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MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites

MOTIVATION: Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms....

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Detalles Bibliográficos
Autores principales: Zhang, Xianglin, Wang, Xiaowo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235486/
https://www.ncbi.nlm.nih.gov/pubmed/35758820
http://dx.doi.org/10.1093/bioinformatics/btac248
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author Zhang, Xianglin
Wang, Xiaowo
author_facet Zhang, Xianglin
Wang, Xiaowo
author_sort Zhang, Xianglin
collection PubMed
description MOTIVATION: Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns. RESULTS: We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest. AVAILABILITY AND IMPLEMENTATION: MeConcord is available at https://github.com/WangLabTHU/MeConcord. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-92354862022-06-29 MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites Zhang, Xianglin Wang, Xiaowo Bioinformatics ISCB/Ismb 2022 MOTIVATION: Intermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns. RESULTS: We proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest. AVAILABILITY AND IMPLEMENTATION: MeConcord is available at https://github.com/WangLabTHU/MeConcord. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-06-27 /pmc/articles/PMC9235486/ /pubmed/35758820 http://dx.doi.org/10.1093/bioinformatics/btac248 Text en © The Author(s) 2022. 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 ISCB/Ismb 2022
Zhang, Xianglin
Wang, Xiaowo
MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
title MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
title_full MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
title_fullStr MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
title_full_unstemmed MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
title_short MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites
title_sort meconcord: a new metric to quantitatively characterize dna methylation heterogeneity across reads and cpg sites
topic ISCB/Ismb 2022
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235486/
https://www.ncbi.nlm.nih.gov/pubmed/35758820
http://dx.doi.org/10.1093/bioinformatics/btac248
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