Quadruplexes (G4s) are of interest, which increases with the number of identified G4 structures and knowledge about their biomedical potential. These unique motifs form in many organisms, including humans, where their appearance correlates with various diseases. Scientists store and analyze quadruplexes using recently developed bioinformatic tools-many of them focused on DNA structures. With an expanding collection of G4 RNAs, we check how existing tools deal with them. We review all available bioinformatics resources dedicated to quadruplexes and examine their usefulness in G4 RNA analysis. We distinguish the following subsets of resources: databases, tools to predict putative quadruplex sequences, tools to predict secondary structure with quadruplexes and tools to analyze and visualize quadruplex structures. We share the results obtained from processing specially created RNA datasets with these tools. Contact: [email protected] Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

MOTIVATION: Quadruplexes attract the attention of researchers from many fields of bio-science. Due to a specific structure, these tertiary motifs are involved in various biological processes. They are also promising therapeutic targets in many strategies of drug development, including anticancer and neurological disease treatment. The uniqueness and diversity of their forms cause that quadruplexes show great potential in novel biological applications. The existing approaches for quadruplex analysis are based on sequence or 3D structure features and address canonical motifs only. RESULTS: In our study, we analyzed tetrads and quadruplexes contained in nucleic acid molecules deposited in Protein Data Bank. Focusing on their secondary structure topology, we adjusted its graphical diagram and proposed new dot-bracket and arc representations. We defined the novel classification of these motifs. It can handle both canonical and non-canonical cases. Based on this new taxonomy, we implemented a method that automatically recognizes the types of tetrads and quadruplexes occurring as unimolecular structures. Finally, we conducted a statistical analysis of these motifs found in experimentally determined nucleic acid structures in relation to the new classification. AVAILABILITY AND IMPLEMENTATION: https://github.com/tzok/eltetrado/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.