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Nip, Ka Ming

University of British Columbia

Since its introduction, RNA-sequencing has allowed us to interrogate the transcriptome of an organism, thereby advancing our understanding of cell biology and diseases. Typically, raw RNA-sequencing data is processed via computational methods, such as transcriptome assembly and visualization, to extract meaningful information. Transcriptome assembly aims to reconstruct full-length transcript sequences from RNA-sequencing reads, which are usually short fragments of the corresponding transcripts. Transcriptome visualization provides a platform for exploring and recognizing patterns in transcriptomic data. Transcriptome assembly and visualization tools have been instrumental in identification of gene structures, annotation of draft genomes, and discovery of molecular markers in diseases. Single-cell RNA-sequencing has enabled us to investigate transcriptome heterogeneity within a tissue sample containing up to a million cells. However, single-cell transcriptome analyses have been predominantly performed at the gene level instead of at the isoform level. In my thesis, I present computational solutions for transcriptome assembly and visualization of single-cell RNA-sequencing data thus enabling isoform-level analysis in single cell transcriptomes. Long-read RNA-sequencing technologies have gained traction in transcriptomic research in recent years as their throughput and data quality improved tremendously. Long-read sequencing is particularly useful in transcriptome assembly because its reads can potentially span multiple exons, which simplifies the transcriptome assembly problem. Reference-free assembly for long-read data is a computationally expensive task due to the long read lengths and high base error rates. In my thesis, I present a fast and memory-efficient reference-free assembly method for long-read RNA-sequencing data.

Text

2023-04-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/84238

Bioinformatics

University of British Columbia

UBCV

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