A novel computational method for inferring competing endogenous interactions. (26th September 2016)

• Record Type: Journal Article
• Authors: Sardina, Davide S; Alaimo, Salvatore; Ferro, Alfredo; Pulvirenti, Alfredo; Giugno, Rosalba
• Is Part Of: Briefings in bioinformatics. Volume 18:Number 6(2017:Nov.)
• Page Start: 1071
• Page End: 1081
• Publication Date: 2016-09-26
• Subjects: computational models -- experimental validations -- competing endogenous RNA; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw084
• Languages: English
• ISSNs: 1467-5463

Abstract

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.