Bioinformatic Applications in Protein Low Complexity Regions and Targeted Metagenomics

Abstract

Part I: Low complexity regions (LCRs) are common motifs in eukaryotic proteins, despite the fact that they are also mutationally unstable. For LCRs to be widely used and tolerated there must be regulatory mechanisms which compensate for their presence. I have endeavored to characterize the relationships and co-evolution of LCRs with the abundance of the proteins that host them as well as the transcripts which encode them. As the abundance of a gene product is ultimately responsible for its associated phenotype, any relationships have implications for the many neurodegenerative diseases associated with LCR expansion. I found that there are indeed relationships. LCRs are more associated with low abundance proteins, but the opposite is true at the RNA level: LCRs encoding transcripts have higher abundance. Investigating the co-evolution of LCRs and transcript abundance revealed that on short evolutionary timescales indels in LCRs influence the selective pressures on TAb. Viewing LCRs through the previously unexplored lens of abundance has generated new results. Results which, together with explorations of information flow and low-complexity in untranslated regions, expand our knowledge of the functional impacts of LCRs evolution. Part II: A commonly encountered problem in DNA sequencing is a situation where the DNA of interest makes up a small proportion of the DNA in a sample. This challenge can be compounded when the DNA of interest may come from many different organisms. Targeted metagenomics is a set of techniques which aim to bias sequencing results towards the DNA of interest. Many of these techniques rely on carefully designed probes which are specific to targets of interest. I have developed a bioinformatic tool, HUBDesign, to design oligonucleotide probes to capture identifying sequences from a given set of targets of interest. Using HUBDesign, and other methods, I have contributed to projects ranging in context from clinical to ancient DNA.