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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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Datensatz-Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2608-F Versions-Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-BC39-9
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 Urheber:
Childs, L.1, Autor              
Nikoloski, Z.2, Autor              
May, P.1, Autor              
Walther, D.1, Autor              
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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Schlagwörter: rna secondary structure noncoding rnas structure prediction gene-expression structured rnas messenger-rna sequences algorithm alignment search
 Zusammenfassung: 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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Sprache(n): eng - Englisch
 Datum: 2009-04-032009
 Publikationsstatus: Im Druck veröffentlicht
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 Identifikatoren: 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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Titel: Nucleic Acids Research
  Andere : Nucleic Acids Res.
Genre der Quelle: Zeitschrift
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Seiten: - Band / Heft: 37 (9) Artikelnummer: - Start- / Endseite: e66 Identifikator: ISSN: 0301-5610
CoNE: /journals/resource/1000000000262810