Literature DB >> 35567415

Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays.

Robert Flynn1, Sam Washer1,2, Aaron R Jeffries1, Alexandria Andrayas3, Gemma Shireby1, Meena Kumari4, Leonard C Schalkwyk3, Jonathan Mill1, Eilis Hannon1.   

Abstract

Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionize the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the etiology of complex disease.
© The Author(s) 2022. Published by Oxford University Press.

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Year:  2022        PMID: 35567415      PMCID: PMC9476619          DOI: 10.1093/hmg/ddac112

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   5.121


  32 in total

1.  Blood-based profiles of DNA methylation predict the underlying distribution of cell types: a validation analysis.

Authors:  Devin C Koestler; Brock Christensen; Margaret R Karagas; Carmen J Marsit; Scott M Langevin; Karl T Kelsey; John K Wiencke; E Andres Houseman
Journal:  Epigenetics       Date:  2013-06-25       Impact factor: 4.528

2.  Power and sample size estimation for epigenome-wide association scans to detect differential DNA methylation.

Authors:  Pei-Chien Tsai; Jordana T Bell
Journal:  Int J Epidemiol       Date:  2015-05-13       Impact factor: 7.196

3.  Bigmelon: tools for analysing large DNA methylation datasets.

Authors:  Tyler J Gorrie-Stone; Melissa C Smart; Ayden Saffari; Karim Malki; Eilis Hannon; Joe Burrage; Jonathan Mill; Meena Kumari; Leonard C Schalkwyk
Journal:  Bioinformatics       Date:  2019-03-15       Impact factor: 6.937

Review 4.  Opportunities and challenges in long-read sequencing data analysis.

Authors:  Shanika L Amarasinghe; Shian Su; Xueyi Dong; Luke Zappia; Matthew E Ritchie; Quentin Gouil
Journal:  Genome Biol       Date:  2020-02-07       Impact factor: 13.583

5.  DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation.

Authors:  Yang Liu; Wojciech Rosikiewicz; Ziwei Pan; Nathaniel Jillette; Ping Wang; Aziz Taghbalout; Jonathan Foox; Christopher Mason; Martin Carroll; Albert Cheng; Sheng Li
Journal:  Genome Biol       Date:  2021-10-18       Impact factor: 17.906

6.  Genome-wide CpG density and DNA methylation analysis method (MeDIP, RRBS, and WGBS) comparisons.

Authors:  Daniel Beck; Millissia Ben Maamar; Michael K Skinner
Journal:  Epigenetics       Date:  2021-05-11       Impact factor: 4.861

7.  Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation.

Authors:  Jonathan Mill; Eilis Hannon; Dorothea Seiler Vellame; Isabel Castanho; Aisha Dahir
Journal:  BMC Genomics       Date:  2021-06-15       Impact factor: 3.969

8.  Tobacco smoking leads to extensive genome-wide changes in DNA methylation.

Authors:  Sonja Zeilinger; Brigitte Kühnel; Norman Klopp; Hansjörg Baurecht; Anja Kleinschmidt; Christian Gieger; Stephan Weidinger; Eva Lattka; Jerzy Adamski; Annette Peters; Konstantin Strauch; Melanie Waldenberger; Thomas Illig
Journal:  PLoS One       Date:  2013-05-17       Impact factor: 3.240

9.  Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling.

Authors:  Ruth Pidsley; Elena Zotenko; Timothy J Peters; Mitchell G Lawrence; Gail P Risbridger; Peter Molloy; Susan Van Djik; Beverly Muhlhausler; Clare Stirzaker; Susan J Clark
Journal:  Genome Biol       Date:  2016-10-07       Impact factor: 13.583

10.  Targeted nanopore sequencing with Cas9-guided adapter ligation.

Authors:  Timothy Gilpatrick; Isac Lee; James E Graham; Etienne Raimondeau; Rebecca Bowen; Andrew Heron; Bradley Downs; Saraswati Sukumar; Fritz J Sedlazeck; Winston Timp
Journal:  Nat Biotechnol       Date:  2020-02-10       Impact factor: 68.164

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