The function of pseudoknots cannot be ignored in the RNA secondary structure. Existing methods for analyzing RNA secondary structures with pseudoknots exhibit many shortcomings. This paper presents a novel RNA secondary structure visualization method in the case of a joint analysis of RNA primary structures and secondary structures. The way is based on the page number representation of the RNA secondary structure. It innovatively uses five vectors to represent bases, which are sequentially connected to outline the characteristics of the RNA secondary structure. The method covers almost all the constituent elements of the RNA secondary structure and extracts features completely. Experiments are based on the available techniques for large-scale annotation of RNA secondary structures, using a combination method of discrete wavelet transform and fractal dimension. The classification effect is compared with the previous RNA secondary structure representation methods. Experimental results show that the RNA secondary structure visualization method proposed in this paper has good application prospects in RNA secondary structure classification.

In organisms, ribonucleic acid (RNA) plays an essential role. Its function is being discovered more and more. Due to the conserved nature of RNA sequences, its function mainly depends on the RNA secondary structure. The discovery of an approximate relationship between two RNA secondary structures helps to understand their functional relationship better. It is an important and urgent task to explore structural similarities from the graphical representation of RNA secondary structures. In this paper, a novel graphical analysis method based on the triple vector curve representation of RNA secondary structures is proposed. A combinational method involving a discrete wavelet transform (DWT) and fractal dimension with sliding window is introduced to analyze and compare the graphs derived from feature extraction; after that, the distance matrix is generated. Then, the distance matrix is analyzed by clustering and visualized as a clustering tree. RNA virus and noncoding RNA datasets are applied to perform experiments and analyze the clustering tree. The results show that the proposed method yields more accurate results in the comparison of RNA secondary structures.

Background Ribonucleic acid (RNA) is an important biological macromolecule. Through in-depth studies of RNA, its function has been increasingly discovered. The function of RNA is mostly dependent on its secondary structure because of its conserved nature. The discovery of an approximate relationship between two RNA secondary structures can help to understand their functional relationship better. This discovery can also help in exploring many unknown functions. Currently, RNA secondary structural similarity analysis methods are mainly divided into alignment-based methods and alignment-free methods. Alignment-free methods can obtain similarities and differences among RNA secondary structures more quickly and more accurately than alignment-based methods. Results In this paper, a novel alignment-free method based on the triple vector curve representation of RNA is proposed. A combinational method involving a discrete wavelet transform and detrended fluctuation analysis (DFA) with a sliding window is used to generate the distance matrix. Finally, a phylogenetic tree is constructed using the distance matrix. Experiments are performed on RNA viruses and non-coding RNA datasets, and the phylogenetic trees generated by different methods are compared. The results show that our method yields more accurate results in the comparison of RNA secondary structures. Conclusion The method in this paper enables a more accurate analysis of the similarities between RNA secondary structures. This method has certain application value in the comparison of RNA secondary structures, especially in the analysis of longer RNA sequences.