NDLI: Computational Prediction of Polycomb-Associated Long Non-Coding RNAs

Content Provider	PubMed Central
Author	Glazko, Galina V. Zybailov, Boris L. Rogozin, Igor B.
Editor	Paolo, Provero
Copyright Year	2012
Abstract	Among thousands of long non-coding RNAs (lncRNAs) only a small subset is functionally characterized and the functional annotation of lncRNAs on the genomic scale remains inadequate. In this study we computationally characterized two functionally different parts of human lncRNAs transcriptome based on their ability to bind the polycomb repressive complex, PRC2. This classification is enabled by the fact that while all lncRNAs constitute a diverse set of sequences, the classes of PRC2-binding and PRC2 non-binding lncRNAs possess characteristic combinations of sequence-structure patterns and, therefore, can be separated within the feature space. Based on the specific combination of features, we built several machine-learning classifiers and identified the SVM-based classifier as the best performing. We further showed that the SVM-based classifier is able to generalize on the independent data sets. We observed that this classifier, trained on the human lncRNAs, can predict up to 59.4% of PRC2-binding lncRNAs in mice. This suggests that, despite the low degree of sequence conservation, many lncRNAs play functionally conserved biological roles.
Related Links	http://dx.doi.org/10.1371/journal.pone.0044878
Starting Page	44878
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	9
Volume Number	7
Language	English
Publisher	Public Library of Science
Publisher Date	2012-09-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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