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Dissertation, RWTH Aachen University, 2022; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagramme (2022). = Dissertation, RWTH Aachen University, 2022, Chromatin accessibility, or the physical access to chromatinized DNA, plays an essential role in controlling the temporal and spatial expression of genes in eukaryotic cells. Assay for transposase- accessible chromatin followed by high throughput sequencing (ATAC-seq) is a sensitive and straight- forward protocol for profiling chromatin accessibility in a genome-wide manner. Moreover, combined with single-cell sequencing technology, the single-cell ATAC-seq (scATAC-seq) is able to map reg- ulatory variation from hundreds to thousands of cells at single-cell resolution, further expanding its applications. However, a major drawback of scATAC-seq data is its inherent sparsity. In other words, many open chromatin regions are not detected due to low input or loss of DNA material in the scATAC-seq experiment, leaving a large number of missing values in the derived count matrix. Such a phenomenon is known as “drop-outs” and is also observed in other single-cell sequencing data, such as scRNA- seq. Although many computational methods have been proposed to address this issue for scRNA-seq based on data imputation or denoising, there is a substantial lack of efforts to assess the usability of these methods on scATAC-seq data. Moreover, the development of specific algorithms for imputing or denoising scATAC-seq is still poorly explored yet.Another critical issue when dealing with the scATAC-seq matrix is the high dimensionality. Be- cause a gene is often regulated by multiple cis-regulatory elements (CREs), the number of features in scATAC-seq (i.e., peaks) is usually one order magnitude higher compared with the number of features in scRNA-seq (i.e., genes). This high dimensionality poses a challenge for the analysis of scATAC-seq, such as clustering and visualization. Therefore, it is a common option to first perform dimensionality reduction prior to interpreting the data. However, the standard computational meth- ods for scRNA-seq data are potentially unsuitable for this task due to the low-count information of scATAC-seq data, i.e., a maximum of 2 digestion events is expected for an individual cell in a specific open chromatin region.In this thesis, we propose scOpen, a computation approach for simultaneous quantification of single-cell open chromatin status and reduction of the dimensionality, to address the aforementioned issues for scATAC-seq data analysis. More formally, scOpen performs imputation and denoising of a scATAC-seq matrix via regularized non-negative matrix factorization (NMF) based on term frequency-inverse document frequency (TF-IDF) transformation. We show that scOpen is able to improve several crucial downstream analysis steps of scATAC-seq data, such as clustering, visualization, cis-regulatory DNA interactions and delineation of regulatory features. Moreover, we also demonstrate its power to dissect chromatin accessibility dynamics on large-scale scATAC-seq data from intact mouse kidney tissue. Finally, we perform additional analyses to investigate the regulatory programs that drive the development of kidney fibrosis. Our analyses shed novel light on mechanisms of myofibroblasts differentiation driving kidney fibrosis and chronic kidney disease (CKD). Altogether, these results demonstrate that scOpen is a useful computational approach in biological studies involving single-cell open chromatin data processing., Published by RWTH Aachen University, Aachen

Gene expression is the process of selectively reading genetic information and it describes a life-essential mechanism in all known living organisms. Key players in the regulation of gene expression are proteins that interact with DNA. DNA-protein interaction sites are nowadays analyzed in a genome wide manner with chromatin immunoprecipitation followed by sequencing (ChIP-seq). With ChIP-seq it becomes possible to assign a discrete value to each genomic location. The value corresponds to the strength of the protein binding event. Peaks, that is, regions with a signal higher than expected by chance, correspond to the protein-DNA interaction sites. Detecting such peaks is the fundamental computational challenge in the ChIP-seq analysis. As in every complex wet lab protocol, ChIP-seq contains a wide range of potential biases. To reduce the effect of unwanted biases, ChIP-seq experiments are often replicated, which helps to distinguish between biological and random events and to verify the reliability of all experimental steps. Complex ChIP-seq based studies emphasize the demand of methods to compare replicated ChIP-seq signals which are associated with distinct biological conditions. These studies investigate the differential peak calling problem which is subject of current biological and medical research. Solving this problem leads to a deeper understanding of gene expression regulation. Several computational challenges arise when detecting differential peaks (DPs). First, the shape of ChIP-seq peaks depends on the underlying protein of interest. For ChIP-seq data of histone modifications, the DNA-protein interactions occur in mid-size to large domains. Here, domains can span several hundreds of base pairs and may have intricate patterns of gains and losses of ChIP-seq signals within the same domain. In contrast, ChIP-seq from transcription factors mostly happens in small isolated peaks. Second, artefacts, which arise due to the complexity of the ChIP-seq protocol, produce signals with distinct signal-to-noise ratios, even when they are produced in the same lab and follow the same protocols. Furthermore, different sequencing depths between samples aggravate the comparison of their ChIP-seq signal. Hence, a robust normalization method for the ChIP-seq signals is required. Finally, clinical samples, where patients have a distinct genetic background, introduce further variation to the distinct ChIP-seq signals. Moreover, replicated ChIP-seq experiments introduce further complexity which has to be reflected by the use of sophisticated statistical models. Current differential peak calling methods fail to cover all listed challenges. They apply heuristic signal segmentation strategies, such as window-based approaches, to identify DPs. There are only a few attempts to normalize ChIP-seq data. Furthermore, most methods do not support replicates. Hence, there is a clear need for computational methods that address all challenges. In this thesis, we propose ODIN and THOR, algorithms to determine changes of protein-DNA complexes for distinct cellular conditions in ChIP-seq experiments without and with replicates. We apply a statistical model (hidden Markov model) to call DPs and to handle replicates. We also introduce a novel normalization strategy which is based on control regions. These features lead to comprehensive algorithms that accurately call DPs in ChIP-seq signals. Moreover, the evaluation of differential peak calling algorithms is an open problem. The research community lacks both a direct metric to rate the algorithms and data sets with a genome wide map of DNA-protein interaction sites which can serve as gold standards. We propose two alternative approaches for the evaluation. First, we present indirect metrics to quantify DPs by taking advantage of gene expression data and second, we use simulation to customize artificial gold standards.

RWTH Aachen University, Diss., 2016; 128 Seiten(2016)., Transcriptional regulation orchestrates the proper temporal and spatial expression of genes. The identification of transcriptional regulatory elements, such as transcription factor binding sites (TFBSs), is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases.The standard computational approach is to use sequence-based methods, which search over the genome’s DNA for sequences representing the DNA binding affinity sequence of transcription factors (TFs). However, this approach is not able to predict active binding sites, i.e. binding sites that are being currently bound by TFs at a particular cell state. This happens as the sequence-based methods do not account for the fact that the chromatin dynamically changes its state between an open form (and accessible to TF binding) and closed (not accessible by TFs).Advances in next-generation sequencing techniques have enabled the measurement of such open chromatin regions in a genome-wide manner with assays such as the chromatin immunoprecipitation followed by massive sequencing (ChIP-seq) and DNase I digestion followed by massive sequencing (DNase-seq). Current research has proven that such open chromatin genome-wide assays improve sequence-based detection of active TFBSs. The rationale is to restrict the sequence-based search of binding sites to genomic regions where these assays indicate the chromatin is open and accessible for TF binding, in a cell-specific manner.We propose the first computational framework which integrates both DNase-seq and ChIP-seq data to perform predictions of active TFBSs. We have previously observed that there is a distinctive pattern at active TFBSs regarding both DNase-seq and ChIP-seq data. Our framework treats these data using signal normalization strategies and searches for these distinctive patterns, the so-called “footprints”, by segmenting the genome using hidden Markov models (HMMs). Given that, our framework - termed HINT (HMM-based identification of TF footprints) - is categorized as a “computational footprinting method”.We evaluate our computational footprinting method by comparing the footprint predictions to experimentally verified active TFBSs. Our evaluation approach creates statistics which enables the comparison between our method and competing computational footprinting methods. Our comparative experiment is the most complete so far, with a total of 14 computational footprinting methods and 233 TFs evaluated.Furthermore, we successfully applied our computational footprinting method HINT in two different biological studies to identify regulatory elements involved in specific biological conditions. HINT has proven to be a useful computational framework in biological studies involving regulatory genomics., Published by Aachen

Biological processes are complex study objects due to their dynamic nature and structural diversity of living organisms. To study dynamic processes statistically, numerous experiments with multiple observations have to be performed, and data have to be analyzed and evaluated. Owing to great technological advances, gigabytes of data are being acquired both in research and industry. Slow and subjective manual analyses are not sufficient anymore, and automated evaluation methods are required. The distribution of biomolecules provides valuable information on a current biological state. The distribution of biomolecules depends on and is influenced by functions of biomolecules, and may thus be used to detect abnormalities. The relatively young research field extit{toponomics} describes the laws of spatial arrangement of molecules. Several evaluation methods have previously been developed, automatized and standardized. However, no standard evaluation methods have been reported to quantitatively analyze such an important biological process like translocation of biomolecules. Translocation processes are vital for living organisms. For instance, substance inclusion into a cell or exclusion from it represent a translocation. Furthermore, signaling biomolecules translocate from the cytoplasm across the nuclear membrane into the nucleus to influence gene and protein expression. Investigating translocation processes may help to understand complex biological functions. It may also be used to analyze signaling events, or may even be employed for diagnostics and therapy monitoring. Manual and case-specific methods for quantitative translocation analysis are known, but fail to be generally applicable. Therefore, I have developed a novel generic automated approach. The method is based on microscopy images of biological samples. I have defined a generic method to quantitatively express distribution of biomolecules in numeric descriptors. Herewith, changes in distribution may be analyzed using different biological samples. Thus, the samples analyzed do not necessarily have to belong to a time series. Furthermore, not only cell cultures, but also tissue samples can be used for the analysis. Evaluations of cell cultures are simpler due to homogeneity and spatial separation of individual objects. However, structural polarity of the cells can be seen only in tissues. I have developed two workflows based on numeric descriptors for the distribution of bio-ewline molecules. The first workflow uses structure detection in images to localize the objects for evaluation. The second workflow avoids this complex operation by a structure-independent information extraction strategy. Both workflows are generic and may be applied to quantify a wide range of translocation processes.

While evaluating image based scientific experiments, experts must analyse and interprete large amounts of image and symbolic data. Visualizing experimental data supports domain experts in their evaluation tasks. Modern laboratory processes produce many images and measurements. Software supplying appropriate views of these data assists experts and enables the experiments' evaluation. The standard approach is developing individual software for each customer and domain. This development process is timeconsuming both for implementation itself and for communicating the detailed requirements to the development team. Additionally requirements change with the scientific progress in the application domains. This effort must be reduced. With this goal in mind, this thesis introduces BioZoom. BioZoom is an approach showing how a generic software solution supports the evaluation of image based scientific experiments. BioZoom consists of information zooming for fast and flexible view configuration by the end user and reuse of appropriate views. BioZoom uses metadata. Metadata describes entities and relationships from the application domain of image based experiments derivable from structured laboratory processes. With BioZoom, end users can compose views interactively with an information zooming tool and reuse task-relevant views. Users of BioZoom bookmark those views while exploring experimental data. While exploring users navigate from an experimental data overview to relevant subsets, further detailing the subsets in appropriate manner. Images are visually integrated with metadata - e.g. image position visualizes relationships between image data, and localisable entities are embedded in their image context. BioZoom generalizes bookmarked views with regard to the structure of the experimental data displayed within them. By reusing these view schemes users build a library of relevant view types. A view scheme will be instantiated in navigation automatically with the data available. Thus the user can easily and directly - starting from an initial data set - navigate to the view scheme required for the work task at hand. A large part of individual analysis software's functionality is realized in this end-user-development-approach. Illustrating this approach the thesis discusses two individual applications for visual evaluation and quality control drawn from an industry background. The applications are compared with BioZoom's views and navigational paths. It is shown that the generic end-user-development approach BioZoom can replace those specialised custom made applications.