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Issue No.02 - March-April (2013 vol.10)
pp: 274-285
Jikai Lei , Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
Prapaporn Techa-angkoon , Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
Yanni Sun , Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
Noncoding RNA (ncRNA) identification is highly important to modern biology. The state-of-the-art method for ncRNA identification is based on comparative genomics, in which evolutionary conservations of sequences and secondary structures provide important evidence for ncRNA search. For ncRNAs with low sequence conservation but high structural similarity, conventional local alignment tools such as BLAST yield low sensitivity. Thus, there is a need for ncRNA search methods that can incorporate both sequence and structural similarities. We introduce chain-RNA, a pairwise structural alignment tool that can effectively locate cross-species conserved RNA elements with low sequence similarity. In chain-RNA, stem-loop structures are extracted from dot plots generated by an efficient local-folding algorithm. Then, we formulate stem alignment as an extended 2D chain problem and employ existing chain algorithms. Chain-RNA is tested on a data set containing annotated ncRNA homologs and is applied to novel ncRNA search in a transcriptomic data set. The experimental results show that chain-RNA has better tradeoff between sensitivity and false positive rate in ncRNA prediction than conventional sequence similarity search tools and is more time efficient than structural alignment tools. The source codes of chain-RNA can be downloaded at or at
RNA, Chain algorithms,chain algorithm, Noncoding RNA search, secondary structures, structural alignment
Jikai Lei, Prapaporn Techa-angkoon, Yanni Sun, "Chain-RNA: A Comparative ncRNA Search Tool Based on the Two-Dimensional Chain Algorithm", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 2, pp. 274-285, March-April 2013, doi:10.1109/TCBB.2012.137
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