Trends in Genetics
ReviewComputational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs
Section snippets
The Emerging Need for Computational Methodologies to Discern Functional lncRNAs
Over the past decade advances in sequencing methodologies have revealed the transcriptional complexity of the genome. Early use of genome tiling arrays and CAGE-sequencing led to the elucidation that a much greater portion of the genome is transcribed than previously expected, with the majority of transcription producing non-protein coding RNAs 1, 2. Initially hampered by characteristic low expression, biological specificity, and lack of sequence conservation [3], the functions of the group of
Computational Techniques to Impute lncRNA Function
Core features of functional lncRNAs can be probed via an array of computational methods strengthened by publicly-available datasets. Gene expression information is commonly utilised to detect potential regulatory targets – currently the most common mechanism of action – or involvement in biological processes. Expression-based approaches depend on experimental data, which continues to be generated across a diverse repertoire of biological contexts and made publicly available through repositories
Integrative Approaches for Functional Candidate Selection
Each of the approaches outlined above can extract potential candidates for characterisation from an initial pool of lncRNAs. However, isolated use produces insufficient evidence to generate detailed hypotheses of function. This is typical with differential expression – where a multitude of lncRNA can be differentially expressed between conditions – but this change tells us little about whether the expression pattern is functional, and if so, how. Instead, integrative approaches that test
Testing lncRNA Functionality
Computational approaches provide a way to test multiple avenues of functionality on a large cohort of potentially important transcripts. Because of their relative ease in reuse on multiple candidates, computational analyses have been extensively used in identifying lncRNAs on a genome-wide scale 15, 17, 18, 45. While these approaches can provide insight into the general trends of lncRNA biology, the known specificity of lncRNA expression and function require that experimental methods (reviewed
Concluding Remarks and Future Directions
Within the past decade, lncRNAs have emerged as important RNA species, capable of fulfilling previously unascertained biological roles. Through the increasing application of high-throughput sequencing methods, lncRNAs continue to be discovered. However, the gap between identified and functionally characterised molecules remains considerably larger than that of protein-coding genes. By utilising a spectrum of freely-available software and publicly-available datasets, it is possible to
Acknowledgements
The authors would like to thank Daniel Thompson for helpful discussion on earlier drafts of this manuscript.
Glossary
- Cis-regulatory
- a type of regulatory relationship defined by the close genomic proximity between the regulator and target genes.
- Enhancer RNA (eRNA)
- a type of lncRNA transcribed from a genomic region possessing chromatin modifications typical of enhancer DNA. Enhancer lncRNAs may be non-functional, with the DNA being responsible for enhancer activity.
- Functional enrichment
- process by which functional annotations of groups of genes are tested for statistical enrichment in particular groups above
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These authors contributed equally to this manuscript.