RNA Sequence
RNA sequence research focuses on understanding the relationship between RNA sequences and their diverse functions, aiming to improve prediction, design, and manipulation of RNA molecules. Current research heavily utilizes deep learning models, including transformer-based architectures and diffusion models, to analyze RNA sequences, predict properties like translation efficiency and secondary structure, and even generate novel sequences with desired characteristics. These advancements are crucial for various applications, such as developing improved mRNA vaccines and therapeutics, optimizing CRISPR-Cas systems for gene editing, and gaining deeper insights into fundamental biological processes.
Papers
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data
Manal Helal, Fanrong Kong, Sharon C. A. Chen, Michael Bain, Richard Christen, Vitali Sintchenko
Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments
Manal Helal, Fanrong Kong, Sharon C-A Chen, Fei Zhou, Dominic E Dwyer, John Potter, Vitali Sintchenko