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Iterative correction of Hi-C data reveals hallmarks of chromosome organization.

ABSTRACT: Extracting biologically meaningful information from chromosomal interactions obtained with genome-wide chromosome conformation capture (3C) analyses requires the elimination of systematic biases. We present a computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. We validate this ICE (iterative correction and eigenvector decomposition) technique on published data obtained by the high-throughput 3C method Hi-C, and we demonstrate that eigenvector decomposition of the obtained maps provides insights into local chromatin states, global patterns of chromosomal interactions, and the conserved organization of human and mouse chromosomes.

SUBMITTER: Imakaev M

PROVIDER: S-EPMC3816492 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Iterative correction of Hi-C data reveals hallmarks of chromosome organization.

Imakaev Maxim M Fudenberg Geoffrey G McCord Rachel Patton RP Naumova Natalia N Goloborodko Anton A Lajoie Bryan R BR Dekker Job J Mirny Leonid A LA

Nature methods 20120902 10

Extracting biologically meaningful information from chromosomal interactions obtained with genome-wide chromosome conformation capture (3C) analyses requires the elimination of systematic biases. We present a computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. We validate this ICE (iterative correction and eigenvector decomposition) technique on publish ...[more]

PMID: 22941365

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