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dc.contributor.authorEllmen, Isaac
dc.date.accessioned2022-09-27 15:04:40 (GMT)
dc.date.available2023-01-26 05:50:08 (GMT)
dc.date.issued2022-09-27
dc.date.submitted2022-09-22
dc.identifier.urihttp://hdl.handle.net/10012/18818
dc.description.abstractWastewater surveillance of SARS-CoV-2 has emerged as a critical tool for tracking the spread of COVID-19. In addition to estimating the relative case numbers using qPCR, SARS-CoV-2 genomic RNA can be extracted from wastewater and sequenced. The sequenced genomes provide information about which lineages, in particular which variants of concern (VOCs) are present in a community. Wastewater RNA sequencing data has two distinct challenges: First, the genomes are highly fragmented and the alignments often have poor genome coverage. Second, the samples are comprised of a mixture of genomes so mutations cannot be directly attributed to a single lineage. In this thesis, I explore methods to overcome these two challenges to extract useful information from the samples. First, I look at the problem of determining the relative abundance of VOCs. Most existing techniques only consider mutations which are unique to a particular VOC which massively reduces the amount of usable data. I introduce a new technique which extends mean and median frequencies over shared mutations in order to make use of the huge pool of shared mutations. Next, I investigate strategies for designing single-amplicon sequencing methods. I look at selecting single amplicons which are well-conserved and rich in information. I also design a single amplicon which is capable of amplifying multiple coronaviruses. I conclude the SARS-CoV-2 work by providing a technique which can identify novel lineages and sublineages from wastewater sequencing runs. Finally, I show that the techniques for analyzing SARS-CoV-2 in wastewater can also be applied to an important plant pathogen, the Tomato Brown Rugose Fruit Virus.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.relation.urihttps://github.com/Ellmen/alcoven
dc.relation.urihttps://github.com/Ellmen/derived-wastewater-lineagesen
dc.subjectbioinformaticsen
dc.subjectsars-cov-2en
dc.subjectwastewateren
dc.subjectsequencingen
dc.titleDiscovering new viral lineages and estimating their abundance in wastewateren
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentBiologyen
uws-etd.degree.disciplineBiologyen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Scienceen
uws-etd.embargo.terms4 monthsen
uws.contributor.advisorCharles, Trevor
uws.contributor.advisorNissimov, Jozef
uws.contributor.affiliation1Faculty of Scienceen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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