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Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens

Aberrant DNA methylation is a universal feature of cancer. Here, we present a protocol for generating high-quality genome-scale DNA methylation sequencing data from a variety of human cancer biospecimens including immortalized cell lines, fresh-frozen surgical resections, and formalin-fixed paraffin...

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Detalles Bibliográficos
Autores principales: Rodger, Euan J., Stockwell, Peter A., Almomani, Suzan, Eccles, Michael R., Chatterjee, Aniruddha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682265/
https://www.ncbi.nlm.nih.gov/pubmed/37950864
http://dx.doi.org/10.1016/j.xpro.2023.102714
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author Rodger, Euan J.
Stockwell, Peter A.
Almomani, Suzan
Eccles, Michael R.
Chatterjee, Aniruddha
author_facet Rodger, Euan J.
Stockwell, Peter A.
Almomani, Suzan
Eccles, Michael R.
Chatterjee, Aniruddha
author_sort Rodger, Euan J.
collection PubMed
description Aberrant DNA methylation is a universal feature of cancer. Here, we present a protocol for generating high-quality genome-scale DNA methylation sequencing data from a variety of human cancer biospecimens including immortalized cell lines, fresh-frozen surgical resections, and formalin-fixed paraffin-embedded tissues. We describe steps for DNA extraction considerations, reduced representation bisulfite sequencing, data processing and quality control, and downstream data analysis and integration. This protocol is also applicable for other human diseases and methylome profiling in other organisms. For complete details on the use and execution of this protocol, please refer to Rodger et al. (2023).(1)
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spelling pubmed-106822652023-11-30 Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens Rodger, Euan J. Stockwell, Peter A. Almomani, Suzan Eccles, Michael R. Chatterjee, Aniruddha STAR Protoc Protocol Aberrant DNA methylation is a universal feature of cancer. Here, we present a protocol for generating high-quality genome-scale DNA methylation sequencing data from a variety of human cancer biospecimens including immortalized cell lines, fresh-frozen surgical resections, and formalin-fixed paraffin-embedded tissues. We describe steps for DNA extraction considerations, reduced representation bisulfite sequencing, data processing and quality control, and downstream data analysis and integration. This protocol is also applicable for other human diseases and methylome profiling in other organisms. For complete details on the use and execution of this protocol, please refer to Rodger et al. (2023).(1) Elsevier 2023-11-10 /pmc/articles/PMC10682265/ /pubmed/37950864 http://dx.doi.org/10.1016/j.xpro.2023.102714 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Rodger, Euan J.
Stockwell, Peter A.
Almomani, Suzan
Eccles, Michael R.
Chatterjee, Aniruddha
Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens
title Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens
title_full Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens
title_fullStr Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens
title_full_unstemmed Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens
title_short Protocol for generating high-quality genome-scale DNA methylation sequencing data from human cancer biospecimens
title_sort protocol for generating high-quality genome-scale dna methylation sequencing data from human cancer biospecimens
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682265/
https://www.ncbi.nlm.nih.gov/pubmed/37950864
http://dx.doi.org/10.1016/j.xpro.2023.102714
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