Results 41 to 50 of about 41,584,039 (265)
Reference panel guided topological structure annotation of Hi-C data
Nature Communications, 2022 Predicting topological structures from Hi-C data provides insight into comprehending gene expression and regulation. Here, the authors present RefHiC, an attention-based deep learning framework that leverages a reference panel of Hi-C datasets to assist ...
Yanlin Zhang, Mathieu Blanchette
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HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM. [PDF]
Brief Bioinform, 2023 Abstract Motivation The Hi-C experiments have been extensively used for the studies of mammalian genomic structures. In the last few years, spatiotemporal Hi-C has significantly contributed to the study of genome dynamic reorganization.
Liu T, Wang Z.
europepmc +3 more sources
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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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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HI-FRIENDS participation in the SKA Data Challenge 2
, 2021 This repository contains the workflow used to find and characterize the HI sources in the data cube of the SKA Data Challenge 2. This is developed by the HI-FRIENDS team.
Darriba, Laura +16 more
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Memory-Optimised Parallel Processing of Hi-C Data [PDF]
2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, 2015 This paper presents the optimisation efforts on the creation of a graph-based mapping representation of gene adjacency. The method is based on the Hi-C process, starting from Next Generation Sequencing data, and it analyses a huge amount of static data in order to produce maps for one or more genes.
DROCCO, MAURIZIO +4 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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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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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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