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.
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Acknowledgements
The authors are grateful to K. Korolev for many productive discussions. The work of M.I., G.F., A.G. and L.M. were supported by the US National Cancer Institute Physical Sciences–Oncology Center at MIT (U54CA143874). This work was supported by US National Institutes of Health grants HG003143 (to J.D.) and F32GM100617 (to R.P.M.) and by a W.M. Keck Foundation Distinguished Young Scholar in Medical Research Award (to J.D.).
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M.I. developed the iterative correction procedure. M.I. and G.F. developed data analysis tools. M.I. and A.G. developed and maintain publicly available software. M.I., G.F., R.P.M. and A.G. performed data analysis. M.I., G.F., R.P.M., N.N., A.G., B.R.L., J.D. and L.A.M. contributed to conceiving the study and wrote the paper.
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Supplementary Figures 1–16, Supplementary Table 1 and Supplementary Note (PDF 9595 kb)
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Imakaev, M., Fudenberg, G., McCord, R. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods 9, 999–1003 (2012). https://doi.org/10.1038/nmeth.2148
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DOI: https://doi.org/10.1038/nmeth.2148
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