Identification and classification of ncRNA molecules using graph properties

  • 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 struThe 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.show moreshow less

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Metadaten
Author:Liam Childs, Zoran Nikoloski, Patrick May, Dirk Walther
URN:urn:nbn:de:kobv:517-opus-45192
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe, ISSN 1866-8372 (paper 145)
Document Type:Postprint
Language:English
Date of Publication (online):2010/07/20
Year of Completion:2009
Publishing Institution:Universität Potsdam
Release Date:2010/07/20
Tag:Gene-expression; Noncoding RNAs; RNA secondary structure; Structure prediction; Structured RNAs
Source:Nucleic acids research 37 (2009), 9, Art. e66, DOI: 10.1093/nar/gkp206
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Weitergabe zu gleichen Bedingungen 2.0 Deutschland
Notes extern:
The article was originally published by Oxford University Press:
Nucleic Acids Research. - 37 (2009), 9, Art. e66 (12 S.)
ISSN 0305-1048
DOI 10.1093/nar/gkp206