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DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity
SUMMARY: Circadian oscillations of gene expression regulate daily physiological processes, and their disruption is linked to many diseases. Circadian rhythms can be disrupted in a variety of ways, including differential phase, amplitude and rhythm fitness. Although many differential circadian biomar...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889843/ https://www.ncbi.nlm.nih.gov/pubmed/36655766 http://dx.doi.org/10.1093/bioinformatics/btad039 |
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author | Xue, Xiangning Zong, Wei Huo, Zhiguang Ketchesin, Kyle D Scott, Madeline R Petersen, Kaitlyn A Logan, Ryan W Seney, Marianne L McClung, Colleen Tseng, George |
author_facet | Xue, Xiangning Zong, Wei Huo, Zhiguang Ketchesin, Kyle D Scott, Madeline R Petersen, Kaitlyn A Logan, Ryan W Seney, Marianne L McClung, Colleen Tseng, George |
author_sort | Xue, Xiangning |
collection | PubMed |
description | SUMMARY: Circadian oscillations of gene expression regulate daily physiological processes, and their disruption is linked to many diseases. Circadian rhythms can be disrupted in a variety of ways, including differential phase, amplitude and rhythm fitness. Although many differential circadian biomarker detection methods have been proposed, a workflow for systematic detection of multifaceted differential circadian characteristics with accurate false positive control is not currently available. We propose a comprehensive and interactive pipeline to capture the multifaceted characteristics of differentially rhythmic biomarkers. Analysis outputs are accompanied by informative visualization and interactive exploration. The workflow is demonstrated in multiple case studies and is extensible to general omics applications. AVAILABILITY AND IMPLEMENTATION: R package, Shiny app and source code are available in GitHub (https://github.com/DiffCircaPipeline) and Zenodo (https://doi.org/10.5281/zenodo.7507989) SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9889843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98898432023-02-02 DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity Xue, Xiangning Zong, Wei Huo, Zhiguang Ketchesin, Kyle D Scott, Madeline R Petersen, Kaitlyn A Logan, Ryan W Seney, Marianne L McClung, Colleen Tseng, George Bioinformatics Applications Note SUMMARY: Circadian oscillations of gene expression regulate daily physiological processes, and their disruption is linked to many diseases. Circadian rhythms can be disrupted in a variety of ways, including differential phase, amplitude and rhythm fitness. Although many differential circadian biomarker detection methods have been proposed, a workflow for systematic detection of multifaceted differential circadian characteristics with accurate false positive control is not currently available. We propose a comprehensive and interactive pipeline to capture the multifaceted characteristics of differentially rhythmic biomarkers. Analysis outputs are accompanied by informative visualization and interactive exploration. The workflow is demonstrated in multiple case studies and is extensible to general omics applications. AVAILABILITY AND IMPLEMENTATION: R package, Shiny app and source code are available in GitHub (https://github.com/DiffCircaPipeline) and Zenodo (https://doi.org/10.5281/zenodo.7507989) SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2023-01-19 /pmc/articles/PMC9889843/ /pubmed/36655766 http://dx.doi.org/10.1093/bioinformatics/btad039 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://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 Xue, Xiangning Zong, Wei Huo, Zhiguang Ketchesin, Kyle D Scott, Madeline R Petersen, Kaitlyn A Logan, Ryan W Seney, Marianne L McClung, Colleen Tseng, George DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity |
title | DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity |
title_full | DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity |
title_fullStr | DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity |
title_full_unstemmed | DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity |
title_short | DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity |
title_sort | diffcircapipeline: a framework for multifaceted characterization of differential rhythmicity |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889843/ https://www.ncbi.nlm.nih.gov/pubmed/36655766 http://dx.doi.org/10.1093/bioinformatics/btad039 |
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