Population structure is an important field of study due to its importance in finding underlying genetics of various diseases.This is why this thesis has looked at a newly presented deep convolutional autoencoder that has been showing promising results when compared to the state-of-the-art method for quantifying genetic similarities within population structure. The main focus was to introduce data augmentation in the form of artificial diploid recombination to this autoencoder in an attempt to increase performance and robustness of the network structure.  The training data for the network consist of arrays containing information about single-nucleotide polymorphisms present in an individual. Each instance of augmented data was simulated by randomising cuts based on the distance between the polymorphisms, and then creating a new array by alternating between the arrays of two randomised original data instances. Several networks were then trained using this data augmentation. The performance of the trained networks was compared to networks trained on only original data using several metrics. Both groups of networks had similar performance for most metrics. The main difference was that networks trained on only original data had a low genotype concordance on simulated data. This indicates an underlying risk using the original networks, which can be overcome by introducing the artificial recombination.

Wound healing is a complex physiological process. Effective wound healing enables the skin barrier function to be restored once the skin is injured. However, due to the complex nature of wounds, the mechanisms underlying tissue repair are still poorly understood. This has hindered the development of treatment for chronic wound, which is posing threat to both human health system and economy. Long non-coding RNAs (lncRNAs) have been identified as important gene expression regulators and to play functional roles in many biological processes.  The aim of this study was to unravel the gene regulatory network in human skin wound healing, in particular, to identify lncRNAs that may play a functional role in skin repair. Here we performed RNA sequencing to profile gene expression in fibroblasts and keratinocytes isolated from matched skin and day-7 acute wounds of five healthy donors. We predicted a total of 1974 and 3444 mRNA–lncRNA correlated pairs in wound fibroblasts and wound keratinocytes, respectively. By integrating the results from gene ontology enrichment and weighted co-expression network analysis, we shortlisted lncRNAs that may play a functional role in human skin wound healing.

Early events in folding can determine if a protein is going to fold, misfold, or aggregate. Understanding these deterministic events is paramount for de novo protein engineering, the enhancement of biopharmaceutical stabilities, and understanding neurodegenerative diseases including amyotrophic lateral sclerosis and Alzheimer's disease. However, the physicochemical and structural biases within high energy states of protein biopolymers are poorly understood. A combined experimental and computational study was conducted on the small β/α-repeat protein CheY to determine the structural basis of its submillisecond misfolding reaction to an off-pathway intermediate. Using permutations, we were able to discriminate between the roles of two proposed mechanisms of folding; a nucleation condensation model, and a hydrophobic collapse model driven by the formation of clusters of isoleucine, leucine, and valine (ILV) residues. We found that by altering the ILV cluster connectivity we could bias the early folding events to either favor on or off-pathway intermediates. Structural biases were also experimentally observed in the unfolded state of a de novo designed synthetic β/α-repeat protein, Di-III_14. Although thermodynamically and kinetically 2-state, Di-III_14 has a well structured unfolded state that is only observable under native-favoring conditions. This unfolded state appears to retain native-like structure, consisting of a hydrophobic 7 core (69% ILV) stabilized by solvent exposed polar groups and long range electrostatic interactions. Together, these results suggest that early folding events are largely deterministic in these two systems. Generally, low contact order ILV clusters favor local compaction and, in specific cases, long range electrostatic interactions may have stabilizing effects in higher energy states.

mRNA decay is an important step in gene regulation, environmental responsiveness, and mRNA quality control. One such quality control pathway, Nonsense-mediated mRNA Decay (NMD), targets transcripts whose translation terminates prematurely. However, the scope and the defining features of NMD-targeted transcripts remain elusive. To address these issues, we re-evaluated the genome-wide expression of annotated transcripts in yeast cells harboring deletions of the UPF1, UPF2, or UPF3 genes. The vast majority of NMD-regulated transcripts are normal-looking protein-coding mRNAs. Our bioinformatics analyses reveal that this set of NMD-regulated transcripts generally have lower translational efficiency, lower average codon optimality scores, and higher ratios of out-of-frame translation. General mRNA decay is predominantly mediated by decapping by the Dcp1-Dcp2 complex and 5' to 3' decay by Xrn1, but the exact mechanism of decapping regulation has remained largely unknown. Several in vitro and in vivo studies have revealed the importance of the C-terminal extension of Dcp2 and the identities of many decapping regulators that interact with the decapping complex. To better understand how decapping regulation is achieved by the C-terminal extension of Dcp2 we generated RNA-Seq libraries from a Dcp2 allele that lacks this portion of Dcp2 along with libraries from strains that contain single deletions of several decapping activators. Our transcriptome-wide results indicate that the C-terminal extension of Dcp2 is crucial for efficient regulation of decapping, and different decapping activators are responsible for targeting different sets of mRNAs. Considering the limited pool of Dcp1-Dcp2 in the cell decapping activators might be in competition for decapping complex binding. Collectively, our results yield valuable insights into the mechanism of substrate selection for mRNA quality control and decay in yeast.

A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes.

Functioning of the living cell is controlled by a complex network of interactions among genes, proteins, and other molecules. A major goal of systems biology is to understand and explain the mechanisms by which these interactions govern the cell's response to various conditions. Molecular interaction networks have proven to be a powerful representation for studying cellular behavior. Numerous algorithms have been developed to unravel the complexity of these networks. Our work addresses the drawbacks of existing techniques. This thesis includes three related research efforts that introduce network-based approaches to bridge current methodological gaps in systems biology. i. Functional enrichment methods provide a summary of biological functions that are overrepresented in an interesting collection of genes (e.g., highly differentially expressed genes between a diseased cell and a healthy cell). Standard functional enrichment algorithms ignore the known interactions among proteins. We propose a novel network-based approach to functional enrichment that explicitly accounts for these underlying molecular interactions. Through this work, we close the gap between set-based functional enrichment and topological analysis of molecular interaction networks. ii. Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression p-values such that two genes connected by an interaction show similar changes in their gene expression values. iii. Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models of cellular processes. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently. We present Linker, an efficient and automated data-driven method that analyzes molecular interactomes. Linker combines teleporting random walks and k-shortest path computations to discover connections from a set of source proteins to a set of target proteins. We demonstrate the efficacy of Linker through two applications: proposing extensions to an existing model of cell cycle regulation in budding yeast and automated reconstruction of human signaling pathways. Linker achieves superior precision and recall compared to state-of-the-art algorithms from the literature. / Ph. D.

The Norway spruce Picea abies is an economically important export to the Swedish economy. There are a number of environmental and endogenous factors that impact the generation time of this species meaning that it can take 20-25 years for a tree to mature. The long generation time creates a challenge for plant breeding programs in terms of how genetic mechanisms are able to be studied as well as how quickly trees can be produced for lumber. The characterization of gene expression patterns in the context of special tissue domains is essential to understanding the underlying functions behind complex biological systems and in the case of P. abies may prove more crucial to determining the activation of genes at specific reproductive growth points. There are several techniques available for the analysis of spatial expression profiles, however, the unique high throughput nature coupled to the morphological information provided by Spatial Transcriptomics creates new opportunities for exploratory analysis. Spatial Transcriptomics offers a distinct approach to answering fundamental questions about the genetic mechanisms that regulate reproductive phase change and cone-setting in conifers. This study focuses on spatial gene expression analysis and the integration of de novo transcriptome assembly contigs to confirm the spatial context of putatively discovered genes such as DAL1, DAL2, DAL3, and DAL10 from previous studies and to potentially localize transcripts that could not previously be identified due to the inability to obtain complete transcripts. The aim is to create a workflow to identify genes that contribute to the growth patterns in the naturally occurring acrocona mutant that could prove useful to improving tree breeding programs.

Information retrieval within a company can be referred to as enterprise search. With the use of enterprise search, employees can find the information they need in company internal data. If a business can take advantage of the knowledge within the organization, it can save time and effort, and be a source for innovation and development within the company.  In this project, two open source search engines, Recoll and Apache Solr, are selected, set up, and evaluated based on requirements and needs at the pharmacometric consulting company Pharmetheus AB. A requirement analysis is performed to collect system requirements at the company. Through a literature survey, two candidate search engines are selected. Lastly, a Proof of Concept is performed to demonstrate the feasibility of the search engines at the company. The search tools are evaluated on criteria including indexing performance, search functionality and configurability. This thesis presents assessment questions to be used when evaluating a search tool. It is shown that the indexing time for both Recoll and Apache Solr appears to scale linearly for less than one hundred thousand pdf documents. The benefit of an index is demonstrated when search times for both search engines greatly outperforms the Linux command-line tools grep and find. It is also explained how the strict folder structure and naming conventions at the company can be used in Recoll to only index specific documents and sub-parts of a file share. Furthermore, I demonstrate how the Recoll web GUI can be modified to include functionality for filtering on document type.  The results show that Recoll meets most of the company’s system requirements and for that reason it could serve as an enterprise search engine at the company. However, the search engine lacks support for authentication, something that has to be further investigated and implemented before the system can be put into production.

Protein-protein and protein-peptide interactions play a central role in various aspects of the structural and functional organization of the cell. While the most complete structural characterization is provided by X-ray crystallography, many biological interactions occur in complexes that will not be amenable to direct experimental analysis. Therefore, it is important to develop computational docking methods that start from the structures of component proteins and predict the structure of their complexes, preferably with accuracy close to that provided by X-ray crystallography. This thesis details three applications of computational protein modeling, including the study of antibody maturation mechanisms, and the development of protocols for peptide-protein interaction prediction and template-based modeling of protein complexes. The first project, a comparative analysis of docking an antigen structure to antibodies across a lineage, reveals insights into antibody maturation mechanisms. A linear relationship between near-native docking results and changes in binding free energy is established, and used to investigate changes in binding affinity following mutation across two antibody-antigen systems: influenza and anthrax. The second project demonstrates that a motif-based search of available protein crystal structures is sufficient to adequately represent the conformational space sampled by a flexible peptide, compared to that of a rigid globular protein. This observation forms the basis for a global peptide-protein docking protocol that has since been implemented into the Structural Bioinformatics Laboratory’s docking web server, ClusPro. Finally, as structure availability remains a roadblock to many studies, researchers turn to homology modeling, in which the desired protein sequence is modeled onto a related structure. This is particularly challenging when the target is a protein complex, further restricting template availability. To address this problem, the third project details the development of a new template-based modeling protocol to be integrated into the ClusPro server. The implementation of a novel template-based search enables users to model both homomeric and heteromeric complexes, greatly expanding ClusPro server functionality. / 2020-08-30T00:00:00Z

Sequencing efforts for gymnosperm genomes typically focus on nuclear and chloroplast DNA, with only three complete mitochondrial genomes published as of 2017. The availability of additional mitochondrial genomes would aid biological and evolutionary understanding of gymnosperms. Identifying mtDNA from existing whole genome sequencing (WGS) data (i.e. contigs) negates the need for additional experimental work but previous classification methods show limitations in sensitivity or accuracy, particularly in difficult cases. In this thesis I present a classification pipeline based on (1) kmer probability scoring and (2) SVM classification applied to the available contigs. Using this pipeline the mitochondrial genomes of six gymnosperm species were obtained: Abies sibirica, Gnetum gnemon, Juniperus communis, Picea abies, Pinus sylvestris and Taxus baccata. Cross-validation experiments showed a satisfying and forsome species excellent degree of accuracy. / Vid sekvensering av gymnospermers arvsmassa har fokus oftast lagts på kärn- och kloroplast-DNA. Bara tre fullständiga mitokondriegenom har publicerats hittills (2017). Fler mitokondriegenom skulle kunna leda till nya kunskaper om gymnospermers biologi och evolution. Då mitokondriernas arvsmassa identifieras från tillgängliga sekvenser för hela organismen (så kallade “contiger”) behövs inget ytterligare laboratoriearbete, men detta förfarande har visat sig leda till bristfällig känslighet och korrekthet, särskilt i svåra fall. I denna avhandling presenterar jag en metod baserad på (1) kmer-sannolikheter och (2) SVM-klassificering applicerad på de tillgängliga contigerna. Med denna metod togs arvsmassan för mitokondrien hos sex gymnospermer fram: Abies sibirica, Gnetum gnemon, Juniperus communis, Picea abies, Pinus sylvestris och Taxus baccata. Korsvalideringsexperiment visade en tillfredställande och för vissa arter utmärkt precision.