Graduate Thesis Or Dissertation

 

A Computational and Evolutionary Approach to Understanding Cryptic Unstable Transcripts in Yeast Public Deposited

https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/hx11xf38w
Abstract
  • Cryptic unstable transcripts (CUTs) are a largely unexplored class of nuclear exosome degraded, non-coding RNAs in budding yeast. It is highly debated whether CUT transcription has a functional role in the cell or whether CUTs represent noise in the yeast transcriptome. I sought to ascertain the extent of conserved CUT expression across a variety of Saccharomyces yeast strains to further understand and characterize the nature of CUT expression. To this end I designed a Hidden Markov Model (HMM) to analyze strand-specific RNA sequencing data from nuclear exosome rrp6Δ mutants to identify and compare CUTs in four different yeast strains: S288c, Σ1278b, JAY291 (S.cerevisiae) and N17 (S.paradoxus). My RNA-seq based method has greatly expanded upon previous CUT annotations in S.cerevisiae, underscoring the extensive and pervasive nature of unstable transcription. Utilizing a four-way genomic alignment I identified a large population of CUTs with conserved syntenic expression across all four strains. Furthermore I observed that certain configurations of gene-CUT pairs, where CUT expression originates from a gene 5’ or 3’ nucleosome free region, correlate with distinct expression trends for the associated gene. Bidirectional gene-CUT pairs correlate with higher expression of genes, and antisense gene-CUT pairs correlate with reduced gene expression. Interestingly these effects on gene expression are most prevalent in the presence of conserved CUT expression. Additionally I have shown that CUTs lack a well-defined 3’ nucleosome free region that is commonly observed at protein-coding genes, and suggests that 3’ NFRs are not characteristic of Sen1-dependent terminated transcripts.
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  • 2015
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  • 2019-11-16
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