair of genomic loci, thus yielding unprecedented challenges for analyzing and handling the resulting complex datasets. In this review, we focus on the increasing complexity of available Hi-C datasets, which parallels the adoption of novel protocol variants. We also review the complexity of the multiple data analysis steps required to preprocess Hi-C sequencing reads and extract biologically meaningful information. Finally, we discuss solutions for handling and visualizing such large genomics datasets.
20-dic-2018
11
1
67
78
Hi-C analysis: from data generation to integration / Pal, Koustav; Forcato, Mattia; Ferrari, Francesco. - In: BIOPHYSICAL REVIEWS. - ISSN 1867-2450. - 11:1(2019), pp. 67-78. [10.1007/s12551-018-0489-1]
Pal, Koustav; Forcato, Mattia; Ferrari, Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1172719
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