Title |
Combinatorial identification of DNA methylation patterns over age in the human brain
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Published in |
BMC Bioinformatics, September 2016
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DOI | 10.1186/s12859-016-1259-3 |
Pubmed ID | |
Authors |
Behrooz Torabi Moghadam, Michal Dabrowski, Bozena Kaminska, Manfred G. Grabherr, Jan Komorowski |
Abstract |
DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites. We utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 29% |
Portugal | 1 | 14% |
Unknown | 4 | 57% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 43% |
Members of the public | 2 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 32 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 25% |
Researcher | 8 | 25% |
Student > Bachelor | 3 | 9% |
Professor | 3 | 9% |
Student > Master | 3 | 9% |
Other | 4 | 13% |
Unknown | 3 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 7 | 22% |
Biochemistry, Genetics and Molecular Biology | 6 | 19% |
Neuroscience | 4 | 13% |
Agricultural and Biological Sciences | 4 | 13% |
Medicine and Dentistry | 2 | 6% |
Other | 6 | 19% |
Unknown | 3 | 9% |