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Combinatorial identification of DNA methylation patterns over age in the human brain
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
Polish Acad Sci, Nencki Inst Expt Biol, Lab Bioinformat, Neurobiol Ctr, Warsaw, Poland..
Polish Acad Sci, Nencki Inst Expt Biol, Lab Bioinformat, Lab Mol Neurobiol, Warsaw, Poland..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
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2016 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 17, 393Article in journal (Refereed) Published
Abstract [en]

Background: 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. Results: 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. Conclusion: 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.

Place, publisher, year, edition, pages
2016. Vol. 17, 393
Keyword [en]
DNA methylation, Aging, Rule-based classification, Feature selection
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:uu:diva-305330DOI: 10.1186/s12859-016-1259-3ISI: 000383750700001OAI: oai:DiVA.org:uu-305330DiVA: diva2:1037360
Funder
Swedish Research Council FormaseSSENCE - An eScience Collaboration
Available from: 2016-10-14 Created: 2016-10-14 Last updated: 2017-11-29Bibliographically approved
In thesis
1. Computational discovery of DNA methylation patterns as biomarkers of ageing, cancer, and mental disorders: Algorithms and Tools
Open this publication in new window or tab >>Computational discovery of DNA methylation patterns as biomarkers of ageing, cancer, and mental disorders: Algorithms and Tools
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Epigenetics refers to the mitotically heritable modifications in gene expression without a change in the genetic code. A combination of molecular, chemical and environmental factors constituting the epigenome is involved, together with the genome, in setting up the unique functionality of each cell type.

DNA methylation is the most studied epigenetic mark in mammals, where a methyl group is added to the cytosine in a cytosine-phosphate-guanine dinucleotides or a CpG site. It has been shown to have a major role in various biological phenomena such as chromosome X inactivation, regulation of gene expression, cell differentiation, genomic imprinting. Furthermore, aberrant patterns of DNA methylation have been observed in various diseases including cancer.

In this thesis, we have utilized machine learning methods and developed new methods and tools to analyze DNA methylation patterns as a biomarker of ageing, cancer subtyping and mental disorders.

In Paper I, we introduced a pipeline of Monte Carlo Feature Selection and rule-base modeling using ROSETTA in order to identify combinations of CpG sites that classify samples in different age intervals based on the DNA methylation levels. The combination of genes that showed up to be acting together, motivated us to develop an interactive pathway browser, named PiiL, to check the methylation status of multiple genes in a pathway. The tool enhances detecting differential patterns of DNA methylation and/or gene expression by quickly assessing large data sets.

In Paper III, we developed a novel unsupervised clustering method, methylSaguaro, for analyzing various types of cancers, to detect cancer subtypes based on their DNA methylation patterns. Using this method we confirmed the previously reported findings that challenge the histological grouping of the patients, and proposed new subtypes based on DNA methylation patterns. In Paper IV, we investigated the DNA methylation patterns in a cohort of schizophrenic and healthy samples, using all the methods that were introduced and developed in the first three papers.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. 55 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1520
Keyword
DNA methylation, machine learning, biomarker, cancer, ageing, classification
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-320720 (URN)978-91-554-9924-2 (ISBN)
Public defence
2017-06-12, A1:111a, BMC Building, Husargatan 3, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2017-05-22 Created: 2017-04-24 Last updated: 2017-06-07

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Torabi Moghadam, BehroozGrabherr, Manfred G.Komorowski, Jan

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