UC Santa Cruz

Transfer RNAs (tRNAs) are essential components of translation across all domains of life. The importance of this function is reflected in the strength of their conservation at the genome level, as well as their presence in hundreds of copies across each eukaryotic genome. Their strong conservation and high copy number at the genome level, in conjunction with their extensive post-transcriptional modifications and extreme variation in transcriptional activity by locus, make tRNA genes an enticing but as yet understudied model gene family.The requirement of tRNA transcripts in exceptionally large quantities causes tRNA loci to experience among the highest rates of transcription in the genome. Consequently, transcription-associated mutagenesis (TAM) and natural selection leave distinct genomic signatures at highly transcribed tRNA loci, such that tRNA genes are strongly conserved despite elevated mutation rates, and their immediate flanking regions are among the most variable sites in the genome. Here, I characterize the relationship between expression, mutation, and selection at tRNA loci in detail by using population genetics, comparative genomics, epigenetics, and transcriptomic data. I then use these findings to engineer a random-forest model to predict tRNA gene transcriptional activity using only DNA data. In the second half of this dissertation, I use the comparative genomics skills developed in the first part to help develop a novel phylogenetics toolkit. I identify the effects of sequencing errors on large SARS-CoV-2 phylogenies at global and local scales, demonstrate a novel method to quickly add samples to phylogenies, and explore recombination events in SARS-CoV-2 data, finding an excess in the region surrounding the Spike protein. In this dissertation, I use publicly available DNA, RNA, and epigenetic data to develop novel bioinformatic analysis methods. Together, the conclusions drawn in this dissertation for both tRNA biology and SARS-CoV-2 answer fundamental evolutionary questions.