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Large-scale clustering of antigen receptor gene sequence data using hyper-dimensional point packing

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dc.contributor.advisor Keller, Stefan Chang, Haiyang 2018-08-20T12:46:58Z 2018-08-20T12:46:58Z 2018-07-20 2018-08-10 2018-08-20
dc.description.abstract Lymphocytes generate abundant antigen receptor (AR) genes to recognize an almost infinite number of epitopes. One challenge is to group AR sequences based on the recognition of a common epitope. Traditional clustering methods are based on hierarchical clustering, which comes at a significant computational cost due to pairwise genetic distance comparisons. In this thesis, a point packing strategy was applied to incrementally break down the data into subsets, which limits pairwise sequence comparison to the final cluster level. Sub-setting was achieved by picking maximally spaced anchor sequences from a dataset, iteratively, and assigning the remaining sequences to the closest anchor. This results in an inverted tree with anchor sequences as nodes and a descending anchor distance gradient for each layer. In addition, new sequences can be added to a clustered dataset by comparison with existing anchor nodes to achieve quick positioning and substantially reduce the computational burden. en_US
dc.description.sponsorship NSERC Discovery: 301-021000-401122-000000 GPA: 300-021000-071886-000000 en_US
dc.language.iso en en_US
dc.rights Attribution-NonCommercial-NoDerivs 2.5 Canada *
dc.rights.uri *
dc.subject clustering en_US
dc.subject antigen receptor en_US
dc.subject anchor en_US
dc.subject bioinformatics en_US
dc.title Large-scale clustering of antigen receptor gene sequence data using hyper-dimensional point packing en_US
dc.type Thesis en_US Bioinformatics en_US Master of Science en_US Department of Pathobiology en_US
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Attribution-NonCommercial-NoDerivs 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 2.5 Canada