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Comparison of EM-seq and PBAT methylome library methods for low-input DNA
DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1–10ng) from two methylome library prepar...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9542412/ https://www.ncbi.nlm.nih.gov/pubmed/34709110 http://dx.doi.org/10.1080/15592294.2021.1997406 |
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author | Han, Yanan Zheleznyakova, Galina Yurevna Marincevic-Zuniga, Yanara Kakhki, Majid Pahlevan Raine, Amanda Needhamsen, Maria Jagodic, Maja |
author_facet | Han, Yanan Zheleznyakova, Galina Yurevna Marincevic-Zuniga, Yanara Kakhki, Majid Pahlevan Raine, Amanda Needhamsen, Maria Jagodic, Maja |
author_sort | Han, Yanan |
collection | PubMed |
description | DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1–10ng) from two methylome library preparation protocols, enzymatic methyl-seq (EM-seq) and post-bisulfite adaptor tagging (PBAT) adapted from single-cell PBAT. EM-seq takes advantage of enzymatic activity while PBAT relies on conventional bisulfite conversion for detection of DNA methylation. We found that both methods accurately quantified DNA methylation genome-wide. They produced expected distribution patterns around genomic features, high C-T transition efficiency at non-CpG sites and high correlation between input amounts. However, EM-seq performed better in regard to library and sequencing quality, i.e. EM-seq produced larger insert sizes, higher alignment rates and higher library complexity with lower duplication rate compared to PBAT. Moreover, EM-seq demonstrated higher CpG coverage, better CpG site overlap and higher consistency between input series. In summary, our data suggests that EM-seq overall performed better than PBAT in whole-genome methylation quantification of low input samples. |
format | Online Article Text |
id | pubmed-9542412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-95424122022-10-08 Comparison of EM-seq and PBAT methylome library methods for low-input DNA Han, Yanan Zheleznyakova, Galina Yurevna Marincevic-Zuniga, Yanara Kakhki, Majid Pahlevan Raine, Amanda Needhamsen, Maria Jagodic, Maja Epigenetics Research Paper DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1–10ng) from two methylome library preparation protocols, enzymatic methyl-seq (EM-seq) and post-bisulfite adaptor tagging (PBAT) adapted from single-cell PBAT. EM-seq takes advantage of enzymatic activity while PBAT relies on conventional bisulfite conversion for detection of DNA methylation. We found that both methods accurately quantified DNA methylation genome-wide. They produced expected distribution patterns around genomic features, high C-T transition efficiency at non-CpG sites and high correlation between input amounts. However, EM-seq performed better in regard to library and sequencing quality, i.e. EM-seq produced larger insert sizes, higher alignment rates and higher library complexity with lower duplication rate compared to PBAT. Moreover, EM-seq demonstrated higher CpG coverage, better CpG site overlap and higher consistency between input series. In summary, our data suggests that EM-seq overall performed better than PBAT in whole-genome methylation quantification of low input samples. Taylor & Francis 2021-11-17 /pmc/articles/PMC9542412/ /pubmed/34709110 http://dx.doi.org/10.1080/15592294.2021.1997406 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Han, Yanan Zheleznyakova, Galina Yurevna Marincevic-Zuniga, Yanara Kakhki, Majid Pahlevan Raine, Amanda Needhamsen, Maria Jagodic, Maja Comparison of EM-seq and PBAT methylome library methods for low-input DNA |
title | Comparison of EM-seq and PBAT methylome library methods for low-input DNA |
title_full | Comparison of EM-seq and PBAT methylome library methods for low-input DNA |
title_fullStr | Comparison of EM-seq and PBAT methylome library methods for low-input DNA |
title_full_unstemmed | Comparison of EM-seq and PBAT methylome library methods for low-input DNA |
title_short | Comparison of EM-seq and PBAT methylome library methods for low-input DNA |
title_sort | comparison of em-seq and pbat methylome library methods for low-input dna |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9542412/ https://www.ncbi.nlm.nih.gov/pubmed/34709110 http://dx.doi.org/10.1080/15592294.2021.1997406 |
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