Strategies to Annotate and Characterize Long Noncoding RNAs: Advantages and Pitfalls

• Published in: Trends in genetics Vol. 34; no. 9; pp. 704 - 721
• Main Authors: Cao, Huifen, Wahlestedt, Claes, Kapranov, Philipp
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.09.2018
• Subjects: challenges to lncRNA annotation; functional annotation of lncRNAs; integrative approach to lncRNA annotation; lncRNAs; methods to analyze lncRNA function; unique features of lncRNAs; lncRNAs; integrative approach to lncRNA annotation; challenges to lncRNA annotation; functional annotation of lncRNAs; unique features of lncRNAs; methods to analyze lncRNA function
• ISSN: 0168-9525
• DOI: 10.1016/j.tig.2018.06.002

The past decade has seen an explosion of interest in long noncoding RNAs (lncRNAs). However, despite the massive volume of scientific data implicating these transcripts in a plethora of molecular and cellular processes, a great deal of controversy surrounds these RNAs. One of the main reasons for this lies in the multiple unique features of lncRNAs which limit the available methods used to characterize them. Combined with their vast numbers and inadequate classification, comprehensive annotation of these transcripts becomes a daunting task. The solution to this complex challenge likely lies in deep understanding of the strengths and weaknesses of each computational and empirical approach, and integration of multiple strategies to reduce noise, authenticate the results, and classify lncRNAs. We review here both the advantages and caveats of strategies commonly used for functional characterization and annotation of lncRNAs in the context of emerging conceptual guidelines for their application. Collectively, lncRNAs represent not only a very exciting and intriguing but also, methodologically, a very challenging group of transcripts to study. The challenges mainly stem from many unique features of these RNAs that create technical hurdles at every level of their annotation. Multiple in silico and wet-bench strategies have been developed for every level of lncRNA annotation, starting with genomic architecture and basic annotation all the way to mechanistic and functional insights. However, each one comes with unique set of advantages but also limitations and caveats whose understanding is crucial for proper implementation and interpretation of these methods. Integration of data from multiple approaches will very likely reduce biological and technological noise, authenticate the true mechanisms of lncRNA function, and classify these transcripts.