Long non-coding RNAs and complex diseases: from experimental results to computational models
TLDR
Some state-of-the-art computational models are introduced, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease- related lnc RNAs for experimental validation and discussed the future directions of developing computational models for lncRNA research.Abstract:
LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA-disease associations and predicting potential human lncRNA-disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.read more
Citations
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A Large Intergenic Noncoding RNA Induced by p53 Mediates Global Gene Repression in the p53 Response
Maite Huarte,Mitchell Guttman,Mitchell Guttman,David M. Feldser,Manuel Garber,Magdalena J. Koziol,Magdalena J. Koziol,Daniela Kenzelmann-Broz,Ahmad M. Khalil,Ahmad M. Khalil,Or Zuk,Ido Amit,Michal Rabani,Laura D. Attardi,Aviv Regev,Aviv Regev,Eric S. Lander,Eric S. Lander,Eric S. Lander,Tyler Jacks,John L. Rinn,John L. Rinn +21 more
TL;DR: In this paper, the identification of lincRNAs (lincRNA-p21) that serve as a repressor in p53-dependent transcriptional responses was reported, and the observed transcriptional repression was mediated through the physical association with hnRNP-K at repressed genes and regulation of p53 mediates apoptosis.
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MicroRNAs and complex diseases: from experimental results to computational models.
TL;DR: Twenty state-of-the-art computational models of predicting miRNA-disease associations from different perspectives are reviewed, including five feasible and important research schemas, and future directions for further development of computational models are summarized.
Journal ArticleDOI
LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases.
TL;DR: The new developments in LncRNADisease 2.0 include an over 40-fold lncRNA-disease association enhancement compared with the previous version, and providing the transcriptional regulatory relationships among lnc RNA, mRNA and miRNA.
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Predicting miRNA-disease association based on inductive matrix completion.
TL;DR: A novel model of Inductive Matrix Completion for MiRNA‐Disease Association prediction (IMCMDA) to complete the missing miRNA‐disease association based on the known associations and the integrated miRNA similarity and disease similarity.
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PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.
TL;DR: The reliable performance of Path-Based MiRNA-Disease Association is demonstrated, which demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.
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TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Journal ArticleDOI
A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language?
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TL;DR: The most complete human lncRNA annotation to date is presented, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts, and expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes.
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