Eimeria tenella is a eukaryotic, intracellular parasite that, along with six other Eimeria species, causes coccidiosis in chickens. This disease can result in weight loss or even death and is estimated to cause 2 billion euros of damages to the chicken industry each year. While much is known of the life cycle of E. tenella in the chicken, less is known about molecular mechanisms of infection and the chicken immune response. In this study, we produced a pipeline for dual RNA-sequencing analysis of a mixed chicken and E. tenella dataset.  We then carried out an analysis on an in vitro infection of the chicken macrophage HD-11 cell line.  This was followed by a differential expression analysis across six time points, 2, 4, 12, 24, 48, and 72 hours post-infection, in order to elucidate these mechanisms. The results showed clear patterns of expression for the chicken immune genes, with strong down-regulation of genes across the immune system at 24 hours and a repetition of early patterns at 72 hours, indicating that reinfection by a second generation of parasite cells was occurring. Several genes that may have important roles in the immune reaction of the chicken were identified, such as MRC2, ITGB3 and ITGA9, along with genes with known roles, such as TLR15. The expression of surface antigen genes in E. tenella was also examined, showing a clear upregulation in the late stages of merogony, suggesting important roles for merozoites. Finally, a co-expression analysis was carried out, showing considerable co-expression among the two organisms.  One of the gene co-expression networks identified appeared to be enriched with both infection specific genes from E. tenella and chicken immune genes. These results, along with the pipeline, will be used in further studies on E. tenella infections and bring us closer to the eventual goal of a vaccine for coccidiosis.

One of the main goals of genetics has been to understand the link between genotype and phenotype. Using yeast (Saccharomyces cerevisiae) as our model organism, we take a closer look at the connection between genetic variation and gene expression to learn more about the mechanisms of gene regulation. We propose an algorithm based on ANOVA to detect causal relationships between coexpressed genes. We first identify expression quantitative trait loci (eQTLs) with strong effects on gene expression. The algorithm then uses these eQTLs with strong effects and the expression of all genes to identify how genes are affecting each other. This is done by analysing coexpressed gene pairs where both genes have an eQTL and finding if the eQTL of one gene affects the expression of the other. Genes that were found to affect the expression of other genes were named “causal genes”. We evaluate our method by comparing its results with known causal genes and conclude that it is a good predictor of known interactions. Using this algorithm, we found 741 genes having causal effects on gene expression, many of which affected the gene expression of many other genes across the genome (2278 total affected genes). Some of the causal genes clustered at six hotspot regions in the genome. Genes in hotspot regions were found to have lower heritability than genes outside these regions. We hypothesize that hotspot regions may be enriched for essential and/or fitness related genes.

There is an emerging global epidemic of obesity and related complications, such as type 2diabetes (T2D). Alterations in body composition (adipose tissue, muscle volume and fatcontents) are known to be associated with an increased metabolic risk. Understanding of theunderlying mechanisms is key for development of novel intervention strategies. One study investigating the effect on body composition by different diets is Lipogain1. In this study, it was found that a small weight gain induced by polyunsaturated fats (PUFA, n=19) or saturated fats (SFA, n=20) had very different effects on body fat, liver fat and lean tissue mass respectively. The SFA group gained more liver fat and fat mass in general, while the PUFA group gained more muscle mass. These results were determined by magnetic resonance imaging.  The goal of this project was to visualize the results from Lipogain1 by utilizing the noveltechnique Imiomics. Imiomics is a method for statistical analysis of whole-body medical images. By utilizing image registration, all images are transformed to a common reference space. This enables point-wise comparisons between all images included in the analysis. In this project, mean images of the alterations in fat content and local volume change of the two groups were created. These were used to visualize the alterations in body composition from the study. Additionally, statistical tests were used to visualize statistically significant differences between the groups.  Differences between the groups could be seen in the mean images. Mainly a higher fat content increase was seen in SFA in comparison to PUFA. There was also a larger volume expansion in fat tissue in SFA than in PUFA, while PUFA instead had a larger volume expansion in muscles. An unexpected result was also found; the liver had expanded in PUFA but not in SFA. Unfortunately, few significant differences could be visualized between the groups when the statistical test was performed. The conclusion was that this method is promising for visualization of these kinds of studies, especially due to the potential of finding new, unexpected results. However, a somewhat larger cohort and possibly larger alterations in body composition might be needed to be able to visualize and quantify statistically significant differences between the groups on a voxel-wise level.

Clustering of single-cell RNA sequencing data is often used to show what states and subtypes cells have. Using this technique, striatal cells were clustered into subtypes using different clustering algorithms. Previously known subtypes were confirmed and new subtypes were found. One of them is a third medium spiny neuron subtype. Using the observed heterogeneity, as a second task, this project questions whether or not differences in individual neurons have an impact on the network dynamics. By clustering spiking activity from a neural network model, inconclusive results were found. Both algorithms indicating low heterogeneity, but by altering the quantity of a subtype between a low and high number, and clustering the network activity in each case, results indicate that there is an increase in the heterogeneity. This project shows a list of potential striatal subtypes and gives reasons to keep giving attention to biologically observed heterogeneity.

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.

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.

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.

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.