Evidence map›Paper›PMID 26041786›Full record

ReviewBriefings in bioinformatics2016

Revealing protein-lncRNA interaction.

Fabrizio Ferrè, Alessio Colantoni, Manuela Helmer-Citterich

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 393 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
393citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

393 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. MCellular oncology (Dordrecht, Netherlands) · 2026
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333 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Fabrizio Ferrè
Alessio Colantoni
Manuela Helmer-Citterich

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computational BiologyComputer SimulationHigh-Throughput Nucleotide SequencingHumansModels, MolecularNucleic Acid ConformationProtein ConformationProtein Interaction MapsRNA-Binding ProteinsRNA, Long NoncodingSELEX Aptamer TechniqueRNA-Binding ProteinsRNA, Long Noncodingco-immunoprecipitationhigh-throughput sequencinglong non-coding RNAsprotein–RNA interactions

Identifiers

PMID26041786
PMCPMC4719072

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.