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03498nam a2200613Ia 4500 |
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10.1186-s12859-021-04113-8 |
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220427s2021 CNT 000 0 und d |
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|a 14712105 (ISSN)
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|a Deciphering hierarchical organization of topologically associated domains through change-point testing
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|b BioMed Central Ltd
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1186/s12859-021-04113-8
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|a Background: The nucleus of eukaryotic cells spatially packages chromosomes into a hierarchical and distinct segregation that plays critical roles in maintaining transcription regulation. High-throughput methods of chromosome conformation capture, such as Hi-C, have revealed topologically associating domains (TADs) that are defined by biased chromatin interactions within them. Results: We introduce a novel method, HiCKey, to decipher hierarchical TAD structures in Hi-C data and compare them across samples. We first derive a generalized likelihood-ratio (GLR) test for detecting change-points in an interaction matrix that follows a negative binomial distribution or general mixture distribution. We then employ several optimal search strategies to decipher hierarchical TADs with p values calculated by the GLR test. Large-scale validations of simulation data show that HiCKey has good precision in recalling known TADs and is robust against random collisions of chromatin interactions. By applying HiCKey to Hi-C data of seven human cell lines, we identified multiple layers of TAD organization among them, but the vast majority had no more than four layers. In particular, we found that TAD boundaries are significantly enriched in active chromosomal regions compared to repressed regions. Conclusions: HiCKey is optimized for processing large matrices constructed from high-resolution Hi-C experiments. The method and theoretical result of the GLR test provide a general framework for significance testing of similar experimental chromatin interaction data that may not fully follow negative binomial distributions but rather more general mixture distributions. © 2021, The Author(s).
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|a article
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|a binomial distribution
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|a Cell culture
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|a cell nucleus
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|a Cell Nucleus
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|a Change-points
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|a chromatin
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|a Chromatin
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|a Chromatin interaction
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|a chromosome
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|a chromosome
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|a Chromosomes
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|a computer simulation
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|a Computer Simulation
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|a controlled study
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|a gene expression regulation
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|a Gene Expression Regulation
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|a Generalized Likelihood Ratio Test
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|a Generalized likelihood-ratio test
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|a genetics
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|a Hi-C data
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|a Hierarchical organizations
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|a Hierarchical TADs
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|a High-throughput method
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|a human
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|a human cell
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|a Humans
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|a Matrix algebra
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|a Mixture distributions
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|a Mixtures
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|a Negative binomial distribution
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|a Optimal search strategy
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|a Significance testing
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|a simulation
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|a Topology
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|a Transcription
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|a Transcription regulations
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|a Chen, Y.
|e author
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|a Wu, Y.
|e author
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|a Xing, H.
|e author
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|a Zhang, M.Q.
|e author
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|t BMC Bioinformatics
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