Results 21 to 30 of about 215,700 (227)

FIREcaller: Detecting frequently interacting regions from Hi-C data

Computational and Structural Biotechnology Journal, 2021
Hi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell ...
Cheynna Crowley, Yuchen Yang, Yunjiang Qiu, Benxia Hu, Armen Abnousi, Jakub Lipiński, Dariusz Plewczyński, Di Wu, Hyejung Won, Bing Ren, Ming Hu, Yun Li   +11 more
doaj   +1 more source

Motif-Hi-C: A motif-based framework for rapid quality control of Hi-C data. [PDF]

aBIOTECH
Kong D, Wang Y, Tang Y, Tian Y, Yang S, Wang Y, Zhang Y, Kong S.   +7 more
europepmc   +2 more sources

Software tools for visualizing Hi-C data [PDF]

Genome Biology, 2016
Abstract Recently developed, high-throughput assays for measuring the three-dimensional configuration of DNA in the nucleus have provided unprecedented insights into the relationship between DNA 3D configuration and function. However, accurate interpretation of data from assays such as ChIA-PET and Hi-C is challenging because the data ...
Galip Gürkan Yardımcı, William Stafford Noble   +1 more
openaire   +2 more sources

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

MHiC, an integrated user-friendly tool for the identification and visualization of significant interactions in Hi-C data

BMC Genomics, 2020
Background Hi-C is a molecular biology technique to understand the genome spatial structure. However, data obtained from Hi-C experiments is biased. Therefore, several methods have been developed to model Hi-C data and identify significant interactions ...
Saman Khakmardan, Mohsen Rezvani, Ali Akbar Pouyan, Mansoor Fateh, Hamid Alinejad-Rokny   +4 more
doaj   +1 more source

Detecting community structures in Hi-C genomic data [PDF]

2016 Annual Conference on Information Science and Systems (CISS), 2016
Community detection (CD) algorithms are applied to Hi-C data to discover new communities of loci in the 3D conformation of human and mouse DNA. We find that CD has some distinct advantages over pre-existing methods: (1) it is capable of finding a variable number of communities, (2) it can detect communities of DNA loci either adjacent or distant in the
Irineo Cabreros, Emmanuel Abbe, Aristotelis Tsirigos   +2 more
openaire   +2 more sources

NeoHiC: A Web Application for the Analysis of Hi-C Data [PDF]

, 2020
High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of chromatin interactions and 3D chromosome folding on a larger scale. A graph-based multi-level representation of Hi-C data is essential for proper visualisation of the spatial pattern they represent, in particular for comparing different experiments or for re-mapping ...
D'Agostino D, Lio P, Aldinucci M, Merelli I   +3 more
openaire   +6 more sources

Loop detection using Hi-C data with HiCExplorer [PDF]

GigaScience, 2020
Chromatin loops are an important factor in the structural organization of the genome. The detection of chromatin loops in Hi-C interaction matrices is a challenging and compute intensive task. The presented approach shows a chromatin loop detection algorithm which applies a strict candidate selection based on continuous negative binomial distributions ...
Joachim Wolff, Rolf Backofen, Björn Grüning   +2 more
openaire   +3 more sources

Computational Processing and Quality Control of Hi-C, Capture Hi-C and Capture-C Data [PDF]

Genes, 2019
Hi-C, capture Hi-C (CHC) and Capture-C have contributed greatly to our present understanding of the three-dimensional organization of genomes in the context of transcriptional regulation by characterizing the roles of topological associated domains, enhancer promoter loops and other three-dimensional genomic interactions.
Peter Hansen, Michael Gargano, Jochen Hecht, Jonas Ibn-Salem, Guy Karlebach, Johannes T. Roehr, Peter N. Robinson   +6 more
openaire   +4 more sources

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
doaj   +1 more source