Posttranscriptional cross talk and communication between genes mediated by microRNA response element (MREs) yield large regulatory competing endogenous RNA (ceRNA) networks. Their inference may improve the understanding of pathologies and shed new light on biological mechanisms. A variety of RNA: messenger RNA, transcribed pseudogenes, noncoding RNA, circular RNA and proteins related to RNA-induced silencing complex complex interacting with RNA transfer and ribosomal RNA have been experimentally proved to be ceRNAs. We retrace the ceRNA hypothesis of posttranscriptional regulation from its original formulation [Salmena L, Poliseno L, Tay Y, et al. Cell 2011;146:353-8] to the most recent experimental and computational validations. We experimentally analyze the methods in literature [Li J-H, Liu S, Zhou H, et al. Nucleic Acids Res 2013;42:D92-7; Sumazin P, Yang X, Chiu H-S, et al. Cell 2011;147:370-81; Sarver AL, Subramanian S. Bioinformation 2012;8:731-3] comparing them with a general machine learning approach, called ceRNA predIction Algorithm, evaluating the performance in predicting novel MRE-based ceRNAs.

A novel computational method for inferring competing endogenous interactions

Giugno Rosalba
2017-01-01

Abstract

Posttranscriptional cross talk and communication between genes mediated by microRNA response element (MREs) yield large regulatory competing endogenous RNA (ceRNA) networks. Their inference may improve the understanding of pathologies and shed new light on biological mechanisms. A variety of RNA: messenger RNA, transcribed pseudogenes, noncoding RNA, circular RNA and proteins related to RNA-induced silencing complex complex interacting with RNA transfer and ribosomal RNA have been experimentally proved to be ceRNAs. We retrace the ceRNA hypothesis of posttranscriptional regulation from its original formulation [Salmena L, Poliseno L, Tay Y, et al. Cell 2011;146:353-8] to the most recent experimental and computational validations. We experimentally analyze the methods in literature [Li J-H, Liu S, Zhou H, et al. Nucleic Acids Res 2013;42:D92-7; Sumazin P, Yang X, Chiu H-S, et al. Cell 2011;147:370-81; Sarver AL, Subramanian S. Bioinformation 2012;8:731-3] comparing them with a general machine learning approach, called ceRNA predIction Algorithm, evaluating the performance in predicting novel MRE-based ceRNAs.
2017
competing endogenous RNA; computational models; experimental validations
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/949704
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