RNAMotifScanX: a graph alignment approach for RNA structural motif identification

    Authors

    RNA-Publ. RNA Soc.

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Oncogene

    Keywords

    RNA structural motif; noncanonical base pair; base-stacking interaction; ribosomal RNA; BASE-PAIRS; RIBOSOMAL-RNA; AUTOMATIC IDENTIFICATION; 3-DIMENSIONAL; STRUCTURES; ISOSTERICITY MATRICES; SECONDARY STRUCTURE; PAIRWISE; ALIGNMENT; BUILDING-BLOCKS; NONCODING RNAS; 3D STRUCTURES; Biochemistry & Molecular Biology

    Abstract

    RNA structural motifs are recurrent three-dimensional (3D) components found in the RNA architecture. These RNA structural motifs play important structural or functional roles and usually exhibit highly conserved 3D geometries and base-interaction patterns. Analysis of the RNA 3D structures and elucidation of their molecular functions heavily rely on efficient and accurate identification of these motifs. However, efficient RNA structural motif search tools are lacking due to the high complexity of these motifs. In this work, we present RNAMotifScanX, a motif search tool based on a base-interaction graph alignment algorithm. This novel algorithm enables automatic identification of both partially and fully matched motif instances. RNAMotifScanX considers noncanonical base-pairing interactions, base-stacking interactions, and sequence conservation of the motifs, which leads to significantly improved sensitivity and specificity as compared with other state-of-the-art search tools. RNAMotifScanX also adopts a carefully designed branch-and-bound technique, which enables ultra-fast search of large kink-turn motifs against a 23S rRNA.

    Subjects

    C. C. Zhong;S. J. Zhang

    Volume

    21

    Issue/Number

    3

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    333

    Last Page

    346

    WOS Identifier

    WOS:000349848800003

    ISSN

    1355-8382

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