Graph-Based Analysis of RNA Secondary Structure Similarity Comparison
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 he...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8841822 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560150391554048 |
|---|---|
| author | Lina Yang Yang Liu Xiaochun Hu Patrick Wang Xichun Li Jun Wu |
| author_facet | Lina Yang Yang Liu Xiaochun Hu Patrick Wang Xichun Li Jun Wu |
| author_sort | Lina Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f4da89d6c2cd4b2d806ad7004cb916ca |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f4da89d6c2cd4b2d806ad7004cb916ca2025-02-03T01:28:25ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/88418228841822Graph-Based Analysis of RNA Secondary Structure Similarity ComparisonLina Yang0Yang Liu1Xiaochun Hu2Patrick Wang3Xichun Li4Jun Wu5Computer and Electronic Information, Guangxi University, Nanning 530004, ChinaComputer and Electronic Information, Guangxi University, Nanning 530004, ChinaSchool of Information and Statistics, Guangxi University of Finance and Economics, Nanning 530007, ChinaComputer and Information Science, Northeastern University, Boston 02115, USAGuangxi Normal University for Nationalities, Chongzuo, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaIn 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.http://dx.doi.org/10.1155/2021/8841822 |
| spellingShingle | Lina Yang Yang Liu Xiaochun Hu Patrick Wang Xichun Li Jun Wu Graph-Based Analysis of RNA Secondary Structure Similarity Comparison Complexity |
| title | Graph-Based Analysis of RNA Secondary Structure Similarity Comparison |
| title_full | Graph-Based Analysis of RNA Secondary Structure Similarity Comparison |
| title_fullStr | Graph-Based Analysis of RNA Secondary Structure Similarity Comparison |
| title_full_unstemmed | Graph-Based Analysis of RNA Secondary Structure Similarity Comparison |
| title_short | Graph-Based Analysis of RNA Secondary Structure Similarity Comparison |
| title_sort | graph based analysis of rna secondary structure similarity comparison |
| url | http://dx.doi.org/10.1155/2021/8841822 |
| work_keys_str_mv | AT linayang graphbasedanalysisofrnasecondarystructuresimilaritycomparison AT yangliu graphbasedanalysisofrnasecondarystructuresimilaritycomparison AT xiaochunhu graphbasedanalysisofrnasecondarystructuresimilaritycomparison AT patrickwang graphbasedanalysisofrnasecondarystructuresimilaritycomparison AT xichunli graphbasedanalysisofrnasecondarystructuresimilaritycomparison AT junwu graphbasedanalysisofrnasecondarystructuresimilaritycomparison |