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Network-Based Methods and Other Approaches for Predicting lncRNA Functions and Disease Associations

  • Rosario Michael Piro
  • Annalisa MarsicoEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1912)

Abstract

The discovery that a considerable portion of eukaryotic genomes is transcribed and gives rise to long noncoding RNAs (lncRNAs) provides an important new perspective on the transcriptome and raises questions about the centrality of these lncRNAs in gene-regulatory processes and diseases. The rapidly increasing number of mechanistically investigated lncRNAs has provided evidence for distinct functional classes, such as enhancer-like lncRNAs, which modulate gene expression via chromatin looping, and noncoding competing endogenous RNAs (ceRNAs), which act as microRNA decoys. Despite great progress in the last years, the majority of lncRNAs are functionally uncharacterized and their implication for disease biogenesis and progression is unknown. Here, we summarize recent developments in lncRNA function prediction in general and lncRNA–disease associations in particular, with emphasis on in silico methods based on network analysis and on ceRNA function prediction. We believe that such computational techniques provide a valuable aid to prioritize functional lncRNAs or disease-relevant lncRNAs for targeted, experimental follow-up studies.

Key words

lncRNA ceRNA Function prediction Disease-gene prediction Network analysis Chromatin interactions 

Notes

Acknowledgements

The authors kindly acknowledge Heike Siebert and Denise Thiel for insightful discussions. This study is supported by the DFG Grant MA 4454/3-1.

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Authors and Affiliations

  1. 1.Institut für InformatikFreie Universität BerlinBerlinGermany
  2. 2.Institut für Medizinische Genetik und HumangenetikCharité-Universitätsmedizin BerlinBerlinGermany
  3. 3.Max-Planck-Institut für molekulare GenetikBerlinGermany

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