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GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis
DNA methylation in gene or gene body could influence gene transcription. Moreover, methylation in gene regions along with CpG island regions could modulate the transcription to undetectable gene expression levels. Therefore, it is necessary to investigate the methylation levels within the gene, gene...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Mary Ann Liebert, Inc., publishers
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994424/ https://www.ncbi.nlm.nih.gov/pubmed/33185472 http://dx.doi.org/10.1089/cmb.2020.0081 |
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author | Wang, Xiao Hao, Dan Kadarmideen, Haja N. |
author_facet | Wang, Xiao Hao, Dan Kadarmideen, Haja N. |
author_sort | Wang, Xiao |
collection | PubMed |
description | DNA methylation in gene or gene body could influence gene transcription. Moreover, methylation in gene regions along with CpG island regions could modulate the transcription to undetectable gene expression levels. Therefore, it is necessary to investigate the methylation levels within the gene, gene body, CpG island regions, and their overlapped regions and then identify the gene-based differentially methylated regions (GeneDMRs). In this study, R package GeneDMRs aims to facilitate computing gene-based methylation rate using next-generation sequencing-based methylome data. The user-friendly GeneDMRs package is presented to analyze the methylation levels in each gene/promoter/exon/intron/CpG island/CpG island shore or each overlapped region (e.g., gene-CpG island/promoter-CpG island/exon-CpG island/intron-CpG island/gene-CpG island shore/promoter-CpG island shore/exon-CpG island shore/intron-CpG island shore). GeneDMRs can also interpret complex interplays between methylation levels and gene expression differences or similarities across physiological conditions or disease states. We used the public reduced representation bisulfite sequencing data of mouse (GSE62392) for evaluating software and revealing novel biologically significant results to supplement the previous research. In addition, the whole-genome bisulfite sequencing data of cattle (GSE106538) given the much larger size was used for further evaluation. |
format | Online Article Text |
id | pubmed-7994424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Mary Ann Liebert, Inc., publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-79944242021-03-26 GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis Wang, Xiao Hao, Dan Kadarmideen, Haja N. J Comput Biol Research Articles DNA methylation in gene or gene body could influence gene transcription. Moreover, methylation in gene regions along with CpG island regions could modulate the transcription to undetectable gene expression levels. Therefore, it is necessary to investigate the methylation levels within the gene, gene body, CpG island regions, and their overlapped regions and then identify the gene-based differentially methylated regions (GeneDMRs). In this study, R package GeneDMRs aims to facilitate computing gene-based methylation rate using next-generation sequencing-based methylome data. The user-friendly GeneDMRs package is presented to analyze the methylation levels in each gene/promoter/exon/intron/CpG island/CpG island shore or each overlapped region (e.g., gene-CpG island/promoter-CpG island/exon-CpG island/intron-CpG island/gene-CpG island shore/promoter-CpG island shore/exon-CpG island shore/intron-CpG island shore). GeneDMRs can also interpret complex interplays between methylation levels and gene expression differences or similarities across physiological conditions or disease states. We used the public reduced representation bisulfite sequencing data of mouse (GSE62392) for evaluating software and revealing novel biologically significant results to supplement the previous research. In addition, the whole-genome bisulfite sequencing data of cattle (GSE106538) given the much larger size was used for further evaluation. Mary Ann Liebert, Inc., publishers 2021-03-01 2021-03-04 /pmc/articles/PMC7994424/ /pubmed/33185472 http://dx.doi.org/10.1089/cmb.2020.0081 Text en © Xiao Wang, et al., 2020. Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. |
spellingShingle | Research Articles Wang, Xiao Hao, Dan Kadarmideen, Haja N. GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis |
title | GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis |
title_full | GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis |
title_fullStr | GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis |
title_full_unstemmed | GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis |
title_short | GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis |
title_sort | genedmrs: an r package for gene-based differentially methylated regions analysis |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994424/ https://www.ncbi.nlm.nih.gov/pubmed/33185472 http://dx.doi.org/10.1089/cmb.2020.0081 |
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