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Orabi, Baraa

University of British Columbia

Alternative splicing is an essential cellular mechanism in humans that enables increased protein diversity and tissue differentiation. Thus, the study of alternative splicing is of great importance in our endeavour to address a variety of human diseases such as cancer. Different transcriptomic sequencing technologies have been deployed to investigate alternative splicing, each with its own trade-offs: short-read sequencing, one of the most commonly used sequencing techniques, has the advantage of a low per-base error rate but suffers from short read lengths that limit its capacity to resolve most alternative splicing events accurately; long-read sequencing, a more recent technology, is able to sequence the full length of most alternative splicing transcripts but suffers from a high sequencing error rate that introduces non-trivial processing challenges; single-cell sequencing, which traditionally relies on short-read sequencing, enables cell-level resolution of transcriptomic sequencing and gene expression analysis but is severely limited in its capacity to resolve alternative splicing events; and finally, hybrid single-cell sequencing that utilizes both short- and long-read sequencing has the potential to enable cell-level gene expression analysis and alternative splicing detection but requires significant computational effort to synthesize the data of its two underlying sequencing techniques. In my dissertation, I present my work on three computational methods that enable the detection of alternative splicing using transcriptomic sequencing technologies: (i) Freddie detects alternatively spliced isoforms using long-read sequencing; (ii) scTagger maps long-reads to their error-corrected cellular barcodes in hybrid short- and long-read single-cell transcriptomic sequencing experiments; and (iii) scFreddie detects alternatively spliced isoforms in the context of the mentioned hybrid single-cell transcriptomic sequencing technique utilizing the output of tools such as scTagger.

Text

2024-08-28

Attribution-NonCommercial-ShareAlike 4.0 International

http://hdl.handle.net/2429/89071

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-sa/4.0/