Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs.
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.
Garvan Institute of Medical Research, Sydney, Australia; St Vincent's Clinical School, University of New South Wales, Sydney, Australia.
Bethany
B
Signal
Garvan Institute of Medical Research, Sydney, Australia; St Vincent's Clinical School, University of New South Wales, Sydney, Australia.
Brian S
BS
Gloss
Garvan Institute of Medical Research, Sydney, Australia; St Vincent's Clinical School, University of New South Wales, Sydney, Australia. Electronic address: m.dinger@garvan.org.au.
Marcel E
ME
Dinger
D019295:Computational Biology; D030541:Databases, Genetic; D016678:Genome; D062085:RNA, Long Noncoding / Q000737:chemistry / Q000235:genetics* / Q000302:isolation & purification
D062085:RNA, Long Noncoding
computational methods; functional mechanisms.; lncRNA; regulation