Title |
Using high-density DNA methylation arrays to profile copy number alterations
|
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Published in |
Genome Biology, February 2014
|
DOI | 10.1186/gb-2014-15-2-r30 |
Pubmed ID | |
Authors |
Andrew Feber, Paul Guilhamon, Matthias Lechner, Tim Fenton, Gareth A Wilson, Christina Thirlwell, Tiffany J Morris, Adrienne M Flanagan, Andrew E Teschendorff, John D Kelly, Stephan Beck |
Abstract |
The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 5 | 20% |
United Kingdom | 4 | 16% |
Canada | 3 | 12% |
Belgium | 1 | 4% |
France | 1 | 4% |
Germany | 1 | 4% |
Brazil | 1 | 4% |
Australia | 1 | 4% |
Argentina | 1 | 4% |
Other | 2 | 8% |
Unknown | 5 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 52% |
Scientists | 9 | 36% |
Science communicators (journalists, bloggers, editors) | 2 | 8% |
Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 1% |
Uruguay | 2 | 1% |
United States | 2 | 1% |
France | 1 | <1% |
Italy | 1 | <1% |
United Kingdom | 1 | <1% |
Canada | 1 | <1% |
New Zealand | 1 | <1% |
Turkey | 1 | <1% |
Other | 4 | 2% |
Unknown | 163 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 49 | 27% |
Student > Ph. D. Student | 36 | 20% |
Student > Master | 23 | 13% |
Student > Bachelor | 11 | 6% |
Other | 10 | 6% |
Other | 27 | 15% |
Unknown | 23 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 48 | 27% |
Biochemistry, Genetics and Molecular Biology | 45 | 25% |
Medicine and Dentistry | 19 | 11% |
Computer Science | 10 | 6% |
Neuroscience | 5 | 3% |
Other | 11 | 6% |
Unknown | 41 | 23% |