Browsing by Author "Schaeper, David"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Computational Methods for Determining RNA-RNA Interactions
    (2023-06) Schaeper, David; Janga, Sarath Chandra; Yan, Jingwen; Srivastava, Mansi
    RNA molecules play vital roles in both viruses and cells, and one way to study their function is through the RNA-RNA interactions (RRIs) that occur. RRIs form in one of two ways, through protein mediated RRIs, where a protein brings the RNA molecules together, or through direct complimentary base pairing between the molecules, called RNA centric. Protein mediated RRIs have been captured and analyzed through experimental protocols such as cross-linking ligation and sequencing of hybrids (CLASH) and mapping RNA interactome in vivo (MARIO). RNA centric interactions have been investigated through experimental protocols ligation of interacting RNA followed by high-throughput sequencing (LIGR-seq), sequencing of psoralen crosslinked, ligated, selected hybrids (SPLASH), psoralen analysis of RNA interactions and structures (PARIS), and cross-linking of matched RNAs and deep sequencing (COMRADES). There are also tools that have been developed to predict RRIs and the predominant tools, RNAup and IntaRNA, utilize minimum free energy (MFE) calculations. In this work, initially RRIs were studied in the context of SARS-CoV-2 and its variants to observe evolutionary changes to RRIs. Using in silico RRIs generated through the COMRADES protocol by Ziv et al alongside computational predictions generated through IntaRNA and a large population of SARS-CoV-2 sequences, covariation analysis was used on the population stratified by variants to determine variant-specific evolutionary changes for certain long-range RRIs. Also, statistical evidence was found for a novel Beta variant specific RNA-RNA interaction. After this, RRIs were studied in the human HEK293T cell line through a novel experimental protocol using Oxford Nanopore long-read sequencing technology to be able to capture more complete information on RRIs mapped with the newly developed pipeline Alignment of Chimera through Clustering and Read Splitting (ACCRES). Through this, multi-molecule RNA interactions were able to be detected using an iterative BLAST approach, which is the first time these have been reported to our knowledge. Interaction interfaces were quantified, and the interactions were characterized by their biotype to understand the landscape of these interactions in the cell line. A network was built, and functional enrichment performed to show the interplay between known functions in the cell.
  • Thumbnail Image
    Item
    Variant-Specific Analysis Reveals a Novel Long-Range RNA-RNA Interaction in SARS-CoV-2 Orf1a
    (MDPI, 2022-09) Dukeshire, Matthew; Schaeper, David; Venkatesan, Pravina; Manzourolajdad, Amirhossein; BioHealth Informatics, School of Informatics and Computing
    Since the start of the COVID-19 pandemic, understanding the pathology of the SARS-CoV-2 RNA virus and its life cycle has been the priority of many researchers. Currently, new variants of the virus have emerged with various levels of pathogenicity and abundance within the human-host population. Although much of viral pathogenicity is attributed to the viral Spike protein’s binding affinity to human lung cells’ ACE2 receptor, comprehensive knowledge on the distinctive features of viral variants that might affect their life cycle and pathogenicity is yet to be attained. Recent in vivo studies into the RNA structure of the SARS-CoV-2 genome have revealed certain long-range RNA-RNA interactions. Using in silico predictions and a large population of SARS-CoV-2 sequences, we observed variant-specific evolutionary changes for certain long-range RRIs. We also found statistical evidence for the existence of one of the thermodynamic-based RRI predictions, namely Comp1, in the Beta variant sequences. A similar test that disregarded sequence variant information did not, however, lead to significant results. When performing population-based analyses, aggregate tests may fail to identify novel interactions due to variant-specific changes. Variant-specific analyses can result in de novo RRI identification.