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Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs

Abstract

Although a considerable portion of eukaryotic genomes is transcribed as long noncoding RNAs (lncRNAs), the vast majority are functionally uncharacterised. The rapidly expanding catalogue of mechanistically investigated lncRNAs has provided evidence for distinct functional subclasses, which are now ripe for exploitation as a general model to predict functions for uncharacterised lncRNAs. By utilising publicly-available genome-wide datasets and computational methods, we present several developed and emerging in silico approaches to characterise and predict the functions of lncRNAs. We propose that the application of these techniques provides valuable functional and mechanistic insight into lncRNAs, and is a crucial step for informing subsequent functional studies.

Type Journal
ISBN 0168-9525 (Print) 0168-9525 (Linking)
Authors Signal, B.; Gloss, B. S.; Dinger, M. E.;
Publisher Name TRENDS GENET
Published Date 2016-01-01 00:00:00
Published Volume 32
Published Issue 10
Published Pages 620-37
URL http://www.ncbi.nlm.nih.gov/pubmed/27592414
Status Published In-print
OpenAccess Link https://publications.gimr.garvan.org.au/download.php?13749_13507/2016-Signal-Trends Genet.pdf