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Multiplex chromatin interaction analysis by signal processing and statistical algorithms

View ORCID ProfileMinji Kim, Meizhen Zheng, Simon Zhongyuan Tian, Daniel Capurso, Byoungkoo Lee, Jeffrey H. Chuang, Yijun Ruan
doi: https://doi.org/10.1101/665232
Minji Kim
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Meizhen Zheng
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Simon Zhongyuan Tian
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Daniel Capurso
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Byoungkoo Lee
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Jeffrey H. Chuang
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Yijun Ruan
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
2Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
3Huazhong Agricultural University, Wuhan, China
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  • For correspondence: yijun.ruan@jax.org
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Abstract

The single-molecule multiplex chromatin interaction data generated by emerging non-ligation-based 3D genome mapping technologies provide novel insights into high dimensional chromatin organization, yet introduce new computational challenges. We developed MIA-Sig (https://github.com/TheJacksonLaboratory/mia-sig.git), an algorithmic framework to de-noise the data, assess the statistical significance of chromatin complexes, and identify topological domains and inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains.

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Posted June 10, 2019.
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Multiplex chromatin interaction analysis by signal processing and statistical algorithms
Minji Kim, Meizhen Zheng, Simon Zhongyuan Tian, Daniel Capurso, Byoungkoo Lee, Jeffrey H. Chuang, Yijun Ruan
bioRxiv 665232; doi: https://doi.org/10.1101/665232
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Multiplex chromatin interaction analysis by signal processing and statistical algorithms
Minji Kim, Meizhen Zheng, Simon Zhongyuan Tian, Daniel Capurso, Byoungkoo Lee, Jeffrey H. Chuang, Yijun Ruan
bioRxiv 665232; doi: https://doi.org/10.1101/665232

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