In this study, a novel method has been assessed to bridge the gap between bioinformatics and ecological conservation efforts to gain evidence to further base conservational plans on. Herein, the validity of using a provisional host metagenome-assembled metagenome to decontaminate the data from host contamination was concluded. To achieve this, 11 samples of increasing host contamination were devised by simulating reads from 100 genomes representing Platanthera bifolia and Platanthera chlorantha endophytic root microbiomes. By following the Critical Interpretation of Metagenome Interpretation benchmarking framework, the method was evaluated on assembly and binning performance. The study concluded strong negative correlations with host contamination that is derived by the lowered proportion of endophytic sequence depth at the higher host contamination levels. Furthermore, statistically significant difference between the control and the perfect GHOST-MAGNET was determined when accounting for the proportion of bins being endophytic.

The functional and anatomical characteristics of cancer cells vary among patients. Additionally, therapeutic approaches display varying responses in different individuals and cancer types due to the anatomical and functional complexity of tumor. Prognosis, and responsiveness to therapy depends on the tissue architecture of the tumor microenvironment (TME). TME cells, including immune cells, endothelial cells, stromal cells, and their subtypes, coexist with cancer cells. The cellular and spatial architecture of the TME show significant variation across and within individuals. There is an important correlation between cell function and its spatial organization in the tissue. To unravel this organization, typical clustering is applied on spatial omics data to find discrete clusters based on local cellular abundance. Alternatively, graph-based methods are used to define clusters of cells that are closest to each other, using community-detection methods. In order to better understand the rules governing the design, formation, and interactions of the TME, the Niche-Phenotype Mapping (NIPMAP) analysis pipeline, developed based on ideas from community ecology and machine learning, was reimplemented on a new and different type of data called Hyperplexed Immunofluorescence Imaging (HIFI) from mouse glioblastoma multiforme cancer (GBM) tissue sections. NIPMAP identified cellular niches and their interactions in this dataset. Niche abundance and their cell type composition were dynamic in response to ionizing radiation (IR) treatment and relapseHausser. Testing different numbers of archetypes, resulted in different optimal niche numbers for different condition groups. So, the optimal niche is not only specific to different tissue and cancer types but also to the treatment and other experimental conditions.

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The paper details the development, implementation and assessment of a suite of bioinformatics tools, namely Adapter Trimmer, Quality Trimmer, Quality Filter  and two Differential Expression Analysis (DEA) tools based on existing libraries like edgeR via rpy2 and PyDESeq2. All these tools are unified within a consolodated graphical user interface (GUI), underscoring the focus on accessibility and user-centric design. While prioritizing simplicity and user experience, the suite´s tools show limitation in their capabilities compared to established, more complex bioinformatics tools such as Cutadapt and Trimmomatic. The tools were designed with a lean functionality profile to adhere  to the project´s constraints, thus narrowing their versatility and adaptability to diverse data sets. However, these trade-offs enabled an accessible and user-friendly local execution platform. The platform distinguishes itself from web-based alternatives such as Galaxy by providing users with data privacy and the potential for faster processing times due to local execution. The study concludes by identifying opportunities for future research to address the limitations of the current suite. This includes the potential integration of more advanced data processing algorithms and the expansion of the toolset to cover a broader range of bioinformatics tasks such as alignment and assembly. Furthermore, a performance benchmarking framework is established to enable systematic comparison with other tools and to guide further refinement of the suit.

Lung cancer is the leading cause of cancer-related deaths worldwide and is divided into two broad histological types, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Network module-based approach is applied to lung cancer subtypes in order to analyze and compare the results with previous literature and thus discover new genetic biomarkers and/or confirm previously discovered ones. Data were extracted and analyzed in GEO2R, later protein-protein interaction (PPI)networks were generated through STRING. Functional modules and genesoverlapping between modules were identified using Cytoscape plugins MCODE and ModuLand, which were compared subsequently. The tools complement each other as MCODE can help visualize the neighbors of nodes identified by ModuLand while ModuLand can help identify significant genes as MCODE identifies all genes equally. Venny was used to analyze the overlapping genes between the subtypes and FunRichfor functional enrichment. The results were consistent with findings of previous literature. ModuLand highlighted nodes previously reported to have a role in various types of cancer including lung cancer, which involved two common proteins: CDK1and HIGD1B. The two functional networks showed clusters belonging to the mitoticsister chromatid segregation. Perhaps the main defective part in the cell cycle of lungcancer is chromatin-related. In conclusion by establishing functional modules and highlighting common genes between the modules for each subtype can shed light on potential mechanisms and further support previous discoveries. Several important genes have been identified at the centre of highly interconnected biological complexes that could serve as candidate biomarkers and hallmarks for future studies.

With the rise of NGS technologies, the transcriptomes of non-model organisms can be reconstructed even with the absence of a reference genome, using de novo assembly tools. There is a wide range of de novo assembly tools frequently being developed, however, there is a still a knowledge gap about the different effects and efficiency of different de novo assembly software types for RNA-seq assembly. This study aims to assemble the transcriptome of two different mussel species, Anodonta anatina and Margaritifera margaritifera, using three different types of genomic assemblers and to evaluate their distinct performances. Here, the transcriptomes have been assembled using whole-genome, single-cell and RNA-seq specific assemblers, and the results have been evaluated and compared using reference-free transcriptome evaluation tools. Whole-genome assemblers are not designed to handle variable transcript expressions and splice variations, and have thus achieved poor performance at assembling the transcriptomes. Single-cell assemblers, however, are designed to assemble genomes with uneven coverage, which make them able to handle variable transcript expressions and have therefore achieved good efficiency at assembling the transcriptomes. Single-cell assembler SPAdes has matched the performance of the well stablished RNA-seq assembler Trinity and the single-cell version of IBDA performed just as well as their RNA version. Overall, the top performing assembler in the study was the RNA version of SPAdes.

In 2020, cancer accounted for almost 20% of all deaths in the United States. Cancer is highly individual, and individualized treatments are essential in the battle against the disease. The tumor microenvironment is complex, and the cancer genome contains mutations driving the cancer. Identification and inference of mutations in the cancer genome are important for individualized diagnosis, prognosis, and treatment decisions. With NGS techniques, getting information about a tumor on the DNA level is possible. However, the data must be analyzed to reveal information from the NGS analysis. A tumor consists of both cancer and normal cells. When analyzing a tumor, DNA from cancer and normal cells is intermixed, and the information of which DNA comes from which cell is lost. The analysis is complicated since the fraction of cancer cells is unknown. Tumor purity is defined as the fraction of cancer cells in a tumor. Traditionally a pathologist decides the tumor purity by visually inspecting a tumor sample. As NGS techniques have developed, computational tools distinguishing between cancer and normal cells, including the fraction, have arisen. The purpose of this master’s thesis was to study how precise computational tools can estimate tumor purity using NGS data compared to a purity estimate made by a pathologist. To study the subject, a search was done for computational tools estimating tumor purity using NGS data. The software code had to be open, and the tools should focus on one tumor specimen from a patient, and papers using a normal sample from the patient were excluded. The search resulted in eight computational tools estimating tumor purity. Further, the two tools, ABSOLUTE and PureCN, were selected for comparison. An open access data set was used containing seven specimens. The data was filtered to imitate panel data targeting 250 genes. For some specimens, ABSOLUTE and PureCN performed consistent estimates with the pathologist’s estimates. However, for most specimens, the estimated purity by the tools was not in agreement with the ones made by the pathologist. PureCN performed more consistently with the pathologist estimates than ABSOLUTE, but it cannot be concluded with certainty. The study in this master’s thesis could not prove that the computational tools, ABSOLUTE and PureCN, are good enough at estimating tumor pu- rity on the imitated panel data to be used in the clinic. The study included data from only seven tumors. Therefore, significant conclusions could not be drawn from it.

Metagenomics studies the totality of genomes of all species in a microbial community. It is a young, growing field with medical, industrial, and ecological applications. Abundant metagenomic data is being produced today, but there is a lack of interpretation and visualization tools. The aim of this project was to create Nanometa Live: a user-friendly, real-time data processing pipeline and graphical user interface that enables visualization of the general species content in a sample, as well as detection of a set of predetermined pathogens. The pipeline was created using Snakemake, with classification by Kraken 2, and sequence validation by BLAST, with the input of the pipeline being fastq batch files from an Oxford Nanopore. The interface was coded in Python using the framework Dash, and utilizes the data produced by the pipeline to visualize results. A Sankey plot and a list of most abundant taxa displays the general species content, while a separate table and a gauge, colored to show the pathogenicity of the sample, displays the user-determined pathogens that the program looks for. Further exploration of the species composition is enabled by a sunburst plot and an icicle chart. Nanometa Live is a fully functioning prototype and can be considered on par with existing tools when it comes to analysis speed, computer requirements, and general user-friendliness. Its strengths are ease of interpretation and flexibility in visualizations, with weaknesses being lack of functionality, such as antibiotic resistance detection, and imperfections in code, structure and packaging.

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