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HiCBricks: building blocks for efficient handling of large Hi-C datasets
SUMMARY: Genome-wide chromosome conformation capture based on high-throughput sequencing (Hi-C) has been widely adopted to study chromatin architecture by generating datasets of ever-increasing complexity and size. HiCBricks offers user-friendly and efficient solutions for handling large high-resolu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703765/ https://www.ncbi.nlm.nih.gov/pubmed/31697323 http://dx.doi.org/10.1093/bioinformatics/btz808 |
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author | Pal, Koustav Tagliaferri, Ilario Livi, Carmen Maria Ferrari, Francesco |
author_facet | Pal, Koustav Tagliaferri, Ilario Livi, Carmen Maria Ferrari, Francesco |
author_sort | Pal, Koustav |
collection | PubMed |
description | SUMMARY: Genome-wide chromosome conformation capture based on high-throughput sequencing (Hi-C) has been widely adopted to study chromatin architecture by generating datasets of ever-increasing complexity and size. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates functions for calling domain boundaries and functions for high-quality data visualization. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/devel/bioc/html/HiCBricks.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7703765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77037652020-12-07 HiCBricks: building blocks for efficient handling of large Hi-C datasets Pal, Koustav Tagliaferri, Ilario Livi, Carmen Maria Ferrari, Francesco Bioinformatics Applications Note SUMMARY: Genome-wide chromosome conformation capture based on high-throughput sequencing (Hi-C) has been widely adopted to study chromatin architecture by generating datasets of ever-increasing complexity and size. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates functions for calling domain boundaries and functions for high-quality data visualization. AVAILABILITY AND IMPLEMENTATION: http://bioconductor.org/packages/devel/bioc/html/HiCBricks.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-03-15 2019-11-07 /pmc/articles/PMC7703765/ /pubmed/31697323 http://dx.doi.org/10.1093/bioinformatics/btz808 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Pal, Koustav Tagliaferri, Ilario Livi, Carmen Maria Ferrari, Francesco HiCBricks: building blocks for efficient handling of large Hi-C datasets |
title | HiCBricks: building blocks for efficient handling of large Hi-C datasets |
title_full | HiCBricks: building blocks for efficient handling of large Hi-C datasets |
title_fullStr | HiCBricks: building blocks for efficient handling of large Hi-C datasets |
title_full_unstemmed | HiCBricks: building blocks for efficient handling of large Hi-C datasets |
title_short | HiCBricks: building blocks for efficient handling of large Hi-C datasets |
title_sort | hicbricks: building blocks for efficient handling of large hi-c datasets |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703765/ https://www.ncbi.nlm.nih.gov/pubmed/31697323 http://dx.doi.org/10.1093/bioinformatics/btz808 |
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