TY  - GEN
A1  - Childs, Liam H.
A1  - Nikoloski, Zoran
A1  - May, Patrick
A1  - Walther, Dirk
T1  - Identification and classification of ncRNA molecules using graph properties
N2  - 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - paper 145 
KW  - RNA secondary structure
KW  - Noncoding RNAs
KW  - Structure prediction
KW  - Gene-expression
KW  - Structured RNAs
Y1  - 2010
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/4323
UR  - https://nbn-resolving.org/urn:nbn:de:kobv:517-opus-45192
ER  -