UBC Theses and Dissertations
Bioinformatic analysis of cis-encoded antisense transcription Morrissy, Anca Sorana
A key first step in understanding cellular processes is a quantitative and comprehensive measurement of gene expression profiles. The scale and complexity of the mammalian transcriptome is a significant challenge to efforts aiming to identify the complete set of expressed transcripts. Specifically, detection of low-abundance sequences, such as antisense transcripts, has historically been difficult to achieve using EST libraries, microarrays, or tag sequencing methods. Antisense transcripts are expressed from the opposite strand of a partner gene, and in some cases can regulate the processing of the sense transcript, highlighting their biological relevance. Recently, efficient profiling of low-frequency transcripts was made possible with the advent of next generation sequencing platforms. Thus, a major goal of my thesis was to assess the prevalence of antisense transcripts using Tag-seq, a tag sequencing method modified to take advantage of the Illumina sequencing platform. The increase in sampling depth provided by Tag-seq resulted in significantly improved detection of low abundance antisense transcripts, and allowed accurate measurements of their differential expression across normal and cancerous states. While antisense transcription is known to regulate sense transcript processing at a small number of loci, no genome wide assessments of this regulatory interaction exist. I addressed this knowledge gap using Affymetrix exon arrays, and found a significant correlation between antisense transcription and alternative splicing in normal human cells. Further exploring the biological relevance of antisense-correlated splicing events in human disease, I found that these events could be used to identify clinically distinct subtypes of cancer. Together, the findings in this thesis provide a new foundation for the investigation of antisense transcripts in the regulation of alternative transcript processing, and open new avenues of research into understanding the molecular heterogeneity of human cancers.
Item Citations and Data
Attribution-NonCommercial-NoDerivs 3.0 Unported