Title |
A data-driven approach to preprocessing Illumina 450K methylation array data
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Published in |
BMC Genomics, May 2013
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DOI | 10.1186/1471-2164-14-293 |
Pubmed ID | |
Authors |
Ruth Pidsley, Chloe C Y Wong, Manuela Volta, Katie Lunnon, Jonathan Mill, Leonard C Schalkwyk |
Abstract |
As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 4 | 44% |
Netherlands | 1 | 11% |
United States | 1 | 11% |
Unknown | 3 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 44% |
Scientists | 3 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 11% |
Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | <1% |
United Kingdom | 5 | <1% |
Norway | 2 | <1% |
Brazil | 2 | <1% |
Spain | 2 | <1% |
Israel | 1 | <1% |
Canada | 1 | <1% |
Germany | 1 | <1% |
Denmark | 1 | <1% |
Other | 3 | <1% |
Unknown | 593 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 164 | 27% |
Researcher | 124 | 20% |
Student > Master | 68 | 11% |
Student > Bachelor | 46 | 7% |
Student > Doctoral Student | 39 | 6% |
Other | 92 | 15% |
Unknown | 83 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 156 | 25% |
Biochemistry, Genetics and Molecular Biology | 139 | 23% |
Medicine and Dentistry | 70 | 11% |
Neuroscience | 32 | 5% |
Computer Science | 25 | 4% |
Other | 70 | 11% |
Unknown | 124 | 20% |