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Bioinformatics analysis of human epigenetic data

Mekouar Nilsen, Nada (2015) BINP32 20142
Degree Projects in Bioinformatics
Abstract
Diabetes mellitus is a result of various metabolic disorders causing continues hyperglycemia, which increases the risk for heart diseases, stroke, kidney failure and blindness. This disease is seen in a large section of world’s population and rapidly increasing worldwide. (1)

The epigenetic marks can be transmitted by eukaryotic cell division (Mitosis) and also between generations of a species (Meiosis) (2). But these marks can be modified during the development by environmental factors(3). The epigenetic factors are necessary for an organism’s life and development but if it happens improperly then the organism’s health may be negatively affected (2, 4). DNA methylation is an epigenetic mark in humans and most vertebrates. DNA... (More)
Diabetes mellitus is a result of various metabolic disorders causing continues hyperglycemia, which increases the risk for heart diseases, stroke, kidney failure and blindness. This disease is seen in a large section of world’s population and rapidly increasing worldwide. (1)

The epigenetic marks can be transmitted by eukaryotic cell division (Mitosis) and also between generations of a species (Meiosis) (2). But these marks can be modified during the development by environmental factors(3). The epigenetic factors are necessary for an organism’s life and development but if it happens improperly then the organism’s health may be negatively affected (2, 4). DNA methylation is an epigenetic mark in humans and most vertebrates. DNA methylation usually occurs on the cytosine base (C) when it is followed by a guanosine base (G) a so called CpG site and a methyl group is attached to the carbon 5 position in the cytosine ring (2, 5).

In this work bioinformatics analysis of Infinium HumanMethylation450 BeadChip data was performed in two different kind of human tissues; liver and pancreatic islets. DNA methylation of 485,577 probes were analyzed in genomic DNA extracted from samples of donors with type 2 diabetes as well as in human samples from non-diabetic subjects. The degree of DNA methylation has been related to disease status using bioinformatics and statistical methods. Examples of these methods are normalization, background correction, quality control, batch correction, Principal component analysis, Hierarchical clustering and multiple linear regression.

This master thesis will identify CpG sites where diabetes is associated with differential DNA methylation. Different bioinformatics methods will be compared and the methods and results for the different studies will be described in this Master’s thesis paper. (Less)
Popular Abstract
Diabetes mellitus is characterized by chronic hyperglycemia. This results in an increased risk for heart diseases, stroke, kidney failure and blindness. This disease is seen in a large section of world’s population and it is rapidly increasing worldwide.

Epigenetic marks can be transmitted through eukaryotic cell division (Mitosis) and also between generations of a species (Meiosis).These marks can also be modified by environmental factors. The epigenetic factors are necessary for an organism’s life and development but if it is regulated improperly then the organism’s health may be negatively affected. DNA methylation is an epigenetic mark in humans and most vertebrates. DNA methylation usually occurs on the cytosine base (C) when it is... (More)
Diabetes mellitus is characterized by chronic hyperglycemia. This results in an increased risk for heart diseases, stroke, kidney failure and blindness. This disease is seen in a large section of world’s population and it is rapidly increasing worldwide.

Epigenetic marks can be transmitted through eukaryotic cell division (Mitosis) and also between generations of a species (Meiosis).These marks can also be modified by environmental factors. The epigenetic factors are necessary for an organism’s life and development but if it is regulated improperly then the organism’s health may be negatively affected. DNA methylation is an epigenetic mark in humans and most vertebrates. DNA methylation usually occurs on the cytosine base (C) when it is followed by a guanosine base (G), a so called CpG site and a methyl group is attached to the carbon 5 position in the cytosine ring.

DNA methylation bioinformatics analysis
In this work bioinformatics analysis of Infinium HumanMethylation450 BeadChip data was performed in two different human tissues; liver from one dataset and pancreatic islets from three different datasets. DNA methylation of 485,577 probes was analyzed in genomic DNA extracted from samples of donors with type 2 diabetes as well as from non-diabetic subjects. The degree of DNA methylation was been related to type 2 diabetes disease status using bioinformatics and statistical methods. Examples of these methods are normalization, background correction, quality control, batch correction, Principal component analysis (PCA), hierarchical clustering and multiple linear regression analysis.

This master thesis identified CpG sites where diabetes is associated with differential DNA methylation. Different bioinformatics methods are compared and the methods and results for the different studies will be described in this Master’s thesis paper. Methylation data from the human liver showed association between gender and methylation using both PCA and hierarchical clustering analyses. Furthermore, Beta Mixture Quantile (BMIQ) normalization affected results generated from both human liver and pancreatic The results from the second dataset of human pancreatic islets demonstrated that the majority of the published data were possible to replicate by the use of the bioinformatics approach used in this thesis.

Supervisor: Charlotte Ling
Master´s Degree Project 60 credits in Bioinformatics 2015
Department of Biology, Lund University (Less)
Please use this url to cite or link to this publication:
author
Mekouar Nilsen, Nada
supervisor
organization
course
BINP32 20142
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
7357179
date added to LUP
2015-06-16 09:18:36
date last changed
2015-06-18 14:04:28
@misc{7357179,
  abstract     = {Diabetes mellitus is a result of various metabolic disorders causing continues hyperglycemia, which increases the risk for heart diseases, stroke, kidney failure and blindness. This disease is seen in a large section of world’s population and rapidly increasing worldwide. (1) 

The epigenetic marks can be transmitted by eukaryotic cell division (Mitosis) and also between generations of a species (Meiosis) (2). But these marks can be modified during the development by environmental factors(3). The epigenetic factors are necessary for an organism’s life and development but if it happens improperly then the organism’s health may be negatively affected (2, 4). DNA methylation is an epigenetic mark in humans and most vertebrates. DNA methylation usually occurs on the cytosine base (C) when it is followed by a guanosine base (G) a so called CpG site and a methyl group is attached to the carbon 5 position in the cytosine ring (2, 5). 

In this work bioinformatics analysis of Infinium HumanMethylation450 BeadChip data was performed in two different kind of human tissues; liver and pancreatic islets. DNA methylation of 485,577 probes were analyzed in genomic DNA extracted from samples of donors with type 2 diabetes as well as in human samples from non-diabetic subjects. The degree of DNA methylation has been related to disease status using bioinformatics and statistical methods. Examples of these methods are normalization, background correction, quality control, batch correction, Principal component analysis, Hierarchical clustering and multiple linear regression. 

This master thesis will identify CpG sites where diabetes is associated with differential DNA methylation. Different bioinformatics methods will be compared and the methods and results for the different studies will be described in this Master’s thesis paper.},
  author       = {Mekouar Nilsen, Nada},
  language     = {eng},
  note         = {Student Paper},
  title        = {Bioinformatics analysis of human epigenetic data},
  year         = {2015},
}