Over the course of this project, I demonstrate the utility of a 4-phase analysis pipeline in the context of cancer therapy and the associated search for antineoplastic drug candidates. I showcase a repeatable means for generating lists of potential targets which may be used in conjunction with methods like small molecule screening as part of a search for broadly effective antineoplastic agents.  By using publicly available variant call format (VCF) data sourced from the 1000 genomes project, global human population-wide data for non-sex chromosomes was filtered and transformed in a 4-phase process to obtain high population frequency, heterozygotic, nonsynonymous single nucleotide variants (nsSNVs) residing in functional domains of proteins. Through manual filtration combined with software-assisted annotation, I obtained a ranked list of 50 top scoring annotated variants across the human autosome, all residing in known protein domains. Additionally, a single top variant was selected for proof-of-concept structure prediction and visualization. When the methodology outlined herein is coupled to additional loss-of-heterozygosity (LOH) prevalence data across cancer genomes, it may be used to identify candidate variants which collectively represent potential loss-of-heterozygosity based collateral lethalities (CL) in the underlying cancer. Furthermore, under the assumption that subsequent methods like small molecule screening succeed in finding molecule(s) targeting a structural aspect of one of these variants, any subsequently developed therapeutic approaches may possess broader therapeutic utility dependent upon the strictness of the initial heterozygotic filtering threshold applied at the onset of the project pipeline. When combined with additional cancer data, the recreation of such gene lists at other degrees of heterozygotic thresholding can allow for the creation of lists of autosomal loss-of-heterozygosity gene candidates, representing potential collateral lethality targets with varied degrees of utility dependent upon the strictness of the initial filtration threshold.

Microbial fuel cell (MFC) technology is a renewable energy source that employs microorganisms as biocatalysts to degrade substrates into electrons and protons, and then transfer the electrons to the anode electrode. Electron transfer rates by microorganisms depend on many factors as well as on their diverse electron transfer mechanisms. The present study compared cytochromes, flavoproteins, electron transfer complexes, redoxins and other extracellular membrane proteins that have direct involvement in electron transfer mechanisms in Escherichia coli str. K-12 MG1655, Rhodopseudomonas pulastris DX-1 and Shewanella oneidensis MR-1. Escherichia coli str. The results showed that K-12 MG1655 had a more diverse range of extracellular proteins for electron transfer mechanisms compared to Rhodopseudomonas pulastris DX-1 and Shewanella oneidensis MR-1. Escherichia coli str. K-12 MG1655 expressed more flavoproteins, redoxin and electron transfer complex related proteins that had direct involvement in electron transfer mechanisms compared to two other bacterial species indicating that it may be able to transfer more electrons when employed in MFC technique. Escherichia coli str. K-12 MG1655 expressed 16 cytochromes, 9 flavoproteins, 6 redoxins, 6 electron transport complexes, 1 hypothetical and 1 oxidoreductase proteins. On the other hand, Rhodopseudomonas pulastris DX-1 and Shewanella oneidensis MR-1 expressed 26 and 35 cytochromes proteins. But these two bacterial species expressed less flavoproteins and redoxin related proteins and they didn’t express any electron transport complexes or hypothetical and oxidoreductase related proteins for electron transfer. STRING and SMART results suggested that the identified proteins transferred electrons either by connecting with other types of identified proteins in the constructed gene network or independently by taking part in oxidation-reduction reaction, metal ion reduction reaction or by their FMN binding activities.

This thesis presents the work done during my master degree projects under the supervision of Alexander Suh and Francisco J. Ruiz-Ruano. My work focused on the development of in-silico methods to improve the assembly of the Germline Restricted Chromosome (GRC) of songbirds, more specifically that of zebra finch.GRCs are a good example of the popular saying "The exception that proves the rule". For a very long time, it was assumed that every cell in a healthy multicellular organism carries the same genetic information. Cytogenetic evidence dating back as far as early XX century suggests that this is not always the case, as it has been documented that certain organisms carry supernumerary B chromosomes, which are dispensable chromosomes that are not part of the normal karyotype of a species. GRCs are often regarded as a special case of B chromosomes, where every individual from a species carries an additional chromosome whose presence is restricted to germline cells only. GRCs presence has been documented in insects, hagfishes and songbirds. A peculiar case of GRCs is that of zebra finch, whose GRC has an estimated size of over 150 Mb, accounting for over 10% of zebra finch total genome size. Despite the first cytogenetic evidence of zebra finch GRC dating back to 1998, it was only last year that the first comprehensive genomic study about this relatively large chromosome was published. This study shed some light on the gene content of the GRC in zebra finch, revealing that the GRC of zebra finch mostly consists of paralogs of A chromosomal genes. The GRC assembly and annotation that were published as part of this study included 115 GRC-linked genes that were identified through germline/soma read mapping, as well as 36 manually curated scaffolds with a median length of 3.6 kb. Considering the conspicuous size of the GRC of zebra finch, it is clear that this is a very fragmented and likely incomplete GRC assembly. There are many factors that can have a negative impact on assembly completeness and contiguity. In the GRC case, these factors collectively affect coverage in ways that are not properly handled by available genome assemblers. In the course of my master degree project I developed kFish, a bioinformatic software to perform alignment-free enrichment of GRC-linked barcodes from a 10x Genomics linked-read DNA Chromium library. kFish uses an iterative approach where the k-mer content of a set of GRC-linked sequences is compared with that of reads corresponding to each individual 10x Genomics barcode. This comparison allows kFish to identify likely GRC-linked barcodes, and then only use reads corresponding to these barcodes when trying to assemble the GRC. First benchmarking results generated using five GRC-linked genes from zebra finch as reference sequences, show that kFish is not only capable of assembling already known GRC-linked sequences, but also new ones with high confidence. kFish can do all of this in a matter of hours, using only few gigabytes of system memory, while previous efforts took over two days to assemble zebra finch genome and identify GRC-linked scaffolds using an approach based on read mapping. High quality genome assemblies and annotations are the foundations of modern genomics research, the lack of which greatly limits the breadth of the questions that can be answered. There is still a lot that we do not understand about GRCs, and part of this is due to the lack of high quality GRC assemblies and annotations. Producing such an assembly will likely require an integrated approach, where multiple sequencing technologies as well as bleeding edge bioinformatic tools such as kFish, are combined together to produce an high quality assembly, which will be crucial to unravel the mystery of GRCs function and evolutionary history.

Schizophrenia and bipolar disorder are two severe mental disorders that affect more than 65 million individuals worldwide. The aim of thisproject was to find co-prediction mechanisms for genes associated with schizophrenia and bipolar disorder using a multi-omics data set and a transparent machine learning approach. The overall purpose of theproject was to further understand the biological mechanisms of these complex disorders. In this work, publicly available multi-omics data collected from post-mortem brain tissue were used. The omics types included were gene expression, DNA methylation, and SNP array data. The data consisted of samples from individuals with schizophrenia, bipolar disorder, and healthy controls. Individuals with schizophrenia or bipolar disorder were considered as a combined CASE class. Using machine learning techniques, a multi-omics pipeline was developedto integrate these data in a manner such that all types were adequately represented. A feature selection was performed on methylation and SNP data, where the most important sites were estimated and mapped to their corresponding genes. Next, those genes were intersected with the gene expression data, and another feature selection was performed on the gene expression data. The most important genes were used to develop an interpretable rule-based model with an accuracy of 88%. The model wasthen visualized as a network. The graph highlighted genes that may be of biological importance, including CACNG8, RTN4, TERT, OSBPL8, and ANTXR1. Moreover, strong co-predictions were found, most notable between CNKSR4 and KDM4C in CASE samples. However, further investigations would need to be performed in order to prove that these are real biological interactions. Through the methods developed and the results found in this project, we hope to shed new light towards analyzing multi-omics data as well as to reveal more about the underlying mechanisms of psychiatric disorders.

During the last decade, the use of Next-Generation Sequencing(NGS) technologies has sky-rocketed. The vast amount of data produced by these platforms require processing and analysis. This is usually performed at locations remote from the sequencing facilities thereby introducing the need for data-transportation to the place of analysis. The use of internet transfer would greatly facilitate the process, however since NGS data is considered to be personal sensitive information the handling of the data is highly regulated by the General Data Protection Regulation(GDPR). During this project, a web-based application was developed for the privacy-protecting transfer of personal sensitive data, implementing an in-motion encryption scheme which ensures data integrity and authenticity. The application consists of three scripts: the HTML web page with JavaScript functionality, a PHP script responsible for connection establishment and integrity verification, and a Python script executing the majority of the server-side operations. The resulting application uses the symmetric encryption algorithm AES in GCM mode, using a key size of 128 bits and transfers 60 Kibibytes of the file at a time. The key is established by using the asymmetric RSA encryption scheme with a 4096 bit key pair. SHA-256 is used for verifying the integrity of the transferred files. The JavaScript encryption speed is 584 MB/s and the Python decryption speed 251 MB/s. While the focus of the project was to optimize the application for NGS data, it is not limited to this type of file and can transfer different formats, enabling the use in multiple different fields.

In the current environment of severe climate change, studying the adaptability of Norway spruce to the environment, that is, local adaptation is of great significance for helping to protect forest tree species and genetic breeding. As a structural variation, copy number variations (CNVs) have been proved to play an important role in shaping population structure and local adaptation in marine species, going beyond traditional studies focusing only on SNPs. Therefore, this experiment was to investigate the association of genotypes, including CNVs and SNPs, with local adaptation in Norway spruce. About 5.631% of CNVs were screened from SNPs, and the population structure of Norway spruce was detected based on the data of SNPs and CNVs. Then, the associations between genotypes (SNPs and CNVs) and the environmental variables are calculated by the model, considering the effects of population structure. Finally, the relationship between CNVs and SNPs and local adaptation of Norway spruce was investigated by redundancy analysis (RDA). The results preliminarily revealed that SNPs and CNVs had certain effects on the local adaptation of Norway spruce and a significant correlation with various environmental factors. However, the results indicated comparing to SNPs, CNVs had no significant effect on the local adaptation of Norway spruce.

The increasing use of plastics has elevated the risk of exposure to environmental pollutants such as plasticisers in the general population, making it necessary to understand the possible long term health consequences of the same. In this study we aim to understand how DEHP affects the gene expression in mice models and if it causes disruptions to its cellular pathways. Two datasets, GSE18564 and GSE14920 comprising of 15 and 60 samples respectively were downloaded from GEO database for analysis. Quality control checks were done using Principal Component Analysis and quantile normalisation. Differentially expressed genes were found using LIMMA model, following which only top 20 genes were selected for pathway analysis using KEGG and Gene Ontology. DEHP was found to be associated with chemical carcinogenesis, including negative regulation of extrinsic apoptotic signaling pathway and fatty acid metabolism. Furthermore, it seems likely that PPAR-alpha might play a key role in DEHP related metabolic disruption. Further studies are required to better elucidate the effect of DEHP on individual metabolic pathway implicated in this thesis.

This study investigates the analyses and clustering of Campylobacter spp., Listeria monocytogenes and Shiga toxin-producing Escherichia coli (STEC) at Livsmedelsverket. Livsmedelsverket is a control authority in Sweden. They work with eating habits, what food contains and safe food and good drinking water, where outbreak investigations of the above-mentioned bacterial types is a part of the work. For the investigations Livsmedelsverket uses a pipeline that is written in the programming language Python. The purpose of this project is to add identification of virulence genes and analysis of the STEC bacterium to the script. But also to develop the existing method to be able to cluster more isolates without losing information, enable the user to adjust parameters in the pipeline and write an ethical analysis to the work that is done. Our study shows the analysis and clustering of the three different types of bacteria, clustering of the samples from the analysis, both adaptively and statically, and that it can determine serotype, sequence type and virulence genes. We therefore conclude that STEC can be added to outbreak investigations at Livsmedelsverkets in-house pipeline. The clustering method has also been modified to be able to use more of the information given from the samples with the restriction of having lower accuracy.

Foodborne diseases are a growing health problem today and can be caused by eating food contaminated with bacteria. To monitor known foodborne diseases, institutions keep track of bacteria in surveillance projects. Whole genome sequencing is becoming the new standard method for comparing isolates, which generates large amounts of data. Today, the standard analyses are focused on conserved regions in genomes. The dynamics in less conserved regions can be studied by creating pan-genomes. A pan-genome consists of conserved genes, called core genes, and genes of varied conservation grade, called accessory genes. This thesis aimed to analyse pan-genomes of large datasets from six bacterial species coming from surveillance projects: Campylobacter coli, Campylobacter jejuni, Escherichia coli, Listeria monocytogenes, Salmonella enterica, and Streptococcus pneumoniae. The purpose was to investigate the species dynamics in the genomes and to look at properties of the genomes not included in the standard analyses that are used in surveillance projects today. Bacterial Pan Genome Analysis tool was used for the pan-genome analysis of the six species and datasets of 1,000-2,000 genomes per species were analysed. All species were estimated to have open pan-genomes, meaning the pan-genomes are increasing in size as more genomes are added. Escherichia coli and Salmonella enterica had more dynamic and open genomes compared to the other species. They had the highest number of accessory genes relative to their genome sizes and had the largest accessory segments between core genes. The synteny of the core genes showed high conservation for a part of the core genes in all species. Some core genes always sat directly after each other in the analysed genomes, never having accessory genes between them. Other core genes always had accessory genes between them, indicating very open regions in the genomes. The core genes were evenly distributed through the reference genomes with some regions showing increased gene density for all species. Some regions had a higher gene density for core genes often followed by core genes, and others for core genes often followed by accessory genes. However, the placement of genes needs to be investigated further with more reference genomes to be able to draw confident conclusions.

The non-coding space makes up around 98 % of the genome, but cancer-driving mutations have so far mostly been discovered in protein-coding regions. The majority of somatic non-coding mutations are neutral passenger mutations and identifying non-coding mutations with driving roles in cancer poses a challenge. In this work, evolutionary constraint was used to explore the non-coding space in human osteosarcoma to improve our understanding of how evolutionary constraint can be applied to identify non-coding driver mutations in cancer and describe the unknown role of non-coding mutations in osteosarcoma. Evolutionary constraint scores derived from an alignment of 33 mammals were used to extract non-coding mutations in functional elements from somatic variants of 38 osteosarcoma samples and genes with an enrichment of non-coding constraint mutations in their regulatory regions were identified. The investigation of those genes revealed that non-coding constraint mutations are likely involved in key osteosarcoma pathways. Furthermore, novel osteosarcoma genes and mechanisms were proposed based on the non-coding constraint mutation enrichment analysis. The regulatory potential of individual non-coding constraint mutations was evaluated based on regulatory annotations, functional evidence, transcription factor affinity predictions and electrophoretic mobility shift assays. We concluded that the analysis of non-coding constraint mutations is an efficient way to discover non-coding mutations with functional impact in osteosarcoma which likely play an important role in the disease.