Nucleic Acids Research Advance Access originally published online on April 1, 2009
Nucleic Acids Research 2009 37(9):e66; doi:10.1093/nar/gkp206
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Nucleic Acids Research, 2009, Vol. 37, No. 9 e66
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Identification and classification of ncRNA molecules using graph properties
1Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1 and 2University of Potsdam, Institute for Biology and Biochemistry, Karl-Liebknecht-Str. 24-25, Haus 26, 14476 Golm, Germany
*To whom correspondence should be addressed. Tel: +49 0 30 5678 624; Fax: +49 0 30 5678 136; Email: childs{at}mpimp-golm.mpg.de
Received December 22, 2008. Revised February 27, 2009. Accepted March 12, 2009.
The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.
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