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Computational Analysis of Hi-C Data
2020The chromatin organization in the 3D nuclear space is essential for genome functionality. This spatial organization encompasses different topologies at diverse scale lengths with chromosomes occupying distinct volumes and individual chromosomes folding into compartments, inside which the chromatin fiber is packed in large domains (as the topologically ...
Forcato M., Bicciato S.
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2021
Processing, storing, and visualizing high-resolution Hi-C data required development of efficient data formats. A sparse matrix format saving only nonzero values has become the norm. A "zoomable" matrix style also became popular, storing multiple resolutions in a single file for interactive visualization.
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Processing, storing, and visualizing high-resolution Hi-C data required development of efficient data formats. A sparse matrix format saving only nonzero values has become the norm. A "zoomable" matrix style also became popular, storing multiple resolutions in a single file for interactive visualization.
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Methods to Assess the Reproducibility and Similarity of Hi-C Data
2021Hi-C experiments are costly to perform and involve multiple complex experimental steps. Reproducibility of Hi-C data is essential for ensuring the validity of the scientific conclusions drawn from the data. In this chapter, we describe several recently developed computational methods for assessing reproducibility of Hi-C replicate experiments.
Tao, Yang, Xi, He, Lin, An, Qunhua, Li
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Processing and Analysis of Hi-C Data on Bacteria
2018The study of three-dimensional genome organization has recently gained much attention in the context of novel techniques for detecting genome-wide contacts using next-generation sequencing. These genome-wide chromosome conformation capture-based methods, such as Hi-C, give a deep topological insight into the architecture of the genome inside the cell ...
Andreas, Hofmann, Dieter W, Heermann
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Methods for the Differential Analysis of Hi-C Data
2021The 3D organization of chromatin within the nucleus enables dynamic regulation and cell type-specific transcription of the genome. This is true at multiple levels of resolution: on a large scale, with chromosomes occupying distinct volumes (chromosome territories); at the level of individual chromatin fibers, which are organized into compartmentalized ...
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3D Genome Reconstruction with ShRec3D+ and Hi-C Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018Hi-C technology, a chromosome conformation capture (3C) based method, has been developed to capture genome-wide interactions at a given resolution. The next challenge is to reconstruct 3D structure of genome from the 3C-derived data computationally.
Jiangeng Li, Wei Zhang, Xiaodan Li
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Analysis of Hi-C Data for Discovery of Structural Variations in Cancer
2021Structural variations (SVs) are large genomic rearrangements that can be challenging to identify with current short read sequencing technology due to various confounding factors such as existence of genomic repeats and complex SV structures. Hi-C breakfinder is the first computational tool that utilizes the technology of high-throughput chromatin ...
Fan, Song +3 more
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Leveraging Hi-C Data to Detect Chromosomal Reorganizations
The three-dimensional (3D) organization of genomes refers to the spatial organization and folding of chromatin within the nucleus of cells. Over the years, the initial development and subsequent applications of chromosome capture techniques (3C) and their derivatives have permitted the study of the 3D genome organization at the deepest level of ...Lucía, Álvarez-González +1 more
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Deciphering 3D Organization of Chromosomes Using Hi-C Data
2018In order to interpret data from Hi-C studies genome-wide contact probability maps need to be translated into models of functional 3D genome organization. Here, we first present an overview of computational methods to analyze contact probability maps in terms of features such as the level and shape of compartmentalization.
Hofmann, A, Heermann, DW
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