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  Identification and classification of ncRNA molecules using graph properties

Childs, L., Nikoloski, Z., May, P., & Walther, D. (2009). Identification and classification of ncRNA molecules using graph properties. Nucleic Acids Research, 37(9), e66. doi:10.1093/nar/gkp206.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2608-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-BC39-9
Genre: Journal Article

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 Creators:
Childs, L.1, Author              
Nikoloski, Z.2, Author              
May, P.1, Author              
Walther, D.1, Author              
Affiliations:
1BioinformaticsCIG, Infrastructure Groups and Service Units, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753303              
2Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

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Free keywords: rna secondary structure noncoding rnas structure prediction gene-expression structured rnas messenger-rna sequences algorithm alignment search
 Abstract: 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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Language(s): eng - English
 Dates: 2009-04-032009
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: ISI: ISI:000266354600031
DOI: 10.1093/nar/gkp206
ISSN: 1362-4962 (Electronic)0305-1048 (Linking)
URI: ://000266354600031http://nar.oxfordjournals.org/content/37/9/e66.full.pdf
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Title: Nucleic Acids Research
  Other : Nucleic Acids Res.
Source Genre: Journal
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Pages: - Volume / Issue: 37 (9) Sequence Number: - Start / End Page: e66 Identifier: ISSN: 0301-5610
CoNE: /journals/resource/1000000000262810