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

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Computational Biology of Non-Coding RNA

Part of the book series: Methods in Molecular Biology ((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.

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Notes

  1. 1.

    See [83] for a review on disease similarity networks, i.e., disease–disease networks in which nodes are diseases and edges represent phenotypic similarities.

  2. 2.

    https://www.ncbi.nlm.nih.gov/mesh.

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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. Institut für Informatik, Freie Universität Berlin, Berlin, Germany

    Rosario Michael Piro & Annalisa Marsico

  2. Institut für Medizinische Genetik und Humangenetik, Charité-Universitätsmedizin Berlin, Berlin, Germany

    Rosario Michael Piro

  3. Max-Planck-Institut für molekulare Genetik, Berlin, Germany

    Annalisa Marsico

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  1. Rosario Michael Piro
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Correspondence to Annalisa Marsico .

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  1. Department of Dermatology, Erlangen University Hospital, Erlangen, Germany

    Xin Lai

  2. Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

    Shailendra K. Gupta

  3. Department of Dermatology, Erlangen University Hospital, Erlangen, Germany

    Julio Vera

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Piro, R.M., Marsico, A. (2019). Network-Based Methods and Other Approaches for Predicting lncRNA Functions and Disease Associations. In: Lai, X., Gupta, S., Vera, J. (eds) Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8982-9_12

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