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dc.contributor.advisorVannberg, Fredrik O.
dc.contributor.authorRavishankar, Shashidhar
dc.date.accessioned2020-01-14T14:45:29Z
dc.date.available2020-01-14T14:45:29Z
dc.date.created2019-12
dc.date.issued2019-09-03
dc.date.submittedDecember 2019
dc.identifier.urihttp://hdl.handle.net/1853/62281
dc.description.abstractThe goal of this thesis is to develop algorithms for the analysis of P. falciparum, P. brasilianum, and P. malariae. Malaria is endemic in many parts of the world, including regions of central Africa, South America, and South East Asia. There are five known species that cause malaria in humans: P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi. P. knowlesi is a zoonotic parasite restricted to mostly South East Asia.According to a World Health Organization (WHO) report from 2018, these five species were responsible for nearly 219 million infections, resulting in an estimated 435,000 deaths related to malaria in 2017. In this work, we highlight algorithms that can identify the similarity between Plasmodium species and detect drug-resistant P. falciparum parasites. The two specific aims in this work, describe two novel algorithms for genomic clustering and molecular surveillance from Next-generation Sequencing (NGS) data. First, we describe a consensus-based variant identification framework molecular surveillance of drug resistance in infectious disease. We highlight its utility by identifying mutations associated with drug resistance in malaria isolates. The scalability of the framework is highlighted by analyzing 8351 M. tuberculosis isolates for the genotypic prediction of drug resistance. In the second aim, we describe a k-mer based alignment-free algorithm for the estimation of similarity between isolates from raw NGS data. Using a weighted Jaccard distance, we describe an exact method for estimation of the distance between isolates from k-mer count data. The memory efficiency, scalability, and accuracy of the algorithm was demonstrated using in-silico datasets generated from genomes of 12 Plasmodium species, as well as real-world isolates from an outbreak of C. auris in Colombia. The improved accuracy and scalability offered by the methods described in this work can facilitate the use of NGS in public health.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectBioinformatics
dc.subjectVariant calling
dc.subjectConsensus variant calling
dc.subjectGenomic clustering
dc.subjectAlignment free algorithms
dc.subjectk-mer based
dc.subjectMolecular surveillance
dc.subjectMalaria
dc.subjectAnti-malarial drug resistance
dc.titleGenetic epidemiology algorithms for tracking drug resistance variants and genomic clustering of plasmodium species
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentBiology
thesis.degree.levelDoctoral
dc.contributor.committeeMemberJordan, I. King
dc.contributor.committeeMemberUdhayakumar, Venkatachalam
dc.contributor.committeeMemberVoit, Eberhard
dc.contributor.committeeMemberMcDonald, John
dc.date.updated2020-01-14T14:45:29Z


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