Discovering common structural motifs from SSU 16 S ribosomal RNA secondary structures

Author

Huang, Hsien-Da ; Fang, Shu-Fen ; Horng, Jorng-Tzong ; Kao, Cheng-Yan

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., National Central Univ., Taiwan, China

fYear

2001

fDate

4-6 Nov 2001

Firstpage

104

Lastpage

111

Abstract

Some structural motifs, like tetra-loops, in ribosomal RNA are known to functionally implicate in virtually every aspect of protein synthesis. Our aim in this study is to discover common structural motifs (CSMs), which are related to specific domains or functions, within the secondary structures of ribosomal RNAs in a data set constructed. After applying data mining techniques to mine the common structural motifs, a machine learning approach is used to find significant discriminating common structural motifs from groups of organisms. By applying to several data sets constructed in this study, it suggests that the CSMs can provide effective information to classify organisms and help biologists understand the functions of ribosomal RNA. From the experiments of the classification of organisms and the construction of phylogenetic trees by CSMs mined, we find our approach is promising

Keywords

biology computing; data mining; learning (artificial intelligence); macromolecules; molecular biophysics; molecular configurations; proteins; SSU 16 S ribosomal RNA secondary structures; common structural motifs; data mining; machine learning; organism classification; phylogenetic trees; protein synthesis; secondary structures; tetra-loops; Assembly; Classification tree analysis; Computer science; DNA; Data mining; Machine learning; Organisms; Phylogeny; Protein engineering; RNA;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and Bioengineering Conference, 2001. Proceedings of the IEEE 2nd International Symposium on

Conference_Location

Bethesda, MD

Print_ISBN

0-7695-1423-5

Type

conf

DOI

10.1109/BIBE.2001.974418

Filename

974418