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genomeSidekick: A user-friendly epigenomics data analysis tool
Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increa...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580848/ https://www.ncbi.nlm.nih.gov/pubmed/36304311 http://dx.doi.org/10.3389/fbinf.2022.831025 |
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author | Chen, Junjie Zhu, Ashley J. Packard, René R. S. Vondriska, Thomas M. Chapski, Douglas J. |
author_facet | Chen, Junjie Zhu, Ashley J. Packard, René R. S. Vondriska, Thomas M. Chapski, Douglas J. |
author_sort | Chen, Junjie |
collection | PubMed |
description | Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increasing the complexity and difficulty of making meaningful inferences from large datasets. One gap in the sequencing data analysis pipeline is the availability of tools to efficiently browse genomic data for scientists that do not have bioinformatics training. To bridge this gap, we developed genomeSidekick, a graphical user interface written in R that allows researchers to perform bespoke analyses on their transcriptomic and chromatin accessibility or chromatin immunoprecipitation data without the need for command line tools. Importantly, genomeSidekick outputs lists of up- and downregulated genes or chromatin features with differential accessibility or occupancy; visualizes omics data using interactive volcano plots; performs Gene Ontology analyses locally; and queries PubMed for selected gene candidates for further evaluation. Outputs can be saved using the user interface and the code underlying genomeSidekick can be edited for custom analyses. In summary, genomeSidekick brings wet lab scientists and bioinformaticians into a shared fluency with the end goal of driving mechanistic discovery. |
format | Online Article Text |
id | pubmed-9580848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95808482022-10-26 genomeSidekick: A user-friendly epigenomics data analysis tool Chen, Junjie Zhu, Ashley J. Packard, René R. S. Vondriska, Thomas M. Chapski, Douglas J. Front Bioinform Bioinformatics Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increasing the complexity and difficulty of making meaningful inferences from large datasets. One gap in the sequencing data analysis pipeline is the availability of tools to efficiently browse genomic data for scientists that do not have bioinformatics training. To bridge this gap, we developed genomeSidekick, a graphical user interface written in R that allows researchers to perform bespoke analyses on their transcriptomic and chromatin accessibility or chromatin immunoprecipitation data without the need for command line tools. Importantly, genomeSidekick outputs lists of up- and downregulated genes or chromatin features with differential accessibility or occupancy; visualizes omics data using interactive volcano plots; performs Gene Ontology analyses locally; and queries PubMed for selected gene candidates for further evaluation. Outputs can be saved using the user interface and the code underlying genomeSidekick can be edited for custom analyses. In summary, genomeSidekick brings wet lab scientists and bioinformaticians into a shared fluency with the end goal of driving mechanistic discovery. Frontiers Media S.A. 2022-07-18 /pmc/articles/PMC9580848/ /pubmed/36304311 http://dx.doi.org/10.3389/fbinf.2022.831025 Text en Copyright © 2022 Chen, Zhu, Packard, Vondriska and Chapski. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioinformatics Chen, Junjie Zhu, Ashley J. Packard, René R. S. Vondriska, Thomas M. Chapski, Douglas J. genomeSidekick: A user-friendly epigenomics data analysis tool |
title | genomeSidekick: A user-friendly epigenomics data analysis tool |
title_full | genomeSidekick: A user-friendly epigenomics data analysis tool |
title_fullStr | genomeSidekick: A user-friendly epigenomics data analysis tool |
title_full_unstemmed | genomeSidekick: A user-friendly epigenomics data analysis tool |
title_short | genomeSidekick: A user-friendly epigenomics data analysis tool |
title_sort | genomesidekick: a user-friendly epigenomics data analysis tool |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580848/ https://www.ncbi.nlm.nih.gov/pubmed/36304311 http://dx.doi.org/10.3389/fbinf.2022.831025 |
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