Computational approaches for improving treatment and prevention of viral infections

The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptor-hiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.Die Behandlung von HIV- oder HCV-Infektionen ist herausfordernd. Daher werden neue Wirkstoffe, sowie neue computerbasierte Verfahren benötigt, welche die Therapie verbessern. In dieser Arbeit wurden Methoden zur Unterstützung der Therapieauswahl entwickelt, aber auch solche, welche neuartige Therapien vorantreiben. geno2pheno[ngs-freq] bestimmt, ob Resistenzen gegen Medikamente vorliegen, indem es Hochdurchsatzsequenzierungsdaten von HIV-1 oder HCV Proben mittels Support Vector Machines oder einem regelbasierten Ansatz interpretiert. geno2pheno[coreceptor-hiv2] bestimmt den HIV-2 Korezeptorgebrauch dadurch, dass es einen Abschnitt des viralen Oberflächenproteins mit einer Support Vector Machine analysiert. openPrimeR kann optimale Kombinationen von Primern für die Multiplex-Polymerasekettenreaktion finden, indem es ein Mengenüberdeckungsproblem löst und auf ein neues logistisches Regressionsmodell für die Vorhersage von Amplifizierungsereignissen zurückgreift. geno2pheno[ngs-freq] und geno2pheno[coreceptor-hiv2] ermöglichen die Personalisierung antiviraler Therapien und unterstützen die klinische Entscheidungsfindung. Durch den Einsatz von openPrimeR auf humanen Immunoglobulinsequenzen konnten Primersätze generiert werden, welche die Isolierung von breit neutralisierenden Antikörpern gegen HIV-1 verbessern. Die in dieser Arbeit entwickelten Methoden leisten somit einen wichtigen Beitrag zur Verbesserung der Prävention und Therapie viraler Infektionskrankheiten

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

A phylogenetic method to perform genome-wide association studies in microbes

Genome-Wide Association Studies (GWAS) are designed to perform an unbiased search of genetic sequence data with the intent of identifying statistically significant associations with a phenotype or trait of interest. The application of GWAS methods to microbial organisms promises to improve the way we understand, manage, and treat infectious diseases. Yet, while microbial pathogens continue to undermine human health, wealth, and longevity, microbial GWAS methods remain unable to fully capitalise on the growing wealth of bacterial and viral genetic sequence data. Clonal population structure and homologous recombination in microbial organisms make it difficult for existing GWAS methods to achieve both the precision needed to reject false positive findings and the statistical power required to detect genuine associations between microbial genotypic and phenotypic variants. In this thesis, we investigate potential solutions to the most substantial methodological challenges in microbial GWAS, and we introduce a new phylogenetic GWAS approach that has been specifically designed for use in bacterial samples. In presenting our approach, we describe the features that render it robust to the confounding effects of both population structure and recombination, while maintaining high statistical power to detect associations. Our approach is applicable to organisms ranging from purely clonal to frequently recombining, to sequence data from both the core and accessory genome, and to binary, categorical, and continuous phenotypes. We also describe the efforts taken to make our method efficient, scalable, and accessible in its implementation within the open-source R package we have created, called treeWAS. Next, we apply our GWAS method to simulated datasets. We develop multiple frameworks for simulating genotypic and phenotypic data with control over relevant parameters. We then present the results of our simulation study, and we use thorough performance testing to demonstrate the power and specificity of our approach, as compared to the performance of alternative cluster-based and dimension-reduction methods. Our approach is then applied to three empirical datasets, from Neisseria gonorrhoeae and Neisseria meningitidis, where we identify core SNPs associated with binary drug resistance and continuous antibiotic minimum inhibitory concentration phenotypes, as well as both core SNP and accessory genome associations with invasive and commensal phenotypes. These applications illustrate the versatility and potential of our method, demonstrating in each case that our approach is capable of confirming known resistance- or virulence-associated loci and discovering novel associations. Our thesis concludes with a review of the previous chapters and an evaluation of the strengths and limitations displayed by the current implementation of our phylogenetic approach to association testing. We discuss key areas for further development, and we propose potential solutions to advance the development of microbial GWAS in future work.Open Acces