As the field of next-generation sequencing (NGS) matures and the technology grows more advanced, it is becoming an increasingly strong tool for solving various biological problems. Harvesting and analysing the full genomic sequence of an individual and comparing it to a reference genome can unravel information about detrimental mutations, in particular ones that give rise to diseases such as cancer. At the Rudbeck Laboratory, Uppsala University, a fully automatic software pipeline for somatic mutational analysis of cancer patient sequence data is in development. This will increase the efficiency and accuracy of a process which today consists of several discrete computation steps. In turn, this will reduce the time to result and facilitate the process of making a diagnosis and delegate the optimal treatment for the patient. However, the genomic data of an individual is very sensitive and private, which demands that great security precautions are taken. Moreover, as more and more data are produced storage space is becoming increasingly valuable, which requires that data are handled and stored as efficiently as possible. In this project, I developed a Python pipeline for automatic compression and encryption of NGS alignment data, which aims to ensure full privacy protection of patient data while maintaining high computational and storage efficiency. The pipeline uses a state-of-the-art real-time compression algorithm combined with an Advanced Encryption Standard cipher. It offers security that meets rigorous modern standards, and performance which at least matches that of existing solutions. The system is made to be easily integrated in the somatic mutation analysis pipeline. This way, the data generated during the analysis, which are too large to be kept in operational memory, can safely be stored to disk.

Transcriptome reconstruction is an important component in the bioinformatical part of transcriptome studies. It is particulary interesting when a reference genome is missing, highly fragmented or incomplete, since in such situations, a simple alignment (or mapping) would not necessarily tell the full story. One species with such a highly fragmented reference genome is the Norway spruce (Picea abies (L.) H. Karst.) -- a conifer, which is very important for Swedish economy. Given its long juvenile phase and irregular cone setting, the demand of cultivated seeds are larger than the supply. This yields a desire to understand the transcriptomal biology behind the cone setting in P. abies. This thesis presents an introduction to this situation, and the biological and bioinformatical background in general, followed by two papers in which this is applied: Paper I introduces a novel de novo transcriptome assembler, with a focus on recovering isoforms, and paper II makes use of this assembler to be able to detect connections between scaffolds in the P. abies genome. Paper I also studies P. abies var acrocona, a mutant with shorter juvenile phase than the wild type, in order to detect how cone setting is initiated.  From differential expression studies of both mRNA and miRNA, a number of genes potentially involved in cone-setting in P. abies were found, and also a set of miRNAs that could be involved in their regulation. / Transkriptomrekonstruktion är en viktig komponent i den bioinformatiska delen av transkriptomstudier. Särskilt intressant är detta när ett referensgenom saknas, är kraftigt fragmenterat eller ofullständigt, ty i dessa situationer skulle inte en vanlig inpassning (eller mappning) kunna berätta allt. En art med ett kraftigt fragmenterat referensgenom är gran (Picea abies (L.) H. Karst.) -- ett barrträd, som är mycket viktigt för svensk ekonomi. På grund av dess långa uppväxtsfas och oregelbundna kottsättning, så är efterfrågan av förädlade fröer större än utbudet. Detta lämnar en önskan att förstå den transkriptomala biologin bakom granens kottsättning. Denna avhandling presenterar en introduktion till denna situation, den generella biologiska och bioinformatiska bakgrunden, följd av två artiklar i vilket detta är tillämpat: Artikel I introducerar en ny de novo transkriptomassembler med fokus på att återskapa isoformer, och artikel II tillämpar denna assembler för att kunna hitta länkar mellan scaffolder (genom-delar som hittills inte kunnat länkas med varandra) i grangenomet. Artikel II studerar även granmutanten acrocona (kottegran), vilken har kortare uppväxtsfas än vildtypen, för att kunna se vad som initierar kottsättning.  Från differentiella expressionsstudier av såväl mRNA som miRNA, hittades ett antal gener potentiellt involverade i granens kottsättning, samt några miRNA som kan vara involverade i dess reglering. / <p>QC 2021-02-12</p>

Background: Multiple sclerosis (MS) is a neurological, autoimmune disease which mainly affects people in the age of 20 to 40. The disease course is unpredictable affecting each patient differently, leading to progressiveand irreversible degradation of the central nervous system. There is no treatment that cures this disease, however, there are treatments that either slows down the disease course or prevents progressive disabilities. A treatment called autologous hematopoietic stem cell transplantation (AHSCT) is thought to reset the immune system and induce a new, more tolerant one, thus haltering the disease course. However, the knowledge about the effects causing the improvement seen in patients treated with AHSCT is limited. Methods: To investigate the effect of AHSCT in MS patients, serum lipidomics data from 16 patients was collected at ten timepoints. The lipidomics data was collected for both positively and negatively charged molecules separately as well as within a single experiment called polarity switching, using mass spectrometry. Since the standard method requires two separate experiments to analyze both positively and negatively charged lipids it requires twice the time and resources compared to polarity switching. Results: Comparing the two mass spectrometry protocols showed that the coefficient of variation (CV) was slightly higher for polarity switching compared to the standard method. Nevertheless, the difference was not significant and both methods had in general a good CV, indicating low technical variation. In addition, this thesis showed that polarity switching has a slightly higher percentage of lipids with zero carryover compared to the standard method. The results also indicated that the expression levels of differentially expressed lipids follow two distinct patterns throughout the AHSCT treatment. The largest intensity variation arises after stem cellreinfusion and the lipid intensities are back to nearly initial levels atthe three month follow-up. Finally, many lipids were found to be associated with the change in c-protein levels as well as erythrocyte, leukocyte, and thrombocyte levels that occurred during treatment. Conclusions: This master thesis showed that polarity switching is a good alternative to the standard method, saving both time and resources without losing too much in specificity. In addition, this thesis has shown that differentially expressed lipids follow two distinct expression patterns through the treatment. The lipids levels for both differentially expressed lipids and lipids associated with clinical data were nearly back to baseline levels three months after AHSCT. Hence, AHSCT has a major but short-lasting impact on the lipid levels in peripheral blood.

Actinorhizal plants are in symbiosis with the nitrogen-fixating soil bacterium Frankia, which forms nodules in the plant root. However, several studies also report other endophytic bacteria appearing in the nodules, but their function and interaction with the host plant or Frankia is not yet understood. This thesis used a bioinformatic approach to investigate the metagenomes of eighteen actinorhizal nodule samples to find out which bacteria are present, how the microbiomes differed from each other, and if the genomes of non-Frankia inhabitants could give indications of any functions. The results showed that the bacterial composition, richness, and diversity differed among the samples, especially between the samples sequenced from the field versus those primarily cultivated in a greenhouse. All samples had a substantial number of sequencing reads belonging to potential endophytes, such as strains of Enterobacteria, Pseudomonas, Streptomyces, Micromonospora, Mycobacteria and Pseudonocardia. There seemed to be a common microbial community shared among the plants on a family level, since no significant difference was found in the core microbiomes between the field and greenhouse groups. Some sequences found in the metagenomes were annotated as potential functions of the fellow travellers, such as antibiotic synthesis, proteins involved in regulating abiotic stresses, but also probable plant damaging compounds rather associated with pathogens than symbionts.

Metabolomics is a rising field combining bioinformatics and cheminformatics together. A major component of research is having a reliable data source, which usually comes in the form of metabolomic databases. This paper documents arising issues revolving categorizing metabolome compounds within databases, and a possible solution in the form of an R package that is capable of matching up various metabolome identifiers that originate from various metabolome databases. Then, by using this package we reflect on the average coverage of external reference between metabolome databases to highlight the lack of a universal compound primary identifier. / <p>The thesis presentation was held over Zoom due to the recent COVID19 pandemic.</p>

The molecular mechanism of complementary sex determination in the haplodiploid organisms is poorly understood and has only been described in the honeybee Apis mellifera. In the haplodiploid system, males develop from unfertilized eggs and females from fertilized eggs. However, in some rare cases, diploid males develop from fertilized eggs. They can be distinguished from diploid and haploid males at the molecular level since they are heterozygous like females but are homozygous, like haploid males, at the sex determination locus. In this project, Osmia bicornis was chosen as the model organism, and the aim is to identify the complementary sex determination locus which should be homozygous in all diploid males. Bee nests were collected from the bees' natural habitat, and potential diploid males were identified. Data analysis of whole-genome sequencing on 17 potential diploid males was performed, which identified 80 candidate sex determination loci with 259 genes. Homologs of the Csd gene in A. mellifera were identified but not found in any candidate complementary sex determination loci.

No description available.

Masspektrometri (MS) är en analysmetod som indikerar provers kemiska sammansättning. Provernas innehåll fragmenteras och joniseras, varefter jonernas förhållande mellan massa och laddning (m/z) och sammanlagda intensiteter mäts i form av ett masspektrum. Tandem-masspektrometri (MS/MS eller MS2) innebär att prover utsätts för MS i två omgångar, där den första resulterar i s.k precursor-joner med skilda m/z och den följande MS omgången analyserar masspektrum från varje precursor-jon. MS2 leder till masspektrum med hög upplösning vilket är användbart vid analys av komplexa prover. Proteinsammansättning i biologiska prover är ett exempel på en typ av vetenskapligt- och kliniskt viktig provtyp samt en mycket komplex sådan. Analysmetoder där MS2 används för sådan analys kallas shotgun-proteomik, vilka tar hänsyn till det extremt stora antalet möjliga peptider genom att använda specifika algoritmer för databehandling. Målet är att identifiera peptid-spektrum matchningar (PSMs), d.v.s att uppskatta vilka peptider som gett upphov till de observerade MS2-spektrumen. I detta syfte används sökmotorer som uppskattar vilka peptider som bäst matchar de precursor-jonerna som för varje MS-spektrum, och bibliotekssökning där MS2-spektrum jämförs med dokumenterade MS2-spektrum som härstammar från diverse peptider för att hitta bäst matchning. I detta projekt utnyttjas en nyligen utvecklad algoritm, en spektrumprediktor, för att skapa en workflow där peptiders masspektrum predikeras utifrån PSMs som hittats av en sökmotor. Därefter jämförs predikerade spektrum med de experimentella spektrumen som användes av sökmotorn, och likheten mellan paren av spektrum räknas ut. Projektet har som mål att kombinera fördelarna med sökmotorer och med bibliotekssökning genom att använda de uträknade likheterna mellan spektrum för att öka antalet PSMs som kan identifieras för den experimentella datan. Genom att använda PSM post-processorn Percolator så kan den uträknade likheten mellan par av spektrum leda till fler PSM-identifikationer då likheten implementeras som features i Percolator. Resultaten av detta visar att Percolator kan identifiera PSMs utifrån features baserade på likhet mellan par av spektrum, varav vissa har q-värden under 0.01 och vissa inte kunde identifieras då Percolator användes i kombination med sökmotorn Crux. Om metoden kan förbättras genom att öka den genomsnittliga likheten mellan par av spektrum, samt om fler mått på likhet implementeras, så kan metoden som beskrivs i projektet bidra till att öka antalet PSM-identifikationer utifrån sökmotorresultat. / Mass spectrometry (MS) is an analysis method revealing chemical composition of samples by fragmenting and ionizing the sample contents and measuring the mass-to-charge ratio (m/z) and cumulative intensity of each produced ion as an ion mass spectrum. Tandem mass spectrometry (MS/MS or MS2) uses two round of MS, the first to produce a set of precursor ions with distinct m/z, and then sequentially analyzing the ionization pattern of each precursor ion with a second round of MS. For complex samples, MS2 provides vastly increased ability to resolve the sample contents. Protein contents of biological samples represents both a critically important analysis target and a highly complex sample type. Analysis of such samples using MS2 is known as shotgun proteomics. The vast number of possible peptides in these samples necessitates the use of specialized algorithms when interpreting MS2 results data which aim to find peptide-spectrum matches (PSMs) between spectra and peptides identities. This includes search engines that predict which peptides best match each MS2 precursor ion, as well as library searching which match known peptide spectra to the MS2 spectral data. This project uses a recent advancement in shotgun proteomics, spectrum predictors, in a workflow that predicts peptide fragment spectra based on peptide identities suggested by a search engine, and calculates spectral similarity between the predicted peptide spectra and the experimental spectra which were assigned these peptides. This method aims to combine the strengths of both search engines and library searching, and to use the similarity score between experimental and predicted spectra to increase the number of spectra that can be confidently matched to a peptide identity. This project utilizes the PSM post-processor Percolator to rescore PSMs after introducing predicted spectrum similarity as a feature of the PSMs. The results indicate that the predicted spectrum similarity score is able to identify additional PSMs when used as a Percolator feature, when compared to the default Percolator PSM features. When using a combination of three similarity scores as a Percolator feature set, a number of PSMs are identified with q-values below 0.01 which were not identified by the corresponding Crux followed by Percolator workflow. If the average spectral similarity of predicted- and experimental spectra can be increased, and additional effective similarity scores can be added, this workflow could provide a useful tool for increasing PSM identifications from search engine results.

Satellite DNA (satDNA) is among the fastest evolving elements in the genome and is highly abundant in some eukaryotic genomes. Its highly repetitive nature means it is challenging to assemble, and thus underrepresented in most assemblies and often understudied as a result. Birds are an ideal model organism for the study of satDNA and its evolution, since the large amount of available sequenced genomes of this clade allows for dense sampling across various evolutionary timescales, and the low number of satDNA families within their satellitomes facilitates their study and comparison between species. Here, we characterize satDNA and its evolution across Passeriformes, an avian clade containing two-thirds of all bird species spanning ~50 million years of evolution. With this goal we use both short-read data and long-read assemblies of species representative of over 30 passerine families in this clade to shed light on the evolution of its satellitome. We focus on examining the phylogenetic relationships between satellites common to most species as well as characterizing satellite array structure and location in genome assemblies. We also analyse satellite abundance in each genome, focusing on differences in the satellite content between male and female individuals to look for satellites present in the female-specific W sex chromosome and the germline-restricted chromosome. Seven satDNA families shared by a quarter of the species were found, that were likely present in an ancestral species shared by most, if not all the species of Passeriformes. We observed that satDNA evolution is complex and does not follow species phylogeny and that satellite arrays generally have a simple head-to-tail conformation, with evidence in four of the sampled species of satDNA arrays with higher-order repeats. We also found two satDNA families with fairly consistent monomer length and conserved regions that we hypothesise to might be functional.

Kroniska leversjukdomar utgör en stor utmaning för sjukvårdssystemen som idag inte kan möta det ökande behovet av levertransplantationer hos patienter som befinner sig i sjukdomens slutskedet. Nationella screeningsprogram för att tidigt upptäcka leversjukdomar har visat sig svåra att införa på grund av specificitetsproblem i kombination med en tidig och ofta asymptomatisk sjukdomsbild vilket gör leversjukdomar svårupptäckta. Detta i kombination med stora risker och kostnader som är förknippade med diagnostiska och prognostiska standardmetoder, såsom leverbiopsi, har gjort det komplicerat att upptäcka sjukdomen tidigt. Utvecklingen av tillförlitliga och minimalt invasiva metoder bör därför prioriteras för att minska transplantationsbehovet av leversjukdomspatienter. Denna studie syftar till att bidra till detta arbete genom att undersöka optimala biomarkörer för leversjukdom genom robusta kvantifieringsmetoder med klinisk potential. I detta examensarbete används riktad masspektrometri och tunga rekombinanta proteinstandarder för att identifiera och absolutkvantifiera 108 proteiner i 2 μL plasma från 245 patienter med leversjukdom samt friska individer. Diagnoserna är spridda över olika stadier och patienterna har olika sjukdomsprogression. Genom differentierad expressionsanalys av proteinnivåerna och vägledd maskininlärning har biomarkörpaneler för stratifiering av leverfibros tagits fram. Detta som en del av The Human Disease Blood Atlas, vars målsättning är att utforska sjukdomsassocierade proteinprofiler och bygga en öppen databas tillgänglig för forskare världen över. Dessa resultat erbjuder lovande vägar för förbättrad diagnostik och personligt anpassade behandlingsstrategier inom leversjukdomshantering. Strategin är kompatibel med teknologier som underlättar biomarkörupptäckt och minimalt invasiv provtagning. / Advanced liver disease poses significant challenges to healthcare systems, which cannot meet the needs of liver transplantation for end-stage liver disease patients. This can be mitigated by the early detection of chronic conditions. However, chronic liver disease tends to be asymptomatic in its early stages and current diagnostic and prognostic gold standard methods, such as liver biopsy, can be expensive and risky, discouraging population-wide screenings. Thus, the development of reliable and minimally invasive methods should be a priority to enhance liver disease patient outcomes. This study aims to contribute to this effort by investigating optimal liver disease biomarkers through robust quantification methods with translational potential. Here, targeted mass spectrometry and heavy recombinant peptide isotopes (SIS-PrEST) are employed for the identification and absolute quantification of more than 100 proteins in small amounts of plasma from over 300 liver disease patients, whose conditions have different etiologies and stages of disease progression. Through differential expression analysis and supervised machine learning, potential biomarker panels for liver disease etiologies and fibrosis levels are identified as part of the Human Disease Blood Atlas’s effort to explore disease-specific protein profiles and build an open-source database accessible to scientists worldwide. These findings offer promising avenues for improved diagnostics and personalized treatment strategies in liver disease management and the strategy is compatible with technologies that facilitate biomarker discovery and minimally disruptive sampling.