\r\non its structure. The objective of the alignment is finding the

\r\nhomology between two or more RNA secondary structures. Knowing

\r\nthe common functionalities between two RNA structures allows

\r\na better understanding and a discovery of other relationships

\r\nbetween them. Besides, identifying non-coding RNAs -that is not

\r\ntranslated into a protein- is a popular application in which RNA

\r\nstructural alignment is the first step A few methods for RNA

\r\nstructure-to-structure alignment have been developed. Most of these

\r\nmethods are partial structure-to-structure, sequence-to-structure, or

\r\nstructure-to-sequence alignment. Less attention is given in the

\r\nliterature to the use of efficient RNA structure representation and the

\r\nstructure-to-structure alignment methods are lacking. In this paper,

\r\nwe introduce an O(N2) Component-based Pairwise RNA Structure

\r\nAlignment (CompPSA) algorithm, where structures are given as

\r\na component-based representation and where N is the maximum

\r\nnumber of components in the two structures. The proposed algorithm

\r\ncompares the two RNA secondary structures based on their weighted

\r\ncomponent features rather than on their base-pair details. Extensive

\r\nexperiments are conducted illustrating the efficiency of the CompPSA

\r\nalgorithm when compared to other approaches and on different real

\r\nand simulated datasets. The CompPSA algorithm shows an accurate

\r\nsimilarity measure between components. The algorithm gives the

\r\nflexibility for the user to align the two RNA structures based on

\r\ntheir weighted features (position, full length, and\/or stem length).

\r\nMoreover, the algorithm proves scalability and efficiency in time and

\r\nmemory performance.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 126, 2017"}