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

Euan J Rodger; Peter A Stockwell; Suzan Almomani; Michael R Eccles; Aniruddha Chatterjee

doi:10.1016/j.xpro.2023.102714

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

STAR Protoc. 2023 Dec 15;4(4):102714. doi: 10.1016/j.xpro.2023.102714. Epub 2023 Nov 10.

Authors

Euan J Rodger¹, Peter A Stockwell², Suzan Almomani², Michael R Eccles², Aniruddha Chatterjee³

Affiliations

¹ Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand. Electronic address: euan.rodger@otago.ac.nz.
² Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
³ Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand; Honorary Professor, UPES University, Dehradun, India. Electronic address: aniruddha.chatterjee@otago.ac.nz.

Abstract

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.

Keywords: Bioinformatics; Cancer; Genomics; Molecular Biology; Sequence analysis; Sequencing.

MeSH terms

DNA Methylation* / genetics
Humans
Neoplasms* / genetics
Sequence Analysis, DNA / methods