Metagenomics is a culture-independent framework for deciphering the complexity of biological communities, often with a focus on microbial communities in a specific environment. The applicability of this approach is widespread due to the ubiquity and presence of unculturable microbes in many environments which can only be investigated using culture-independent methods. With advances in DNA sequencing and the introduction of high-throughput sequencing technologies, studying microbial life as communities has become more accessible. However, the breadth of data generated dictates that computational processing steps must be in place to analyze the data. Due to the large diversity in computational and bioinformatic steps possible for metagenomic data, differences in methods of analysis can lead to discordant interpretations of results. The performance of different metagenomics methods must therefore be assessed to establish the effect on the interpretation of results. Taxonomic classification is an integral step in metagenomic analysis and many tools exist for this purpose. To determine which tools are better suited for particular types of metagenomic data, a comparative analysis of performance was conducted for numerous tools. The findings suggest that hybrid programs may have the best performance and warrant further investigation. Programs such as CLARK, KRAKEN, and MEGAN also performed well and are suitable for metagenomic analysis. Utilizing these methods, investigation into the bacterial populations of four freshwater beaches was examined. Bacterial communities in beach waters and sands were more distinct in terms of taxonomic composition than communities in different lakes. Functional capacity was stable between beach habitats, although enrichment of anaerobic and stress related genes in the sand suggests that this is a relatively harsh environment. The detection of sequences belonging to pathogens in the sands of these beaches also has implications for public health and warrants changes in water quality monitoring procedures. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20286 |
| Date | 11 1900 |
| Creators | Salama, Yasser |
| Contributors | Golding, Brian, Biology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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