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GenomicDistributions: fast analysis of genomic intervals with Bioconductor

BACKGROUND: Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues ge...

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Autores principales: Kupkova, Kristyna, Mosquera, Jose Verdezoto, Smith, Jason P., Stolarczyk, Michał, Danehy, Tessa L., Lawson, John T., Xue, Bingjie, Stubbs, John T., LeRoy, Nathan, Sheffield, Nathan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003978/
https://www.ncbi.nlm.nih.gov/pubmed/35413804
http://dx.doi.org/10.1186/s12864-022-08467-y
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author Kupkova, Kristyna
Mosquera, Jose Verdezoto
Smith, Jason P.
Stolarczyk, Michał
Danehy, Tessa L.
Lawson, John T.
Xue, Bingjie
Stubbs, John T.
LeRoy, Nathan
Sheffield, Nathan C.
author_facet Kupkova, Kristyna
Mosquera, Jose Verdezoto
Smith, Jason P.
Stolarczyk, Michał
Danehy, Tessa L.
Lawson, John T.
Xue, Bingjie
Stubbs, John T.
LeRoy, Nathan
Sheffield, Nathan C.
author_sort Kupkova, Kristyna
collection PubMed
description BACKGROUND: Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data. RESULTS: GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs. CONCLUSIONS: GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08467-y.
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spelling pubmed-90039782022-04-13 GenomicDistributions: fast analysis of genomic intervals with Bioconductor Kupkova, Kristyna Mosquera, Jose Verdezoto Smith, Jason P. Stolarczyk, Michał Danehy, Tessa L. Lawson, John T. Xue, Bingjie Stubbs, John T. LeRoy, Nathan Sheffield, Nathan C. BMC Genomics Software BACKGROUND: Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data. RESULTS: GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs. CONCLUSIONS: GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08467-y. BioMed Central 2022-04-12 /pmc/articles/PMC9003978/ /pubmed/35413804 http://dx.doi.org/10.1186/s12864-022-08467-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Kupkova, Kristyna
Mosquera, Jose Verdezoto
Smith, Jason P.
Stolarczyk, Michał
Danehy, Tessa L.
Lawson, John T.
Xue, Bingjie
Stubbs, John T.
LeRoy, Nathan
Sheffield, Nathan C.
GenomicDistributions: fast analysis of genomic intervals with Bioconductor
title GenomicDistributions: fast analysis of genomic intervals with Bioconductor
title_full GenomicDistributions: fast analysis of genomic intervals with Bioconductor
title_fullStr GenomicDistributions: fast analysis of genomic intervals with Bioconductor
title_full_unstemmed GenomicDistributions: fast analysis of genomic intervals with Bioconductor
title_short GenomicDistributions: fast analysis of genomic intervals with Bioconductor
title_sort genomicdistributions: fast analysis of genomic intervals with bioconductor
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9003978/
https://www.ncbi.nlm.nih.gov/pubmed/35413804
http://dx.doi.org/10.1186/s12864-022-08467-y
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