Polynomial encoding for RNA structure analytics

Advancements in sequencing technologies have produced a
wealth of genomic data. In parallel, the development of artificial
intelligence has enabled powerful folding models that accurately
predict molecular structures from sequences. These advancements have
resulted in a myriad of biomolecular structure data. Biological
structures are more directly linked to their functions, and data
analytics of these structures advance biological research by providing
novel approaches and insights. A major challenge in structure data
analytics is the lack of efficient and accurate structure encodings.
In this talk, we introduce encodings of RNA secondary structures using
graph polynomials. We show that the tree-distinguishing polynomial
enables efficient, accurate and interpretable RNA secondary structure
analysis using modern data analytics tools. We demonstrate its
applications in predicting and understanding R-loop formation, as well
as in analyzing similarity and diversity of RNA structures in the
genomes of single-stranded RNA viruses.