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Genome Biol. 2015 Sep 2;16:183. doi: 10.1186/s13059-015-0745-7.

Analysis methods for studying the 3D architecture of the genome.

Ay F1,2, Noble WS3,4.

Author information

1
Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. ferhatay@uw.edu.
2
Feinberg School of Medicine, Northwestern University, Chicago, 60661, IL, USA. ferhatay@uw.edu.
3
Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. william-noble@uw.edu.
4
Department of Computer Science and Engineering, University of Washington, Seattle, 98195, WA, USA. william-noble@uw.edu.

Abstract

The rapidly increasing quantity of genome-wide chromosome conformation capture data presents great opportunities and challenges in the computational modeling and interpretation of the three-dimensional genome. In particular, with recent trends towards higher-resolution high-throughput chromosome conformation capture (Hi-C) data, the diversity and complexity of biological hypotheses that can be tested necessitates rigorous computational and statistical methods as well as scalable pipelines to interpret these datasets. Here we review computational tools to interpret Hi-C data, including pipelines for mapping, filtering, and normalization, and methods for confidence estimation, domain calling, visualization, and three-dimensional modeling.

PMID:
26328929
PMCID:
PMC4556012
DOI:
10.1186/s13059-015-0745-7
[Indexed for MEDLINE]
Free PMC Article
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