[EN] Our understanding of the biological mechanisms for most common human diseases is far from complete. Even with well established genetic landscapes, our capacity to make accurate phenotypical predictions or determine personalised disease risk using genetics alone is not possible for most diseases due to our lack of understanding of the mechanisms by which genetic alterations cause disease. Several suggestions have been proposed to explain this manifested lack of direct relation between genotype and phenotype, including interactions with other molecules, pleiotropy and environmental perturbations. Due to their essential role in carrying cellular functions, proteins and its interactions seem crucial to translate genomic data to phenotypic states. In this thesis I present three different and independent approaches to integrate human genomic data with prior knowledge in terms of protein-protein interactions (PPIs). The overall objective is, by making use of the interactome structure, to propose functional hypotheses that help to interpret the genetic variability observed in different human phenotypes. First I developed a methodology to extract the network component associated to any gene list ranked by any experimental parameter, as the one coming from case-control genome-wide associations studies. Second I performed a systematic analysis of human variants in the context of the protein interactome. There I study how the interactome structure can help us to explain the amount of apparently deleterious variation observed in actual populations and, therefore, give insight in its role in shaping the patterns of variability. Results are compared against somatic mutation found in Leukemia patients. Finally, I structurally resolved the protein interactome and used it to study how somatic mutations found in primary tumours distribute across the interacting interfaces and identify those with a potential role in driving oncogenesis. Although each chapter covers a different question, all of them demonstrate the potential of the interactome in helping to interpret genomic variation observed under diverse research scenarios. / [ES] Nuestro conocimiento acerca de los mecanismos biológicos causantes de la mayoría de enfermedades humanas comunes es aun pobre. Incluso con mapas genéticos de alta resolución, nuestra capacidad para hacer predicciones fenotípicas certeras o determinar el riesgo de una persona a padecer una enfermedad utilizando solamente marcadores genéticos es muy baja. Entre las principales causas de esta aparente falta de relación directa entre genotipo y fenotipo están las interacciones moleculares, los fenómenos de pleiotropía y la influencia de los factores externos. Debido al papel esencial que ejercen en llevar a cabo las funciones celulares, las proteínas y sus interacciones han adquirido una atención especial en la traducción de los datos genotípicos a estados fenotípicos. En esta tesis se presentan tres estrategias diferentes para la integración de datos genómicos humanos con la red de interacciones proteicas (interactoma). El objetivo común de todas ellas es, haciendo uso de la estructura del interactoma, proponer hipótesis funcionales que ayuden a interpretar los patrones de variabilidad observados en diferentes estados fenotípicos humanos. Primero, se propone una metodología para extraer el componente del interactoma asociado a los genes relevantes en una lista ranqueada por cualquier parámetro experimental, como el estadístico derivado de los estudios de asociación genómicos. Es segundo lugar se describe un análisis sistemático de las variantes genéticas observadas en humanos sanos en el contexto del interactoma. En él se estudia cómo la estructura del interactoma puede ayudar en explicar la aparentemente elevada cantidad de variantes deletéreas observadas en los últimos estudios poblacionales de secuenciación de genomas. Los resultados son comparados con las mutaciones somáticas observadas en pacientes de Leucemia. Finalmente, se presenta un estudio de las mutaciones somáticas observadas en tumores primarios utilizando una versión del interactoma que incluye la estructura tridimensional de las proteínas. Aunque cada estudio presentado en la tesis pretende resolver preguntas diferentes, todos ellos demuestran el potencial del interactoma de proteínas en ayudar a interpretar la variación genómica humana observada en un contexto tanto evolutivo como de enfermedad. / [CAT] El nostre coneixement sobre els mecanismes biològics causants de la majoria de malalties humanes comuns es encara pobre. Tot i que en l'actualitat tenim mapes genètics d'alta resolució, la nostra capacitat per a fer prediccions fenotípiques certeres utilitzant únicament marcadors genètics es encara molt baixa degut a que no entenem les bases moleculars a traves de les quals les alteracions genètiques condicionen un fenotip de malaltia. Entre les principals causes d'aquesta aparent falta de relació directa entre genotip i fenotip estan la complexitat introduïda per les interacciones moleculars, els fenòmens de peleiotropia i la influencia dels factors externs. Degut al paper clau en dur a terme la majoria de funcions cel·lulars, les proteïnes i les seues interaccions han adquirit una especial atenció en la traducció de les dades genotípiques en estats fenotípics. Aquesta tesi presenta tres estartègies diferents per a la integració de dades genòmiques humanes amb la xarxa d'interaccions proteiques (interactoma). L'objectiu comú es, fent ús de l'estructura del interactoma, proposar hipòtesis funcionals que ajuden a interpretar els patrons de variabilitat genètica observats en diferents estats fenotípics. En primer lloc, es proposa una metodologia per a extraure el component de l'interactoma associat als gens rellevants en una llista ranquejada per qualsevol paràmetre experimental, com l'estadístic derivat d'estudis d'assocaició de genoma. En segon lloc, es descriu un anàlisi sistemàtic de les variants genètiques observades en humans sans en el context del interactoma. Ací s'analitza com l'estructura del interactoma pot ajudar a explicar l'aparent elevada quantitat de variants deletèries observades en els últims estudis poblacionals de sequenciació de genomes. Els resultats son comparats amb les mutacions somàtiques observades en pacients de Leucèmia. Finalment, es presenta un estudi de les mutacions somàtiques observades en tumors primaris de més de 20 tipus utilitzant una versió del interactoma més resolutiva, que inclou l'estructura tridimensional de les proteïnes. Encara que cada estudi presentat en la tesi planteja resoldre qüestions diferents, tots ells demostren el potencial del interactoma de proteïnes en ajudar a interpretar la variació genòmica humana observada en un context tant poblacional com de malaltia. / García Alonso, LM. (2015). Functional profiling of human genomic data using the protein interactome [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/55848 / TESIS

Human Mendelian disease in Saudi Arabia is both significant and challenging. Next-generation sequencing (NGS) has resulted in important discoveries of the genetic variants responsible for inherited disease. However, the success of clinical genomics using NGS requires accurate and consistent identification of rare genome variants. Rarity is one very important criterion for pathogenicity. Here we describe a model to detect variants by analyzing allele frequencies of a Saudi population. This work will enhance the opportunity to improve variant calling workflow to gain robust frequency estimates in order to better detect rare and unusual variants which are frequently associated with inherited disease.

Eukaryotes organize their genomes by wrapping DNA around positively charged proteins called histones to form a structure known as chromatin. This structure is ideal for keeping the genome safe from damage, but also becomes an obstacle for the transcriptional machinery to access information stored in the DNA. To facilitate a balance between storage and accessibility, eukaryotes utilize a family of enzymes known as ATP-dependent chromatin remodelers to directly manipulate chromatin structure. The diverse activities of these chromatin remodeling enzymes range from simply sliding nucleosomes to reveal transcription start sites, to editing the composition of a nucleosome by exchanging canonical histones for histone variants. Chromatin remodeling enzymes recognize features of the nucleosome that activate their ATPase domains and enable proper remodeling function. One nuclear epitope that has been extensively studied is the nucleosomal acidic patch. This negatively charged region on the face of the nucleosome has been shown to be essential for remodeling enzymes like Chd1, ISWI, and INO80C. The chromatin remodeler SWR1C edits nucleosomes by removing the canonical histone H2A from nucleosomes and exchanges it for the histone variant H2A.Z, but the role of the acidic patch in this process has not been investigated. In this work, I showed that SWR1C has normal binding affinity to acidic patch mutant nucleosomes and retains ATPase stimulation but can no longer exchange dimers on this substrate. This work also identified a novel arginine anchor on the essential SWR1C subunit, Swc5, that binds specifically to the nucleosomal acidic patch. The data in this work suggest a mechanism where SWR1C engages nucleosomes and uses the Swc5 subunit to recognize the nucleosomal acidic patch to couple ATPase activity to histone dimer exchange.

Every day more than half of the world consumes rice as a primary dietary resource. Thus, rice is one of the most important food crops in the world. Rice and its wild relatives are part of the genus Oryza. Studying the genome structure, function, and evolution of Oryza species in a comparative genomics framework is a useful approach to provide a wealth of knowledge that can significantly improve valuable agronomic traits. The Oryza genus includes 27 species, with 11 different genome types as identified by genetic and cytogenetic analyses. Six genome types, including that of domesticated rice - O. sativa and O. glaberrima, are diploid, and the remaining 5 are tetraploids. Three of the tetraploid species contain the CCDD genome types (O. grandiglumis, O. latifolia, and O. alta), which arose less than 2 million years ago. Polyploidization is one of the major contributors to evolutionary divergence and can thereby lead to adaptation to new environmental niches. An important first step in the characterization of the polyploid Oryza species is the generation of a high-quality reference genome sequence. Unfortunately, up until recently, the generation of such an important and fundamental resource from polyploid species has been challenging, primarily due to their genome complexity and repetitive sequence content. In this project, I assembled two high-quality genomes assemblies for O. grandiglumis and O. latifolia using PacBio long-read sequencing technology and an assembly pipeline that employed 3 genome assemblers (i.e., Canu/2.0, Mecat2, and Flye/2.5) and multiple rounds of sequence polishing with both Arrow and Pilon/1.23. After the primary assembly, sequence contigs were arranged into pseudomolecules, and homeologous chromosomes were assigned to their respective genome types (i.e., CC or DD). Finally, the assemblies were extensively edited manually to close as many gaps as possible. Both assemblies were then analyzed for transposable element and structural variant content between species and homoeologous chromosomes. This enabled us to study the evolutionary divergence of those two genomes, and to explore the possibility of neo-domesticating either species in future research for my PhD dissertation.

Many companies use product families in order to offer product variants that appeal to different market segments while minimizing costs. Because the market demand is generally not uniform for all possible product variants, during the design phase a decision must be made as to which variants will be offered and how many. This thesis presents a new approach to solving this problem. The product is defined in terms of performance parameters. The market demand is captured in a preference model and applied to these parameters in order to represent the total potential market. The number and placement of the product variants are optimized in order to maximize percentage of the potential market that they span. This method is applied to a family of mountain bikes and a family of flow-regulating disks used in industrial applications. These examples show that usage of this method can result in a significant increase in potential market and a significant reduction in production costs.

Yes / Objectives: Resistance-associated variants (RAVs) in Rv0678, a regulator of the MmpS5-MmpL5 efflux pump, have been shown to lead to increased MICs of bedaquiline (2- to 8- fold) and clofazimine (2- to 4-fold). The prevalence of these Rv0678 RAVs in clinical isolates and their impact on treatment outcomes are important factors to take into account in bedaquiline treatment guidelines. Methods: Baseline isolates from two bedaquiline MDR-TB clinical trials were sequenced for Rv0678 RAVs and corresponding bedaquiline MICs were determined on 7H11 agar. Rv0678 RAVs were also investigated in non-MDRTB sequences of a population-based cohort. Results: Rv0678 RAVs were identified in 23/347 (6.3%) of MDR-TB baseline isolates. Surprisingly, bedaquiline MICs for these isolates were high (>0.24 mg/L, n¼8), normal (0.03 0.24 mg/L, n¼11) or low(<0.03 mg/L, n¼4). A variant at position 11 in the intergenic region mmpS5–Rv0678 was identified in 39 isolates (11.3%) and appeared to increase the susceptibility to bedaquiline. In non-MDR-TB isolates, the frequency of Rv0678 RAVs was lower (6/ 852 or 0.7%). Competition experiments suggested that rifampicin was not the drug selecting for Rv0678 RAVs. Conclusions: RAVs in Rv0678 occur more frequently in MDR-TB patients than previously anticipated, are not associated with prior use of bedaquiline or clofazimine, and in the majority of cases do not lead to bedaquiline MICs above the provisional breakpoint (0.24mg/L). Their origin remains unknown. Given the variety of RAVs in Rv0678 and their variable effects on the MIC, only phenotypic drug-susceptibility methods can currently be used to assess bedaquiline susceptibility. / This work was supported by Janssen Pharmaceutica. N. C. was supported by a Baekeland PhD scholarship from the Flemish Institute for Scientific Technology (IWT 130308, Belgium). C. J. M., L. R. and B. d. J. were supported by a European Research Council Starting Grant INTERRUPTB (311725).

The diagnosis of genetic illnesses has undergone a revolution with advancements in sequencing technology. Next-generation sequencing (NGS) has become a standard practice in genetic diagnostics, enabling the identification of various genetic variations. However, distinguishing causative variants from a vast number of benign background variants presents a significant challenge. This study focuses on improving the rank score model used in genetic rare-disease diagnostics at a clinical genomics facility in Stockholm. The objective is to develop a more effective and optimized model through the utilization of exploratory data analysis techniques and machine learning methods, investigating the strengths and weaknesses of various existing annotation scores to identify suitable features and enhance the model's classification performance. The research methodology involved analyzing publicly available ClinVar data, utilizing statistical methods such as principal component analysis (PCA), heatmap, Welch's t-test, and Chi-Square test to evaluate the correlation, patterns, and classification abilities of different variant types. In addition, the study employed a machine learning approach that combines allele frequency filtering and logistic regression trained on both public and in-house datasets to prioritize single nucleotide variants (SNVs) and insertions/deletions (InDels). The resulting model assigns binary class labels (benign or pathogenic) and provides scores for variant classification. Promising performance was observed in both the ClinVar dataset and the unique patient datasets, demonstrating the model's potential for clinical application. The findings of this study hold the potential to enhance genetic rare-disease diagnostics and contribute to advancements in rare disease research.

No description available.

Insertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. Despite this importance and being the second most abundant variant type in the human genome, indels have not been studied as much as the single nucleotide polymorphism (SNP). With the advance of next-generation sequencing technology, many indel calling tools have been developed. However, performance comparison of commonly used tools has shown that (1) the tools have limited power in identifying indels and there are significant number of indels undetected, and (2) there is significant disagreement among the indel sets produced by the tools. These findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results. Two indels are biologically equivalent if the resulting sequences are the same. Storing biologically equivalent indels as distinct entries in databases causes data redundancy and misleads downstream analysis. It is thus desirable to have a unified system for identifying and representing equivalent indels. This dissertation describes UPS-indel, a utility tool that creates a universal positioning system for indels so that equivalent indels can be uniquely determined by their coordinates in the new system. Results show that UPS-indel identifies more redundant indels than existing algorithms. While mapping short reads to the reference genome, a significant number of short reads are unmapped and excluded from downstream analyses, thereby causing information loss in the subsequent variant calling. This dissertation describes Genesis-indel, a computational pipeline that explores the unmapped reads to identify missing novel indels. Results analyzing sequence alignment of 30 breast cancer patients show that Genesis-indel identifies many novel indels that also show significant enrichment in oncogenes and tumor suppressor genes, demonstrating the importance of rescuing indels hidden in the unmapped reads in cancer and disease studies. Somatic mutations play a vital role in transforming healthy cells into cancer cells. Therefore, accurate identification of somatic mutations is essential. Many somatic mutations callers are available with different strengths and weaknesses. An ensemble approach integrating the power of the callers is warranted. This dissertation describes SomaticHunter, an ensemble of two callers, namely Platypus and VarDict. Results on synthetic tumor data show that for both SNPs and indels, SomaticHunter achieves recall comparable to the state-of-the-art somatic mutation callers and the highest precision, resulting in the highest F1 score. / Doctor of Philosophy / Insertion and deletion (indel), a common form of genetic variation in the human genome, is associated with genetic diseases and cancer. However, indels are heavily understudied due to experimental and computational challenges. This dissertation addresses the computational challenges in three aspects. First, the current approach of representing indels is ambiguous and causes significant database redundancy. A universal positioning system, UPS-indel, is proposed to represent equivalent indels unambiguously and the UPS-indel algorithm is theoretically proven to find all equivalent indels and is thus exhaustive. Second, a significant number of indels are hidden in DNA reads not mapped to the reference genome. Genesis-indel, a computational pipeline that explores the unmapped reads to identify novel indels that are initially missed, is developed. Genesis-indel has been shown to uncover indels that can be important genetic markers for breast cancer. Finally, mutations occurring in somatic cells play a vital role in transforming healthy cells into cancer cells. Therefore, accurate identification of somatic mutation is essential for a better understanding of cancer genomes. SomaticHunter, an ensemble of two sensitive variant callers, is developed. Simulated studies using whole genome and whole exome sequences have shown that SomaticHunter achieves recall comparable to state-of-the-art somatic mutation callers while delivering the highest precision and therefore resulting in the highest F1 score among all the callers compared.

INTRODUCTION : Les syndromes polymalformatifs constituent un large groupe de maladies génétiques dont l'hétérogénéité limite notre capacité à identifier le gène causal à l’aide des investigations conventionnelles. Le séquençage de l'exome en clinique offre une solution à cette limitation et est maintenant disponible en recherche ou dans quelques laboratoires cliniques aux États-Unis. L'utilisation systématique du séquençage de l'exome reste encore entravée par notre capacité à gérer les trouvailles accidentelles et à prédire efficacement le ou les changements causals à partir de plusieurs milliers de variants. Ainsi, pour faciliter l'analyse de l'exome et accélérer l'implémentation du séquençage de l'exome en clinique, nous avons développé, et récemment publié à ce sujet, un logiciel nommé PhenoVar. Celui-ci intègre les données génotypiques et phénotypiques, puis suggère à l’utilisateur une courte liste priorisée de diagnostics potentiels pour révision. Nous présentons ici les données préliminaires de la validation de PhenoVar chez des patients atteints de syndromes polymalformatifs indéterminés, en comparaison à l'analyse bio-informatique standard. MÉTHODE : Un total de 27 patients atteints de syndromes polymalformatifs d'étiologie génétique probable a été accepté pour le séquençage de l'exome. Un résultat normal a été obtenu pour chaque patient lors d’une analyse d'hybridation génomique comparative sur micropuce et reste sans diagnostic clair après le test de quelques gènes susceptibles par séquençage Sanger. À ce jour, nous avons réalisé le séquençage de 22 patients. Un médecin généticien a effectué l'analyse de ces patients utilisant PhenoVar, en parallèle à l'analyse standard réalisée par l'équipe de bio-informatique. RÉSULTATS : En moyenne, PhenoVar a réduit le nombre de diagnostics potentiels à réviser de façon manuelle à 20 par patient, en comparaison à 64 pour l'analyse bio- informatique conventionnelle. Nous avons obtenu un rendement diagnostique global de 50% (11/22) et de 45% avec PhenoVar. Neuf fois sur onze, le bon diagnostic s’est retrouvé dans les dix premiers diagnostics de la liste de PhenoVar. Nous avons aussi identifié une variation pathologique dans BRCA2, trouvaille accidentelle réalisée durant l’analyse conventionnelle et remise au patient avec son consentement. L’outil PhenoVar permet de masquer ce type de diagnostics sans lien avec la présentation clinique. La dépendance à l’égard des bases de données s’est avérée être une limite de notre approche. CONCLUSION : Nos résultats préliminaires suggèrent que le séquençage de l'exome combiné avec PhenoVar, en utilisant une approche axée sur le phénotype, conduit à un rendement diagnostique similaire à l'analyse bio-informatique standard et réduit le nombre de diagnostics à réviser. Comme il peut être utilisé directement par les médecins généticiens, ce logiciel pourrait faciliter l'utilisation de routine du séquençage de l'exome dans un cadre clinique. / BACKGROUND : Polymalformation syndromes consist in a large group of heterogeneous genetic disorders, for which our ability to identify the causative gene using conventional investigations remains limited. Exome sequencing offers a solution and is now available either on a research basis or in few USA clinical laboratories. Routine utilization of exome sequencing is still hindered by our capacity to manage accidental findings and to predict effectively the causative change(s) out of several thousands of variants. To facilitate exome analysis and accelerate implementation of exome sequencing in clinical practice, we have developed and recently published a software named PhenoVar. This software integrates the patient’s phenotype to the genotype data and suggests to the physician-user a short list of prioritized potential diagnoses for review. Here, we present the preliminary results of PhenoVar validation in patients affected with an undetermined polymalformation syndrome, in comparison to standard bioinformatics analysis. METHODS : A total of 27 patients with polymalformative syndromes of likely genetic etiology were accepted for exome sequencing. Each patient has a normal CGH array and remains without a clear diagnosis after Sanger sequencing-based gene tests. To date, we completed the sequencing of 22 patients. A medical geneticist performed the analysis on these patients using PhenoVar, in parallel of the standard analysis done by the bioinformatics team. RESULTS : On average, PhenoVar reduced the number of potential diagnoses for manual review to 20 per patient in comparison to 64, for standard bioinformatics analysis. We obtained a global diagnostic yield of 50% (11/22) and a yield of 45% with PhenoVar. Nine times out of eleven, the correct diagnosis was found in the top ten diagnoses of the PhenoVar’s list. We also identified a pathological variant in BRCA2, accidental finding made during the conventional analysis and given to the patient who provided consent. PhenoVar allows hiding such diagnoses unrelated to the clinical presentation. Dependency on central databases has proven to be a limitation of our approach. CONCLUSION : Our preliminary results suggests that exome sequencing combined with PhenoVar, using a phenotype-driven approach, led to a similar diagnostic yield than standard bioinformatics analysis and reduced the number of diagnoses to review. Since it can be used directly by medical geneticists, this software could facilitate routine utilization of exome sequencing in clinical practice.