Discovering new viral lineages and estimating their abundance in wastewater
| dc.contributor.author | Ellmen, Isaac | |
| dc.date.accessioned | 2022-09-27T15:04:40Z | |
| dc.date.available | 2023-01-26T05:50:08Z | |
| dc.date.issued | 2022-09-27 | |
| dc.date.submitted | 2022-09-22 | |
| dc.description.abstract | Wastewater 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.identifier.uri | http://hdl.handle.net/10012/18818 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.relation.uri | https://github.com/Ellmen/alcov | en |
| dc.relation.uri | https://github.com/Ellmen/derived-wastewater-lineages | en |
| dc.subject | bioinformatics | en |
| dc.subject | sars-cov-2 | en |
| dc.subject | wastewater | en |
| dc.subject | sequencing | en |
| dc.title | Discovering new viral lineages and estimating their abundance in wastewater | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Science | en |
| uws-etd.degree.department | Biology | en |
| uws-etd.degree.discipline | Biology | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 4 months | en |
| uws.contributor.advisor | Charles, Trevor | |
| uws.contributor.advisor | Nissimov, Jozef | |
| uws.contributor.affiliation1 | Faculty of Science | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |