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Briefings in Bioinformatics
Article
License: implied-oa
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PubMed Central
Other literature type . 2016
License: CC BY NC
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Briefings in Bioinformatics
Article . 2015 . Peer-reviewed
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DBLP
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Revealing protein–lncRNA interaction

Authors: Fabrizio Ferrè; Alessio Colantoni; Manuela Helmer-Citterich;

Revealing protein–lncRNA interaction

Abstract

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

Country
Italy
Keywords

Models, Molecular, Settore BIO/11 - BIOLOGIA MOLECOLARE, Co-immunoprecipitation; High-throughput sequencing; Long non-coding RNAs; Protein-RNA interactions; Molecular Biology; Information Systems, Protein Conformation, SELEX Aptamer Technique, high-throughput sequencing, 500, Computational Biology, High-Throughput Nucleotide Sequencing, RNA-Binding Proteins, co-immunoprecipitation, long non-coding RNAs, protein–RNA interactions, high-throughput sequencing, co-immunoprecipitation, long non-coding RNAs, Papers, protein–RNA interactions, Humans, Nucleic Acid Conformation, Computer Simulation, RNA, Long Noncoding, Protein Interaction Maps

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    584
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
584
Top 0.1%
Top 1%
Top 1%
Green
gold