A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

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Evaluation of affinity-based genome-wide DNA methylation data: Effects of CpG density, amplification bias, and copy number variation

Genome Research ◽

10.1101/gr.110601.110 ◽

2010 ◽

Vol 20 (12) ◽

pp. 1719-1729 ◽

Cited By ~ 92

Author(s):

M. D. Robinson ◽

C. Stirzaker ◽

A. L. Statham ◽

M. W. Coolen ◽

J. Z. Song ◽

...

Keyword(s):

Dna Methylation ◽

Copy Number Variation ◽

Copy Number ◽

Methylation Data ◽

Amplification Bias ◽

Genome Wide ◽

Number Variation

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Improved reporting of DNA methylation data derived from studies of the human placenta

Epigenetics ◽

10.4161/epi.27648 ◽

2014 ◽

Vol 9 (3) ◽

pp. 333-337 ◽

Cited By ~ 15

Author(s):

Kirsten Hogg ◽

E Magda Price ◽

Wendy P Robinson

Keyword(s):

Dna Methylation ◽

Human Placenta ◽

Methylation Data

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Network-based classification of recurrent endometrial cancers using high-throughput DNA methylation data

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '12 ◽

10.1145/2382936.2382990 ◽

2012 ◽

Cited By ~ 2

Author(s):

Jianhua Ruan ◽

Tim H. Huang ◽

Md. Jamiul Jahid ◽

Fei Gu ◽

Chengwei Lei ◽

...

Keyword(s):

Dna Methylation ◽

High Throughput ◽

Methylation Data ◽

Endometrial Cancers

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Computational and Statistical Analysis of Array-Based DNA Methylation Data

Methods in Molecular Biology - Cancer Bioinformatics ◽

10.1007/978-1-4939-8868-6_10 ◽

2018 ◽

pp. 173-191 ◽

Cited By ~ 2

Author(s):

Jessica Nordlund ◽

Christofer Bäcklin ◽

Amanda Raine

Keyword(s):

Dna Methylation ◽

Statistical Analysis ◽

Methylation Data

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MinION nanopore sequencing provides similar methylation estimates to Sanger bisulfite sequencing in the TRPA1 promoter region

10.1101/2021.09.17.460763 ◽

2021 ◽

Author(s):

Sara Gombert ◽

Kirsten Jahn ◽

Hansi Pathak ◽

Alexandra Burkert ◽

Gunnar Schmidt ◽

...

Keyword(s):

Dna Methylation ◽

Gold Standard ◽

Promoter Region ◽

Bisulfite Sequencing ◽

Nanopore Sequencing ◽

Methylation Data ◽

New Approaches

Bisulfite sequencing has long been considered the gold standard for measurement of DNA methylation at single CpG resolution. In the meantime, several new approaches have been developed, which are regarded as less error-prone. Since these errors were shown to be sequence-specific, we aimed to verify the methylation data of a particular region of the TRPA1 promoter obtained from our previous studies. For this purpose, we compared methylation rates obtained via direct bisulfite sequencing and nanopore sequencing. Thus, we were able to confirm our previous findings to a large extent.

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