|Title:||Bioinformatics from genetic variants to methylation|
|Abstract:||An important research topic in bioinformatics is the analysis of DNA, the molecule that encodes the genetic information of all organisms. The basis for this is sequencing, a procedure in which the sequence of DNA bases is determined. In addition to the identification of variations in the base sequence itself, advances in sequencing methods and a steady reduction in sequencing costs open up new fields of research: the analysis of functionally relevant non-base-related changes, so-called epigenetics. An important example of such a mechanism is DNA methylation, a process in which methyl groups are added to DNA without altering the sequence itself. Methylation takes place only at specific sites, and the methylation information of human DNA consists of approximately 30 million methylation levels between 0 and 1 in total. This thesis deals with problems and solutions for each phase of DNA methylation analysis. The most advanced method for detecting DNA methylation based on resolution is Whole-Genome Bisulfite Sequencing (WGBS), a technique that modifies DNA at unmethylated sites. We describe the special in-silico treatment required to process this altered DNA and existing concepts as well as newly developed bioinformatic methods for efficient determination of DNA methylation levels and their further processing with our developed tool camel. A common downstream analysis step is the detection of differentially methylated regions (DMRs), for which we have implemented a modification of the widely used method BSmooth in order to deal with today’s common data sizes. Setting up and creating new sequencing protocols, e.g., the mentioned WGBS, is complicated and requires adjustments to several parameters. We have developed a method based on a linear program (LP) that can predict the duplicate rate of supersamples. This critical quality measure represents the proportion of redundant data that in most cases needs to be removed from any further analysis. By using our method, it becomes possible to test, adjust and improve parameters for small test libraries only and to estimate the duplication rate for potential full-size samples. Once the sequencing protocol has been established, the methylation recognition of camel can be used as part of automated workflows, such as our mosquito workflow. This pipeline processes the generated WGBS samples from the raw data to the degree of methylation, including all essential intermediate steps. Such workflows are one of the central components of bioinformatics since the calculation must be parallel, reproducible and scalable. The distribution of the detected methylation levels, e.g., values of several samples at a specific location, can often be described as a beta-mixture model. The standard approach for estimating the parameters for such a model, the EM algorithm, has problems for data points of 0 or 1, which are very common as methylation levels. For this reason, we have developed an alternative algorithm based on moments that overcome this disadvantage.It is robust for data points within the closed interval [0; 1] and can also be applied to similar data sets in addition to methylation levels. This work deals not only with epigenetic but also with genetic variants. To analyze these, we present a second pipeline (ape) for data from targeted sequencing, where for example only genes are sequenced. The recognized variants then serve as input for our graphical environment eagle, a tool for computer scientists and geneticists to recognize possible causal genetic variants. As the name implies: The configuration of the analysis and presentation of the results is done via a graphical user interface. Unlike other tools, eagle is not based on databases, but on encapsulated hdf5 files. The use of this universal file-system-like data structure offers some advantages and makes the system easy to use especially for non-computer scientists. At the end of the thesis, we use all methods presented for the detection, analysis, and characterization of interindividual DMRs between several donors. This leads to some computational challenges because DMR detection is usually performed on two different groups. Our developed approach processes independent samples and calculates key metrics such as p-values and the number of undetectable DMRs. Through whole genome association studies (GWAS) on more than 1000 array data sets of methylation and variants, we show that (interindividual) DMRs as a subtype of epigenetics are related to genetic variation.|
|Subject Headings (RSWK):||Bioinformatik|
|Appears in Collections:||LS 11|
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