Mycobacterium tuberculosis genomics: The Next Generation

Doctoral thesis (2021)

Authors

C.A. Anyansi Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2021 C.A. Anyansi
DOI: https://doi.org/10.4233/uuid:2276c5ba-1ec3-4ff7-b0fb-141175f4c76f
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Published Date

2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

This thesis represents one small step in the battle with TB, where we apply WGS approaches to explore different topics of TB research. Here we offer the global community a method to diagnose complex TB infections consisting of multiple distinct strains. We show that this functionality is necessary and has been overlooked by the TB community in research studies, which might have contributed to poor treatment outcomes. As the increase in samples resistant to multiple antibiotics is a pressing challenge, we explore global trends in antibiotic resistance evolution and have identified a particular order of resistance acquisition for 6 anti-tubercular drugs. This thesis also provides complete assemblies of 18 MTB genomes spanning 7 lineages that was used to analyze MTB’s largest and least studied gene family.