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PiiL: visualization of DNA methylation and gene expression data in gene pathways
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. (Computational Biology and Bioinformatics)ORCID iD: 0000-0001-6212-8200
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Department of Plant Physiology, Umeå University, Umeå, Sweden. (Genomics)
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Institute of Computer Science, Polish Academy of Sciences, Warsaw, 01248, Poland. (Computational Biology and Bioinformatics)ORCID iD: 0000-0002-0766-8789
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. (Genomics)
2017 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 18, article id 571Article in journal (Refereed) Published
Abstract [en]

DNA methylation is a major mechanism involved in the epigenetic state of a cell. It has been observed that the methylation status of certain CpG sites close to or within a gene can directly affect its expression, either by silencing or, in some cases, up-regulating transcription. However, a vertebrate genome contains millions of CpG sites, all of which are potential targets for methylation modification, and the specific effects of most sites has not been characterized to date. To study the complex interplay between methylation status, cellular programs, and the resulting phenotypes, we present PiiL, an interactive gene expression pathway browser, facilitating the analysis through an integrated view of methylation and expression on multiple levels.

PiiL allows for specific hypothesis testing by quickly assessing pathways or gene networks, where the data is projected onto pathways that can be downloaded directly from the online KEGG database. PiiL provides a comprehensive set of analysis features, allowing for quickly searching for specific patterns, as well as to examine individual CpG sites and their impact on expression of the host gene and other genes in regulatory networks. To exemplify the power of this approach, we analyzed two types of brain tumors, Glioblastoma multiform and lower grade gliomas.

At a glance, we could confirm earlier findings that the predominant methylation and expression patterns separate perfectly by mutations in the IDH genes, rather than by histology. We could also infer the IDH mutation status for samples for which the genotype was not known. By applying different filtering methods, we show that a subset of CpG sites exhibits consistent methylation patterns, and that the status of sites affect the expression of key regulator genes, as well as other genes located downstream in the same pathways.

PiiL is implemented in Java with focus on a user-friendly graphical interface. The source code is available under the GPL license from https://github.com/behroozt/PiiL.git.

Place, publisher, year, edition, pages
2017. Vol. 18, article id 571
Keywords [en]
DNA methylation, gene expression, KEGG pathways, visualization
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-320675DOI: 10.1186/s12864-017-3950-9ISI: 000406759000002OAI: oai:DiVA.org:uu-320675DiVA, id: diva2:1090226
Funder
Swedish Research Council FormaseSSENCE - An eScience CollaborationAvailable from: 2017-04-23 Created: 2017-04-23 Last updated: 2018-01-13Bibliographically 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. p. 55
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1520
Keywords
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: 2018-01-13

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Torabi Moghadam, BehroozZamani, NedaKomorowski, JanGrabherr, Manfred
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