Results 31 to 40 of about 215,700 (227)
SRHiC: A Deep Learning Model to Enhance the Resolution of Hi-C Data
Frontiers in Genetics, 2020 Hi-C data is important for studying chromatin three-dimensional structure. However, the resolution of most existing Hi-C data is generally coarse due to sequencing cost.
Zhilan Li, Zhiming Dai, Zhiming Dai
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qc3C: Reference-free quality control for Hi-C sequencing data.
PLoS Computational Biology, 2021 Hi-C is a sample preparation method that enables high-throughput sequencing to capture genome-wide spatial interactions between DNA molecules. The technique has been successfully applied to solve challenging problems such as 3D structural analysis of ...
Matthew Z DeMaere, Aaron E Darling
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Revisiting Assessment of Computational Methods for Hi-C Data Analysis. [PDF]
Int J Mol Sci, 2023 The performances of algorithms for Hi-C data preprocessing, the identification of topologically associating domains, and the detection of chromatin interactions and promoter–enhancer interactions have been mostly evaluated using semi-quantitative or synthetic data approaches, without utilizing the most recent methods, since 2017.
Yang J, Zhu X, Wang R, Li M, Tang Q.
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Comparison of computational methods for Hi-C data analysis [PDF]
Nature Methods, 2017 Hi-C is a genome-wide sequencing technique used to investigate 3D chromatin conformation inside the nucleus. Computational methods are required to analyze Hi-C data and identify chromatin interactions and topologically associating domains (TADs) from genome-wide contact probability maps.
Mattia Forcato +5 more
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scHiCTools: A computational toolbox for analyzing single-cell Hi-C data.
PLoS Computational Biology, 2021 Single-cell Hi-C (scHi-C) sequencing technologies allow us to investigate three-dimensional chromatin organization at the single-cell level. However, we still need computational tools to deal with the sparsity of the contact maps from single cells and ...
Xinjun Li +4 more
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Unsupervised embedding of single-cell Hi-C data [PDF]
Bioinformatics, 2018 Abstract Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this ...
Jie Liu 0045 +3 more
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BMC Bioinformatics, 2022 Background Understanding the role of various factors in 3D genome organization is essential to determine their impact on shaping large-scale chromatin units such as euchromatin (A) and heterochromatin (B) compartments. At this level, chromatin compaction
Mikhail D. Magnitov +4 more
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Computational tools for Hi‐C data analysis
Quantitative Biology, 2017 BackgroundIn eukaryotic genome, chromatin is not randomly distributed in cell nuclei, but instead is organized into higher‐order structures. Emerging evidence indicates that these higher‐order chromatin structures play important roles in regulating genome functions such as transcription and DNA replication.
Zhijun Han, Gang Wei
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Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data. [PDF]
PLoS Computational Biology, 2016 Chromosome conformation capture (3C) techniques have revealed many fascinating insights into the spatial organization of genomes. 3C methods typically provide information about chromosomal contacts in a large population of cells, which makes it difficult
Simeon Carstens +2 more
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Computational and Structural Biotechnology Journal, 2021 The high-throughput genome-wide chromosome conformation capture (Hi-C) method has recently become an important tool to study chromosomal interactions where one can extract meaningful biological information including P(s) curve, topologically associated ...
Honglong Wu +5 more
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