Genetic polymorphism in Plasmodium falciparum is a considerable obstacle to malaria intervention. Parasites have repeatedly evolved to overcome every front-line antimalarial deployed throughout history, and artemisinin resistant populations are expanding in Southeast Asia. Promising vaccine candidates routinely fail when challenged by the genetic diversity of natural parasite populations, and a recent trial using a blood-stage antigen showed immunity was allele specific. Modern sequencing technologies have revolutionized our understanding of parasite genomics and population genetics by providing access to single nucleotide variation, but characterizing more complex polymorphism remains a key challenge. Solving this problem is important because the selective pressures from drugs and host immunity often create complex polymorphism in the most clinically relevant genes that is missed using standard genotyping methods. In three sections, this thesis is a narrative about 1) encountering complex variation, 2) overcoming it with novel tools, and then 3) innovatively applying those tools to old and new questions. I first show examples of complex variation in a vaccine candidate (EBA-175) and a drug resistance gene (pfcrt) while reporting SNP based analyses of Kenyan and Tanzanian field isolates. While introducing this complex variation I also describe biological insights discovered in these populations. In Kenya I show evidence that chloroquine resistance selects for parasites that are primaquine sensitive, use a GWAS approach to discover new drug resistance loci, and catalogue variation in known resistance genes. In Tanzania I describe the population structure and allele frequencies of parasites from two geographic regions. In the second section of the thesis I develop methods for accessing complex variation and demonstrate their utility by producing de novo assemblies of eba-175, pfcrt, ama1, and msp3.4 from thousands of sequenced samples. Finally, in the third section I apply these tools in depth to eba-175. I comprehensively characterize the SNP and structural variation in eba-175 using an alignment of 1419 de novo assemblies. I use this resource to illustrate the profiles of positive selection across the gene, and corroborate these signals of balancing selection by showing the geographic distribution of the F/C indels and a lesser known 6bp indel positioned between the DBL domains. I then use the alignments to design Sequenom genotyping assays that facilitate a genome wide association study, testing for human associations with the eba-175 indels in the infecting parasite. I close by reporting a potential association on human chromosome 14 with the 6bp indel in eba-175.

La maladie de Huntington est une maladie neurodégénérative héréditaire qui est devenue un modèle d'étude pour comprendre la physiopathologie des maladies du cerveau associées à la production de protéines mal conformées et à la neurodégénérescence. Bien que plusieurs mécanismes aient été mis en avant pour cette maladie, dont plusieurs seraient aussi impliqués dans des pathologies plus fréquentes comme la maladie d’Alzheimer ou la maladie de Parkinson, nous ne savons toujours pas quels sont les mécanismes ou les profils moléculaires qui déterminent fondamentalement la dynamique des processus de dysfonction et de dégénérescence neuronale dans cette maladie. De même, nous ne savons toujours pas comment le cerveau peut résister aussi longtemps à la production de protéines mal conformées, ce qui suggère en fait que ces protéines ne présentent qu’une toxicité modérée ou que le cerveau dispose d'une capacité de compensation et de résilience considérable. L'hypothèse de mon travail de thèse est que l'intégration de données génomiques et transcriptomiques au travers des modèles qui récapitulent différentes phases biologiques de la maladie de Huntington peut permettre de répondre à ces questions. Dans cette optique, l'utilisation des réseaux de gènes et la mise en application de concepts issus de la théorie des graphes sont particulièrement bien adaptés à l'intégration de données hétérogènes, au travers des modèles et au travers des espèces. Les résultats de mon travail suggèrent que l'altération précoce (avant les symptômes, avant la mort cellulaire) et éventuellement dès le développement cérébral) des grandes voies de développement et de maintenance neuronale, puis la persistance voire l'aggravation de ces effets, sont à la base des processus physiopathologiques qui conduisent à la dysfonction puis à la mort neuronale. Ces résultats permettent aussi de prioriser des gènes et de générer des hypothèses fortes sur les cibles thérapeutiques les plus intéressantes à étudier d'un point de vue expérimental. En conclusion, mes recherches ont un impact à la fois fondamental et translationnel sur l'étude de la maladie de Huntington, permettant de dégager des méthodes d'analyse et des hypothèses qui pourraient avoir valeur thérapeutique pour les maladies neurodégénératives en général. / Huntington’s disease is a hereditary neurodegenerative disease that has become a model to understand physiopathological mechanisms associated to misfolded proteins that ocurs in brain diseases. Despite exciting findings that have uncover pathological mechanisms occurring in this disease and that might also be relevant to Alzheimer’s disease and Parkinson’s disease, we still do not know yet which are the mechanisms and molecular profiles that rule the dynamic of neurodegenerative processes in Huntington’s disease. Also, we do not understand clearly how the brain resist over such a long time to misfolded proteins, which suggest that the toxicity of these proteins is mild, and that the brain have exceptional compensation capacities. My work is based on the hypothesis that integration of ‘omics’ data from models that depicts various stages of the disease might be able to give us clues to answer these questions. Within this framework, the use of network biology and graph theory concepts seems particularly well suited to help us integrate heterogeneous data across models and species. So far, the outcome of my work suggest that early, pre-symptomatic alterations of signaling pathways and cellular maintenance processes, and persistency and worthening of these phenomenon are at the basis of physiopathological processes that lead to neuronal dysfunction and death. These results might allow to prioritize targets and formulate new hypotheses that are interesting to further study and test experimentally. To conclude, this work shall have a fundamental and translational impact to the field of Huntington’s disease, by pinpointing methods and hypotheses that could be valuable in a therapeutic perspective.

La maladie de Huntington est une maladie neurodégénérative héréditaire qui est devenue un modèle d'étude pour comprendre la physiopathologie des maladies du cerveau associées à la production de protéines mal conformées et à la neurodégénérescence. Bien que plusieurs mécanismes aient été mis en avant pour cette maladie, dont plusieurs seraient aussi impliqués dans des pathologies plus fréquentes comme la maladie d’Alzheimer ou la maladie de Parkinson, nous ne savons toujours pas quels sont les mécanismes ou les profils moléculaires qui déterminent fondamentalement la dynamique des processus de dysfonction et de dégénérescence neuronale dans cette maladie. De même, nous ne savons toujours pas comment le cerveau peut résister aussi longtemps à la production de protéines mal conformées, ce qui suggère en fait que ces protéines ne présentent qu’une toxicité modérée ou que le cerveau dispose d'une capacité de compensation et de résilience considérable. L'hypothèse de mon travail de thèse est que l'intégration de données génomiques et transcriptomiques au travers des modèles qui récapitulent différentes phases biologiques de la maladie de Huntington peut permettre de répondre à ces questions. Dans cette optique, l'utilisation des réseaux de gènes et la mise en application de concepts issus de la théorie des graphes sont particulièrement bien adaptés à l'intégration de données hétérogènes, au travers des modèles et au travers des espèces. Les résultats de mon travail suggèrent que l'altération précoce (avant les symptômes, avant la mort cellulaire) et éventuellement dès le développement cérébral) des grandes voies de développement et de maintenance neuronale, puis la persistance voire l'aggravation de ces effets, sont à la base des processus physiopathologiques qui conduisent à la dysfonction puis à la mort neuronale. Ces résultats permettent aussi de prioriser des gènes et de générer des hypothèses fortes sur les cibles thérapeutiques les plus intéressantes à étudier d'un point de vue expérimental. En conclusion, mes recherches ont un impact à la fois fondamental et translationnel sur l'étude de la maladie de Huntington, permettant de dégager des méthodes d'analyse et des hypothèses qui pourraient avoir valeur thérapeutique pour les maladies neurodégénératives en général. / Huntington’s disease is a hereditary neurodegenerative disease that has become a model to understand physiopathological mechanisms associated to misfolded proteins that ocurs in brain diseases. Despite exciting findings that have uncover pathological mechanisms occurring in this disease and that might also be relevant to Alzheimer’s disease and Parkinson’s disease, we still do not know yet which are the mechanisms and molecular profiles that rule the dynamic of neurodegenerative processes in Huntington’s disease. Also, we do not understand clearly how the brain resist over such a long time to misfolded proteins, which suggest that the toxicity of these proteins is mild, and that the brain have exceptional compensation capacities. My work is based on the hypothesis that integration of ‘omics’ data from models that depicts various stages of the disease might be able to give us clues to answer these questions. Within this framework, the use of network biology and graph theory concepts seems particularly well suited to help us integrate heterogeneous data across models and species. So far, the outcome of my work suggest that early, pre-symptomatic alterations of signaling pathways and cellular maintenance processes, and persistency and worthening of these phenomenon are at the basis of physiopathological processes that lead to neuronal dysfunction and death. These results might allow to prioritize targets and formulate new hypotheses that are interesting to further study and test experimentally. To conclude, this work shall have a fundamental and translational impact to the field of Huntington’s disease, by pinpointing methods and hypotheses that could be valuable in a therapeutic perspective.

This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.

In this thesis, I investigate the genetics of neuropsychiatric disorders by analysing large data sets derived from high-throughput experiments, using novel comparative genomics approaches. In the first project, I explore characteristics of rare, de novo copy number variants identified among autism patients by employing various bioinformatics resources including Mouse Genome Informatics phenotypes, Gene Ontology terms, and protein-protein interactions. I describe how I objectively identified a number of mouse model phenotypes that are significantly associated with autism, and that provide insight into the aetiologies for both copy number deletions and duplications. In the second project, I investigate the genetics of obsessive-compulsive disorder by resequencing genomic regions of human case-control cohorts and the best spontaneous disease model organisms, namely dogs with canine compulsive disorder, and breed-matched controls. Targeted sequencing experiments yielded a large number of high-quality genetic variants in both humans and dogs. I prioritised variants and genes using case- control comparisons and functional annotations such as types of mutation, evolutionary conservation status and regulatory marks. In turn, I generated several hypotheses that are experimentally tractable. Replication of these findings in a larger cohort is necessary, although it lies beyond the scope of this thesis. Results from both projects indicate that the analytical frameworks employed in this thesis could be profitably applied to other neuropsychiatric disorders.

Alcohol Dependence (AD) is a chronic substance use disorder with moderate heritability (60%). Linkage and genome-wide association studies (GWAS) have implicated a number of loci; however, the molecular mechanisms underlying AD are unclear. Advances in systems biology allow genome-wide expression data to be integrated with genetic data to detect expression quantitative trait loci (eQTL), polymorphisms that regulate gene expression levels, influence phenotypes and are significantly enriched among validated genetic signals for many commonly studied traits including AD. We integrated genome-wide mRNA and miRNA expression data with genotypic data from the nucleus accumbens (NAc), a major addiction-related brain region, of 36 subjects (18 AD cases, 18 matched controls). We applied weighted gene co-expression network analysis (WGCNA) to identify mRNA and miRNA gene co-expression modules significantly associated with AD. We identified six mRNA modules, two of which were downregulated in AD and were enriched for neuronal marker gene expression. The remaining four modules were upregulated in AD and enriched for astrocyte and microglial marker gene expressions. After performing gene set enrichment analysis (GSEA), we found that neuronal-specific modules enriched for oxidative phosphorylation, mitochondrial dysfunction and MAPK signaling pathways and glial-specific modules enriched for immune related processes, cell adhesion molecules and cell signaling pathways. WGCNA was also applied to miRNA data and identified two downregulated and one upregulated modules in AD. We intersected computationally predicted miRNA:mRNA interactions with miRNA and mRNA expression correlations to identify 481 significant (FDR<0.10) miRNA:mRNA targeting pairs. Over half (54%) of the mRNAs were targeted cooperatively by more than one miRNA suggesting a potentially important cellular mechanism relevant to AD. After integrating our expression and genetic data we identified 591 significant mRNA and 68 significant miRNA cis-eQTLs (<1 megabase) (FDR<0.10). After querying against GWAS data from the Colaborative Study on Genetics of Alcohol and Study of Addiction: Gentics and Environment, eQTLs for neuronatin (NNAT; rs1780705), proteosome subunit type 5 (PSMB5; rs10137082), long non-coding RNA (PKI55; rs13392737), adaptor related protein complex 1 sigma one subunit (AP1S1; rs12079545) and translocation associate membrane protein 1 (TRAM1; rs13277972) were associated with AD or alcohol related phenotypes at p<10-4.

O transtorno do espectro do autismo (TEA) é um transtorno do neurodesenvolvimento com apresentação clínica heterogênea. A nova classificação dimensional do DSM-5 permitiu a inclusão de toda a variabilidade fenotípica sob o mesmo guarda-chuva, criando a oportunidade de entender melhor os subgrupos de TEA de acordo com seus mecanismos fisiopatológicos heterogêneos. O objetivo deste estudo foi buscar componentes de vulnerabilidade a partir de fatores de risco (escolaridade materna, classe social, estresse e exposição tóxico ambiental durante a gestação, complicações na gravidez e história psiquiátrica familiar) e caracterizar subgrupos de TEA a partir destes componentes. Para evitar qualquer possível agrupamento baseado em parâmetros fenotípicos estabelecidos, como QI e gravidade, analisamos especificamente um grupo de pacientes homogêneos com TEA grave. A análise de componentes principais (PCA) foi realizada em dados de 68 crianças com TEA entre 3 e 7 anos de idade, e encontramos dois componentes principais: PC1, componente de vulnerabilidade genética e metabólica e PC2 componente de vulnerabilidade psicosocial. Com os escores do PCA, realizamos uma análise de clusters . Os resultados mostraram um cluster representando uma dimensão com maior vulnerabilidade genética, e outro com maior exposição a ambiente desfavorável e estressante durante a gestação. A análise de metiloma foi realizada para validar e explorar melhor a diferença entre os subgrupos. Encontramos 11.879 probes (p < 0.05) diferencialmente metiladas (DMPs). Os sítios CpG das DMPs estavam enriquecidos para regiões de metilação variáveis (VMRs). Sondas hipermetiladas apresentaram taxas mais altas nas características rVarBase associadas a SNPs funcionais, indicando maior risco de doença explicado por variações comuns (SNPs). A análise do módulo funcional dos promotores de genes encontrou diferenças relacionadas à resposta imune, processos metabólicos e estresse. A análise do relógio de metilação do DNA mostrou uma tendência de aumento do DNAm Age para ambos o clusters, mas sem diferença estatística. Por fim, a análise do exoma de 33 pacientes representantes dos dois clusters mostrou como esperado que ambos os subgrupos têm variantes raras deletérias, mas sem diferenças entre eles no número de variantes em genes intolerantes à variância de acordo com o escore RVIS. Nossos resultados mostram que estes grupos apresentam diferenças quanto aos componentes de vulnerabilidade uma relacionada com antecedentes genéticos hereditários comuns e, outra mais relacionada à resposta ambiental ao estresse. Este estudo corrobora que variações comuns e raras são importantes, mas influências ambientais devem ser consideradas para melhor encontrar subgrupos de TEA / Autism spectrum disorder (ASD) is a neurodevelopmental disorder with highly heterogeneous clinical presentation. The new dimensional DSM-5 classification allowed the inclusion of all phenotypic variability under the same umbrella, creating the opportunity to better understand ASD subgroups according to its heterogeneous pathophysiology mechanisms. The aim of this study was to search components of vulnerability from risk factors during gestation (mother schooling, social class, stress and environmental toxic exposition during gestation, pregnancy complications and familial psychiatric history) e characterize ASD subgroups from those components. To avoid any possible grouping based on phenotypic established parameters such as IQ and severity, we analysed a group of homogeneous patients with severe ASD specifically. Principal component analysis (PCA) was performed on data from 68 children with ASD between 3 and 7 years of age, and we found two principal components: PC1, component of genetic and metabolic vulnerability e PC2 component of unfavorable social environment vulnerability. With the PCA scores we performed clustering analysis. The results showed one cluster representing a dimension with stronger genetic vulnerability, and the other with more exposure to unfavorable and stressful environment during gestation. Methylome analysis has been performed to better explore subgroup difference. We found 11.879 (p < 0.05) differentially methylated probes (DMPs). CpG sites from those DMPs were found to be enriched in variable methylated regions (VMRs). The clusters have hypermethylated probes presented higher rates in different rVarBase regulatory regions associated to functional SNPs, indicating they may have different affected regulatory regions and more liability to disease explained by common variations (SNPs). Functional module analysis on gene promoters found differences related to immune response , metabolic processes and stress. DNA methylation clock analysis showed a tendency of higher DNAm Age for both Clusters, but here was no statistical DMAm Age acceleration difference. Lastly exome analysis of 33 patients representing both clusters showed as expected that both subgroups have deleterious rare variants, but without differences between them in the number of variants in genes intolerants to variance according to RVIS score. Our results show that this groups presents differences of vulnerability components, one related to common hereditary genetic antecedents, and another more related to the environmental response to stress. This study corroborates that common and rare variants are important, but environmental influences should be considered to better find subgroups of ASD

Phytoplankton are single celled organisms capable of phytosynthesis, and are present in all the major oceans and lakes in the world. Phytoplankton contribute to 50% of the total primary production on Earth, and are the dominating primary producer in most aquatic ecosystems. This thesis is based on the 1D deterministic model by Jäger et. al. (2010) which models phytoplankton dynamics in freshwater lakes, where phytoplankton growth is limited by the availability of light and phosphorus. The original model is here extended to two dimensions to include a horizontal dimension as well as a vertical dimension, in order to simulate phytoplankton dynamics under varying lake bottom topographies. The model was solved numerically using a grid transform and a finite volume method in MATLAB. Using the same parameter settings as the 1D case studied by Jäger et. al. (2010), an initial study of plankton dynamics was done by varying the horizontal and vertical diffusion coefficients independently.

Mutações nos genes envolvidos na embriogênese hipofisária já foram descritas relacionadas a quadros isolados de deficiência hormonal múltipla e/ou associado a fenótipos extra-pituitários. As mutações encontradas em humanos foram descritas em genes envolvidos na embriogênese hipofisária e cujos fenótipos foram gerados em animais a partir de nocaute, servindo de ponto de partida para sua busca em pacientes com fenótipo similar. Essa estratégia é conhecida como busca por gene candidato, e é feita pela técnica de sequenciamento tradicional Sanger. Na última década, com o avanço de novas tecnologias de sequenciamento, diversos genes foram associados ao hipopituitarismo, principalmente utilizando-se a metodologia de exoma. Contudo, ainda há uma grande parcela dessa população sem diagnóstico molecular, como evidenciado em um levantamento na literatura por Fang e colaboradores e cuja tendência foi observada no ambulatório de Endocrinologia do Desenvolvimento do Hospital das Clínicas, onde apenas 14% dos pacientes tiveram o seu diagnóstico molecular determinado. Com isso, as tecnologias de sequenciamento de última geração, passaram a ser uma ferramenta promissora para determinação molecular dos fenótipos dos pacientes. Logo, alguns pacientes em seguimento no ambulatório de endocrinologia do desenvolvimento tiveram o exoma sequenciado, e uma análise das métricas do sequenciamento evidenciou regiões de cobertura muito baixa, o que não permitiu a conclusão sobre a presença ou ausência de variantes nessas regiões. Entre essas regiões estão os genes SOX2 e SOX3, os quais possuem variantes conhecidas causadoras do fenótipo. Esse trabalho tem como objetivo analisar a cobertura dos genes envolvidos na embriogênese hipofisária assim como os relacionados ao hipopituitarismo congênito em quatro diferentes kits de preparação de biblioteca para exoma, a fim de identificar a melhor metodologia para um diagnóstico molecular dos pacientes alem determinar variantes especificas da população brasileira na região de interesse através de busca no site ABraOM. Foram analisados 76 genes em um total de 119 amostras separadas em três grupos, sendo o primeiro grupo de amostras HapMap, o segundo de um paciente com hipopituitarismo e sua mãe e o terceiro de amostras brasileiras aleatórias. Os kits utilizados foram NimbleGen (Roche), Nextera (Illumina), SureSelect e SureSelect+UTR (Agilent). Para isso, foram utilizados diversos programas de bioinformática, tendo entre eles o FASTQC, BWA, GATK, Annovar, Qualimap e bedTools. Análises da qualidade do sequenciamento, assim como a taxa de mapeamento e duplicação mostraram que as amostras utilizadas apresentavam qualidades adequadas e similares entre si para a análise. De acordo com os resultados obtidos em relação a cobertura, o kit da NimbleGen apresenta uma queda em sua cobertura dos genes de interesse em relação a sua capacidade de cobertura do exoma global, algo que pode ser devido à alta taxa de GC na região de interesse, uma vez que a capacidade do kit nessas regiões é deficiente em relação aos demais. Os genes com piores coberturas em todas as quatro tecnologias foram os genes HES5, que apesar de fazer parte da embriologia hipofisária, não possui variante relacionada ao fenótipo em humanos, e o SOX3 que, apesar de ter muita baixa cobertura na NimbleGen, é bem coberto na SureSelect. Isso corrobora com a análise de capacidade de cobertura em regiões com alta taxa de GC. Somado a isso observou-se que a população brasileira tem 885 variantes únicas e exclusivas. Concluímos, portanto, que o kit SureSelect, da Agilent, tem o melhor desempenho na região de interesse, assim como no exoma global, sendo o indicado para estudos em coortes de hipopituitarismo e a população brasileira possui variantes únicas inerentes a ela / Mutations in the genes involved in pituitary embryogenesis have been described related to isolated cases of multiple hormonal deficiency and/or associated with extra-pituitary phenotypes. Mutations found in humans were described in genes involved in pituitary embryogenesis by generating phenotypes knockout animals, serving as the starting point for their search in patients with similar phenotype. This strategy is known as gene candidate search and is performed by the traditional Sanger sequencing technique. In the last decade, with the advancement of new sequencing technologies, several genes have been associated with hypopituitarism, mainly using the exome methodology. However, there is still a large portion of this population without molecular diagnosis, as evidenced by a survey in the literature by Fang et al., This trend was also observed in the outpatient clinic of Developmental Endocrinology of Hospital das Clínicas, where only 14% of the patients had their molecular diagnosis. With this, high throughput sequencing technologies have become a promising tool for the molecular determination of patients\' phenotypes. Therefore, we sequenced the exome of some of our patients, and an analysis of the sequencing quality showed very low coverage regions, which harms the researcher\'s ability to reach a conclusion regarding presence or lack of variants in these regions. Among these regions are the SOX2 and SOX3 genes, which have many variants that are known to cause the phenotype. This work aims to analyze the coverage of the genes involved in pituitary embryogenesis as well as those related to congenital hypopituitarism in four different exome library preparation kits in order to identify the best methodology for a molecular diagnosis of the patients and to determine specific variants of the Brazilian population in the region of interest by searching the ABraOM website. A total of 76 genes were analyzed in a total of 119 samples in three groups: the first group of HapMap samples, the second of a patient with hypopituitarism and his mother, and the third group of random Brazilian samples. The kits used were NimbleGen (Roche), Nextera (Illumina), SureSelect and SureSelect + UTR (Agilent). For this, several bioinformatics programs were used, among them FASTQC, BWA, GATK, Annovar, Qualimap and bedTools. Sequencing quality analysis, as well as the mapping and duplication rate, showed that the samples used presented adequate and similar qualities for the comparison. According to the results obtained in relation to the coverage, the NimbleGen kit shows a drop in its coverage of the genes of interest in relation to its capacity to cover the global exome, something that may be due to the high GC rate in the region of interest, once the capacity of the kit in these regions is not as good as the others. The genes with the worst coverage in all four technologies were the HES5 gene, which despite being part of the pituitary embryology, have no phenotype-related variant in humans, and SOX3 which, despite having very low coverage in NimbleGen, is well covered on SureSelect. This corroborates the analysis of coverage capacity in regions with a high GC rate. In addition to this it was observed that the Brazilian population has 885 unique and exclusive variants. Therefore, we conclude that the Agilent\'s SureSelect kit has the best performance in the region of interest, as well as in the global exome, being recommended for studies in hypopituitarism cohorts, and that the Brazilian population has unique variants inherent to it

Hepatitis C infection is a global disease that causes an estimated 399,000 deaths per year. Treatment has improved dramatically in recent years through the development of direct acting antivirals that target specific regions of the Hepatitis C virus (HCV). Unfortunately the virus can have a preexisting resistance or become resistant to these drugs by mutations in the genes that code for the target proteins. These mutations are called resistance-associated substitutions (RASs). Since RASs can cause treatment failure for patients, resistance detection is performed in clinical practice to select the ideal regimen. Currently RASs are detected by using Sanger sequencing and a partly manual workflow that can discriminate the presence of a RAS if it is present in 15-20% of viruses in a patients blood. A new method with the capacity to detect lower ratios of RASs in HCV sequences was developed, which utilizes Pacific Biosciences’ (PacBio’s) sequencing and a bioinformatics analysis software called CLAMP. To validate this new approach, 123 HCV patient samples were sequenced with both methods and then analyzed. The RASs detected with the new method were congruent to what was found with the Sanger-based workflow. The new approach was also shown to correctly genotype the virus samples, identify any co-existing mutations on the same sequences, and detect if there were any mixed genotype infections in the samples. The new procedure was found to be a valid replacement for the Sanger based workflow, with the possibility to perform additional analyses and perform automated and time efficient RAS detection.