<p dir="ltr">This dissertation explores the integration of Quantum Information Science and Engineering (QISE) into formal K-12 curriculum through a Design-Based Research (DBR) approach. The overarching purpose is to develop a NGSS-aligned quantum-infused science curriculum unit for middle school students, aiming to enhance student understanding and engagement in quantum randomness. The study emphasizes the sequential introduction of concepts (from radioactive decay to quantum computing), interdisciplinary inquiry-based learning, and alignment of content and assessment strategies by leveraging Learning Progressions (LPs) and Hypothetical Learning Trajectories (LTs). Methods employed in this DBR study included iterative design processes, teacher feedback, and teaching experiments with 10 participant in-service middle school science teachers as well as quantitative assessment and evaluation of students’ learning and engagement data. Also, it is aimed to focus on professional development for teachers, incorporating NGSS and the Framework as the foundational guidelines. Findings highlighted the importance of teacher feedback in refining educational strategies, the challenges of teaching advanced quantum concepts at the middle school level, and the benefits of using classical physics as a gateway to introduce quantum concepts. This study is also manifestation of a structured teaching-learning pathway, guided by validation and hypothetical LPs, to support students' progression of understanding towards more sophisticated knowledge in QISE. Implications included the potential for enhancing coordination and sequencing of QISE teaching at the K-12 level, contributing to the cultivation of a diverse and quantum-savvy workforce. This DBR study hoped to set a foundation for future research endeavors, emphasizing the need for comprehensive teacher training in K-12 QISE education and the transformative power of education in fostering deeper comprehension and engagement with complex subjects.</p>

Tesis por compendio / [EN] The work done for this doctorate thesis focuses on error correction of Next Generation Sequencing (NGS) data in the context of High Performance Computing (HPC). Due to the reduction in sequencing cost, the increasing output of the sequencers and the advancements in the biological and medical sciences, the amount of NGS data has increased tremendously. Humans alone are not able to keep pace with this explosion of information, therefore computers must assist them to ease the handle of the deluge of information generated by the sequencing machines. Since NGS is no longer just a research topic (used in clinical routine to detect cancer mutations, for instance), requirements in performance and accuracy are more stringent. For sequencing to be useful outside research, the analysis software must work accurately and fast. This is where HPC comes into play. NGS processing tools should leverage the full potential of multi-core and even distributed computing, as those platforms are extensively available. Moreover, as the performance of the individual core has hit a barrier, current computing tendencies focus on adding more cores and explicitly split the computation to take advantage of them. This thesis starts with a deep analysis of all these problems in a general and comprehensive way (to reach out to a very wide audience), in the form of an exhaustive and objective review of the NGS error correction field. We dedicate a chapter to this topic to introduce the reader gradually and gently into the world of sequencing. It presents real problems and applications of NGS that demonstrate the impact this technology has on science. The review results in the following conclusions: the need of understanding of the specificities of NGS data samples (given the high variety of technologies and features) and the need of flexible, efficient and accurate tools for error correction as a preliminary step of any NGS postprocessing. As a result of the explosion of NGS data, we introduce MuffinInfo. It is a piece of software capable of extracting information from the raw data produced by the sequencer to help the user understand the data. MuffinInfo uses HTML5, therefore it runs in almost any software and hardware environment. It supports custom statistics to mould itself to specific requirements. MuffinInfo can reload the results of a run which are stored in JSON format for easier integration with third party applications. Finally, our application uses threads to perform the calculations, to load the data from the disk and to handle the UI. In continuation to our research and as a result of the single core performance limitation, we leverage the power of multi-core computers to develop a new error correction tool. The error correction of the NGS data is normally the first step of any analysis targeting NGS. As we conclude from the review performed within the frame of this thesis, many projects in different real-life applications have opted for this step before further analysis. In this sense, we propose MuffinEC, a multi-technology (Illumina, Roche 454, Ion Torrent and PacBio -experimental), any-type-of-error handling (mismatches, deletions insertions and unknown values) corrector. It surpasses other similar software by providing higher accuracy (demonstrated by three type of tests) and using less computational resources. It follows a multi-steps approach that starts by grouping all the reads using a k-mers based metric. Next, it employs the powerful Smith-Waterman algorithm to refine the groups and generate Multiple Sequence Alignments (MSAs). These MSAs are corrected by taking each column and looking for the correct base, determined by a user-adjustable percentage. This manuscript is structured in chapters based on material that has been previously published in prestigious journals indexed by the Journal of Citation Reports (on outstanding positions) and relevant congresses. / [ES] El trabajo realizado en el marco de esta tesis doctoral se centra en la corrección de errores en datos provenientes de técnicas NGS utilizando técnicas de computación intensiva. Debido a la reducción de costes y el incremento en las prestaciones de los secuenciadores, la cantidad de datos disponibles en NGS se ha incrementado notablemente. La utilización de computadores en el análisis de estas muestras se hace imprescindible para poder dar respuesta a la avalancha de información generada por estas técnicas. El uso de NGS transciende la investigación con numerosos ejemplos de uso clínico y agronómico, por lo que aparecen nuevas necesidades en cuanto al tiempo de proceso y la fiabilidad de los resultados. Para maximizar su aplicabilidad clínica, las técnicas de proceso de datos de NGS deben acelerarse y producir datos más precisos. En este contexto es en el que las técnicas de comptuación intensiva juegan un papel relevante. En la actualidad, es común disponer de computadores con varios núcleos de proceso e incluso utilizar múltiples computadores mediante técnicas de computación paralela distribuida. Las tendencias actuales hacia arquitecturas con un mayor número de núcleos ponen de manifiesto que es ésta una aproximación relevante. Esta tesis comienza con un análisis de los problemas fundamentales del proceso de datos en NGS de forma general y adaptado para su comprensión por una amplia audiencia, a través de una exhaustiva revisión del estado del arte en la corrección de datos de NGS. Esta revisión introduce gradualmente al lector en las técnicas de secuenciación masiva, presentando problemas y aplicaciones reales de las técnicas de NGS, destacando el impacto de esta tecnología en ciencia. De este estudio se concluyen dos ideas principales: La necesidad de analizar de forma adecuada las características de los datos de NGS, atendiendo a la enorme variedad intrínseca que tienen las diferentes técnicas de NGS; y la necesidad de disponer de una herramienta versátil, eficiente y precisa para la corrección de errores. En el contexto del análisis de datos, la tesis presenta MuffinInfo. La herramienta MuffinInfo es una aplicación software implementada mediante HTML5. MuffinInfo obtiene información relevante de datos crudos de NGS para favorecer el entendimiento de sus características y la aplicación de técnicas de corrección de errores, soportando además la extensión mediante funciones que implementen estadísticos definidos por el usuario. MuffinInfo almacena los resultados del proceso en ficheros JSON. Al usar HTML5, MuffinInfo puede funcionar en casi cualquier entorno hardware y software. La herramienta está implementada aprovechando múltiples hilos de ejecución por la gestión del interfaz. La segunda conclusión del análisis del estado del arte nos lleva a la oportunidad de aplicar de forma extensiva técnicas de computación de altas prestaciones en la corrección de errores para desarrollar una herramienta que soporte múltiples tecnologías (Illumina, Roche 454, Ion Torrent y experimentalmente PacBio). La herramienta propuesta (MuffinEC), soporta diferentes tipos de errores (sustituciones, indels y valores desconocidos). MuffinEC supera los resultados obtenidos por las herramientas existentes en este ámbito. Ofrece una mejor tasa de corrección, en un tiempo muy inferior y utilizando menos recursos, lo que facilita además su aplicación en muestras de mayor tamaño en computadores convencionales. MuffinEC utiliza una aproximación basada en etapas multiples. Primero agrupa todas las secuencias utilizando la métrica de los k-mers. En segundo lugar realiza un refinamiento de los grupos mediante el alineamiento con Smith-Waterman, generando contigs. Estos contigs resultan de la corrección por columnas de atendiendo a la frecuencia individual de cada base. La tesis se estructura por capítulos cuya base ha sido previamente publicada en revistas indexadas en posiciones dest / [CA] El treball realitzat en el marc d'aquesta tesi doctoral se centra en la correcció d'errors en dades provinents de tècniques de NGS utilitzant tècniques de computació intensiva. A causa de la reducció de costos i l'increment en les prestacions dels seqüenciadors, la quantitat de dades disponibles a NGS s'ha incrementat notablement. La utilització de computadors en l'anàlisi d'aquestes mostres es fa imprescindible per poder donar resposta a l'allau d'informació generada per aquestes tècniques. L'ús de NGS transcendeix la investigació amb nombrosos exemples d'ús clínic i agronòmic, per la qual cosa apareixen noves necessitats quant al temps de procés i la fiabilitat dels resultats. Per a maximitzar la seua aplicabilitat clínica, les tècniques de procés de dades de NGS han d'accelerar-se i produir dades més precises. En este context és en el que les tècniques de comptuación intensiva juguen un paper rellevant. En l'actualitat, és comú disposar de computadors amb diversos nuclis de procés i inclús utilitzar múltiples computadors per mitjà de tècniques de computació paral·lela distribuïda. Les tendències actuals cap a arquitectures amb un nombre més gran de nuclis posen de manifest que és esta una aproximació rellevant. Aquesta tesi comença amb una anàlisi dels problemes fonamentals del procés de dades en NGS de forma general i adaptat per a la seua comprensió per una àmplia audiència, a través d'una exhaustiva revisió de l'estat de l'art en la correcció de dades de NGS. Esta revisió introduïx gradualment al lector en les tècniques de seqüenciació massiva, presentant problemes i aplicacions reals de les tècniques de NGS, destacant l'impacte d'esta tecnologia en ciència. D'este estudi es conclouen dos idees principals: La necessitat d'analitzar de forma adequada les característiques de les dades de NGS, atenent a l'enorme varietat intrínseca que tenen les diferents tècniques de NGS; i la necessitat de disposar d'una ferramenta versàtil, eficient i precisa per a la correcció d'errors. En el context de l'anàlisi de dades, la tesi presenta MuffinInfo. La ferramenta MuffinInfo és una aplicació programari implementada per mitjà de HTML5. MuffinInfo obté informació rellevant de dades crues de NGS per a afavorir l'enteniment de les seues característiques i l'aplicació de tècniques de correcció d'errors, suportant a més l'extensió per mitjà de funcions que implementen estadístics definits per l'usuari. MuffinInfo emmagatzema els resultats del procés en fitxers JSON. A l'usar HTML5, MuffinInfo pot funcionar en gairebé qualsevol entorn maquinari i programari. La ferramenta està implementada aprofitant múltiples fils d'execució per la gestió de l'interfície. La segona conclusió de l'anàlisi de l'estat de l'art ens porta a l'oportunitat d'aplicar de forma extensiva tècniques de computació d'altes prestacions en la correcció d'errors per a desenrotllar una ferramenta que suport múltiples tecnologies (Illumina, Roche 454, Ió Torrent i experimentalment PacBio). La ferramenta proposada (MuffinEC), suporta diferents tipus d'errors (substitucions, indels i valors desconeguts). MuffinEC supera els resultats obtinguts per les ferramentes existents en este àmbit. Oferix una millor taxa de correcció, en un temps molt inferior i utilitzant menys recursos, la qual cosa facilita a més la seua aplicació en mostres més gran en computadors convencionals. MuffinEC utilitza una aproximació basada en etapes multiples. Primer agrupa totes les seqüències utilitzant la mètrica dels k-mers. En segon lloc realitza un refinament dels grups per mitjà de l'alineament amb Smith-Waterman, generant contigs. Estos contigs resulten de la correcció per columnes d'atenent a la freqüència individual de cada base. La tesi s'estructura per capítols la base de la qual ha sigut prèviament publicada en revistes indexades en posicions destacades de l'índex del Journal of Citation Repor / Alic, AS. (2016). Improved Error Correction of NGS Data [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67630 / Compendio

Epigenetic modifications, such as DNA methylation and histone modifications, play important roles in gene expression and regulation, and are highly involved in cellular processes such as stem cell pluripotency/differentiation and tumorigenesis. Chromatin immunoprecipitation (ChIP) is the technique of choice for examining in vivo DNA-protein interactions and has been a great tool for studying epigenetic mechanisms. However, conventional ChIP assays require millions of cells for tests and are not practical for examination of samples from lab animals and patients. Automated microfluidic chips offer the advantage to handle small sample sizes and facilitate rapid reaction. They also eliminate cumbersome manual handling. In this report, I will talk about three different projects that utilized microfluidic immunoprecipitation followed by next genereation sequencing technologies to enable low input and high through epigenomics profiling. First, I examined RNA polymerase II transcriptional regulation with microfluidic chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) assays. Second, I probed the temporal dynamics in the DNA methylome during cancer development using a transgenic mouse model with microfluidic methylated DNA immunoprecipitation followed by next generation sequencing (MeDIP-seq) assays. Third, I explored negative enrichment of circulating tumor cells (CTCs) followed by microfluidic ChIP-seq technology for studying temporal dynamic histone modification (H3K4me3) of patient-derived tumor xenograft on an immunodeficient mouse model during the course of cancer metastasis. In the first study, I adapted microfluidic ChIP-seq devices to achieve ultrahigh sensitivity to study Pol2 transcriptional regulation from scarce cell samples. I dramatically increased the assay sensitivity to an unprecedented level (~50 K cells for pol2 ChIP-seq). Importantly, this is three orders of magnitude more sensitive than the prevailing pol2 ChIP-seq assays. I showed that MNase digestion provided better ChIP-seq signal than sonication, and two-steps fixation with MNase digestion provided the best ChIP-seq quality followed by one-step fixation with MNase digestion, and lastly, no fixation with MNase digestion. In the second study, I probed dynamic epigenomic changes during tumorigenesis using mice often require profiling epigenomes using a tiny quantity of tissue samples. Conventional epigenomic tests do not support such analysis due to the large amount of materials required by these assays. In this study, I developed an ultrasensitive microfluidics-based methylated DNA immunoprecipitation followed by next-generation sequencing (MeDIP-seq) technology for profiling methylomes using as little as 0.5 ng DNA (or ~100 cells) with 1.5 h on-chip process for immunoprecipitation. This technology enabled me to examine genome-wide DNA methylation in a C3(1)/SV40 T-antigen transgenic mouse model during different stages of mammary cancer development. Using this data, I identified differentially methylated regions and their associated genes in different periods of cancer development. Interestingly, the results showed that methylomic features are dynamic and change with tumor developmental stage. In the last study, I developed a negative enrichment of CTCs followed by ultrasensitive microfluidic ChIP-seq technology for profiling histone modification (H3K4Me3) of CTCs to resolve the technical challenges associated with CTC isolation and difficulties related with tools for profiling whole genome histone modification on tiny cell samples. / Ph. D. / The human genome has been sequenced and completed over a decade ago. The information provided by the genomic map inspired numerous studies on genetic variations and their roles in diseases. However, genomic information alone is not always sufficient to explain important biological processes. Gene activation and expression are not only associated with alteration in the DNA sequence, but also affected by other changes to DNA and histones. Epigenetics refers to the molecular mechanisms that affect gene expression and phenotypes without involving changes in the DNA sequence. For example, the DNA can get methylated, the histone protein that is wrapped around by DNA can also get methylated or acetylatied, and transcription factors can bind to different part of DNA. All of these can affect gene expression without alter the DNA sequences. Epigenetic changes occur throughout all stages of cell development or in response to environmental cues. They change transcription patterns in a tissue/cell-specific fashion. For example, transcriptional silencing of tumor-suppressor genes by DNA methylation plays an important role in cancer development. Therefore, understanding of epigenetic regulations will help to improve various aspects of biomedicine. For instance, personalized medicine can be vi tailored based on epigenetic profile of certain patient to specifically control gene expression in the disease treatment. However, the technology for profiling epigenetic modifications, i.e. Chromatin Immunoprecipitation (ChIP), suffers from serious limitations. The key limitation is the sensitivity of the assay. Conventional assay requires a large number of cells (>10⁶ cells per ChIP). This is feasible when using cell lines. However, such requirement has become a major challenge when primary cells are used because very limited amounts of samples can be generated from lab animals or patients. Population heterogeneity information may also be lost when a large cell number is used. In this project, we developed an automated ultrasensitive microfluidic chromatin/DNA immunoprecipitation followed by next-generation sequencing (ChIP/MeDIP-Seq) technology for profiling epigenetic modifications (e.g., histone modifications, transcriptional regulations, and DNA methylation). We extensively optimized design parameters for each and every step of ChIP/MeDIP (e.g. sonication/crosslinking time, antibody concentration, washing conditions) in order to reach highest sensitivity of 0.1 ng DNA (or ~50-100 cells) as starting material for IP, which is roughly 4-5 orders of magnitude higher than the prevailing protocol and 2-3 orders of magnitude higher than the-state-of-the-art(~50 ng). With such sensitivity, we were able to study temporal dynamics in the DNA methylomes during the various stages of mammary cancer development from a transgenic mouse mode. We were able to investigate transcriptional regulation of RNA polymerase II from scarce cell samples. We were also able to study histone modification (H3K4Me3) of circulating tumor cells during cancer metastasis.

RNA-Seq is an increasingly popular approach to transcriptome profiling that uses the capabilities of next generation sequencing technologies and provides better measurement of levels of transcripts and their isoforms. In this thesis, we apply RNA-Seq protocol and transcriptome quantification to estimate gene expression and pathway activity levels. We present a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. In the first version of IsoDE, we compared the tool against four existing methods: Fisher's exact test, GFOLD, edgeR and Cuffdiff on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. We also introduce the second version of IsoDE which runs 10 times faster than the first implementation due to some in-memory processing applied to the underlying gene expression frequencies estimation tool and we also perform more optimization on the analysis. The second part of this thesis presents a set of tools to differentially analyze metabolic pathways from RNA-Seq data. Metabolic pathways are series of chemical reactions occurring within a cell. We focus on two main problems in metabolic pathways differential analysis, namely, differential analysis of their inferred activity level and of their estimated abundance. We validate our approaches through differential expression analysis at the transcripts and genes levels and also through real-time quantitative PCR experiments. In part Four, we present the different packages created or updated in the course of this study. We conclude with our future work plans for further improving IsoDE 2.0.

International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / New tracking/control system requirements demand that the present DFCS/GRDCUS/MTACS data link bit rate be increased. A possible method for achieving this increase is to select two additional 31-bit chip code patterns that are orthogonal to the present chip codes, and to each other. This method will not require any more bandwidth than the present 10 MHZ used. This method suggest that each of the four chip code patterns are assigned a two bit value ie: 00, 01, 10, 11. At present, the two correlated chip codes represent data in a pulse position method. No data is contained in which of the two chip codes actually correlated. This new method suggest each of the four chip code patterns will still perform the pulse position modulation and provide two additional bits of data. These additional two bits of data will up the data rate of the link by 100 percent.

The aim of the current thesis was the analysis of the genetics of sex determination of farmed fish with sexual dimorphism, using Next Generation Sequencing. Three different species of farmed fish with sex-determining systems of varying complexity were studied. Both full-sibs and more distantly related specimens of Atlantic halibut (Hippoglossus hippoglossus), Nile tilapia (Oreochromis niloticus) and European sea bass (Dicentrarchus labrax) were used for this study. Application of Restriction-site Associated DNA sequencing (RAD-seq) and double digest Restriction-site Associated DNA sequencing (ddRAD-seq), two related techniques based on next generation sequencing, allowed the identification of thousands of Single Nucleotide Polymorphisms (SNPs; &gt; 3,000) for each of the above species. The first SNP-based genetic maps for the above species were constructed during the current study. The first evidence concerning the location of the sex-determining region of Atlantic halibut is provided in this study. In the case of Nile tilapia both novel sex-determining regions and fine mapping of the major sex-determining region are presented. In the study of European sea bass evidence concerning the absence of a major sex-determining gene was provided. Indications of putative sex-determining regions in this species are also provided. The results of the current thesis help to broaden current knowledge concerning sex determination in three important farmed fish. In addition the results of the current thesis have practical applications as well, towards the production of mono-sex stocks of those species for the aquaculture industry.

Récemment, une nouvelle classe d'ADN circulaire extrachromosomique (eccDNA) appelée microADN a été identifiée dans des tissus humains et murins. Ces microADNs ont une longueur de 100 à 400 pb, sont dérivés de régions génomiques non répétitives uniques et présentent un enrichissement au niveau des régions géniques et riches en GC. Bien qu'il ait été proposé qu'ils puissent provenir du métabolisme de l'ARN ou des défauts de réplication, leurs mécanismes de production et leur éventuelle fonctionnalité restent à déterminer. Grâce à l'analyse des microADNs extraits d'une série de 10 lignées cellulaires lymphoblastoïdes humaines (LCL), nous avons confirmé la distribution nonaléatoire des microADNs vers les régions actives du génome. Les microADNs identifiés présentaient des loci d'origine redondants et une périodicité de taille de 190 pb pouvant correspondre à la fragmentation de l'ADN lors de l'apoptose caspase-dépendante. L'apoptose induite de ces LCLs par des drogues chimiothérapeutiques (méthotrexate ou L-asparaginase) a entrainé la modulation de la diversité et de la taille des microADNs, suggérant qu'une partie de ces entités pourrait être des produits résiduels de la mort cellulaire apoptotique. Ainsi, bien que compatible avec l'observation initiale suggérant que les microADNs proviennent d'un processus physiologique normal, ces résultats impliquent une source de production alternative ou complémentaire. / Recently, a new class of extrachromosomal circular DNA (eccDNA) called microDNA was identified in mouse and human tissues. These microDNAs are 100 to 400 bp long, derive from unique nonrepetitive genomic regions and show an enrichment in GC rich and genic sequences. While it has been proposed that they could arise from RNA metabolism or replication defects, their production mechanisms and eventual functionality remain unclear. Through the analysis of microDNAs extracted from a series of 10 human lymphoblastoid cell lines (LCLs), we confirmed the non-random distribution of microDNA towards active regions of the genome. Identified microDNAs showed redundant loci of origin and a size periodicity of 190 bp that matched caspase-dependant DNA fragmentation of apoptotic cells. Strikingly, the chemotherapeutic drug-induced apoptosis (using methotrexate or Lasparaginase) of these LCLs modulated both diversity and size of microDNAs further suggesting that a part of microDNAs could represent circularized by-products of the programmed cell death. Thus, while compatible with the original observation that microDNAs originated from a normal physiological process, these results imply an alternative or complementary source of production.

Background: Lysosomal storage diseases (LSD) often manifest with cherry red macular spots. Diagnosis is based on clinical features and specific biochemical and enzymatic patterns. In uncertain cases, genetic testing with next generation sequencing can establish a diagnosis, especially in milder or atypical phenotypes. We report on the diagnostic work-up in a boy with sialidosis type I, presenting initially with marked cherry red macular spots but non-specific urinary oligosaccharide patterns and unusually mild excretion of bound sialic acid. Methods: Biochemical, enzymatic and genetic tests were performed in the patient. The clinical and electrophysiological data was reviewed and a genotype-phenotype analysis was performed. In addition a systematic literature review was carried out. Case report and results: Cherry red macular spotswere first noted at 6 years of age after routine screening myopia. Physical examination, psychometric testing, laboratory investigations aswell as cerebralMRIwere unremarkable at 9 years of age. So far no clinical myoclonic seizures occurred, but EEG displays generalized epileptic discharges and visual evoked potentials are prolonged bilaterally. Urine thin layer chromatography showed an oligosaccharide pattern compatible with different LSD including sialidosis, galactosialidosis, GM1 gangliosidosis or mucopolysaccharidosis type IV B. Urinary bound sialic acid excretion was mildly elevated in spontaneous and 24 h urine samples. In cultured fibroblasts, α-sialidase activity was markedly decreased to b1%; however, bound and free sialic acid were within normal range. Diagnosis was eventually established by multigene panel next generation sequencing of genes associated to LSD, identifying two novel, compound heterozygous variants in NEU1 gene (c.699CNA, p.S233R in exon 4 and c.803ANG; p.Y268C in Exon 5 in NEU1 transcriptNM_000434.3), leading to amino acid changes predicted to impair protein function. Discussion: Sialidosis should be suspected in patients with cherry red macular spots, even with non-significant urinary sialic acid excretion. Multigene panel next generation sequencing can establish a definite diagnosis, allowing for counseling of the patient and family.

<p>Our primary goal is to better understand complex diseases using statistically disciplined approaches. As multi-modal data is streaming out of consortium projects like Genotype-Tissue Expression (GTEx) project, which aims at collecting samples from various tissue sites in order to understand tissue-specific gene regulation, new approaches are needed that can efficiently model groups of data with minimal loss of power. For example, GTEx project delivers RNA-Seq, Microarray gene expression and genotype data (SNP Arrays) from a vast number of tissues in a given individual subject. In order to analyze this type of multi-level (hierarchical) multi-modal data, we proposed a series of efficient-score based tests or score tests and leveraged groups of tissues or gene isoforms in order map genomic biomarkers. We model group-specific variability as a random effect within a mixed effects model framework. In one instance, we proposed a score-test based approach to map expression quantitative trait loci (eQTL) across multiple-tissues. In order to do that we jointly model all the tissues and make use of all the information available to maximize the power of eQTL mapping and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants. In the second instance, we showed the flexibility of our model framework by expanding it to include tissue-specific epigenetic data (DNA methylation) and map eQTL by leveraging both tissues and methylation. Finally, we also showed that our methods are applicable on different data type such as whole transcriptome expression data, which is designed to analyze genomic events such alternative gene splicing. In order to accomplish this, we proposed two different models that exploit gene expression data of all available gene-isoforms within a gene to map biomarkers of interest (either genes or gene-sets) in paired early-stage breast tumor samples before and after treatment with external beam radiation. Our efficient score-based approaches have very distinct advantages. They have a computational edge over existing methods because they do not need parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Also, the efficient score is the locally most powerful test and is guaranteed a theoretical optimality over all other approaches in a neighborhood of the null hypothesis. This theoretical performance is born out in extensive simulation studies which show that our approaches consistently outperform existing methods both in statistical power and computational speed. We applied our methods to publicly available datasets. It is important to note that all of our methods also accommodate the analysis of next-generation sequencing data.</p> / Dissertation

Characterising and understanding factors that affect the rate of molecular evolution in proteins has played a major part in the development of evolutionary theory. The early analyses of amino acid substitutions stimulated the development of the neutral theory of molecular evolution, which later evolved into the nearly neutral theory. More recent work has lead to a better understanding of the role selection plays at the molecular level, but there is still limited understanding of how higher levels of protein organisation affect the way natural selection acts. The investigation of this question is the central aim of this thesis, which is addressed via the analysis of selective pressures in secondary protein structures in insects, mammals and fungi. The analyses for the first two groups were conducted using publically available datasets. To conduct the analyses in fungi, genome sequence data from the fungal genus Microbotryum (sequenced in our laboratory) was assembled and annotated, resulting in the development of a number of bioinformatics tools which are described here. The fungal, insect and mammalian datasets were interrogated with regard to a number of structural features, such as protein secondary structure, position of a site with regard to adaptively evolving sites, hydropathy and solvent-accessibility. These features were correlated with the signals of positive and purifying selection detected using phylogenetic maximum likelihood and Bayesian approaches. I conclude that all of the factors examined can have an effect on the rate of molecular evolution. In particular, disordered and hydrophilic regions of the protein are found to experience fewer physiochemical constraints and contain a higher proportion of adaptively evolving sites. It is also revealed that positively selected residues are ‘clustered’ together spatially, and these trends persist in the three taxa. Finally, I show that this variation in adaptive evolution is a result of both selective events and physiochemical constraint.