Evidence map›Paper›PMID 27049585›Full record

ReviewGenomics, proteomics & bioinformatics2016

Long non-coding RNA Databases in Cardiovascular Research.

Frank Rühle, Monika Stoll

Open access · goldAbstract readReview
In one paragraph

Review in Genomics, proteomics & bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
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

21 citing papers in PubMed, 39 citations in OpenAlex.

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  12. Genome-wide methods for investigating long noncoding RNAs.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2019
    Review
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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

2 authors at 1 institution in 1 country.

Frank RühleInstitute of Human Genetics, Genetic Epidemiology, University of Muenster, 48149 Muenster, Germany.
Monika StollInstitute of Human Genetics, Genetic Epidemiology, University of Muenster, 48149 Muenster, Germany; Cardiovascular Research Institute Maastricht (CARIM), Genetic Epidemiology and Statistical Genetics, Maastricht Center for Systems Biology (MaCSBio), Maastricht University, 6211 LK Maastricht, The Netherlands. Electronic address: mstoll@uni-muenster.de.
Maastricht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rising interest in the regulatory functions of long non-coding RNAs (lncRNAs) in complex human diseases such as cardiovascular diseases, there is an increasing need in public databases offering comprehensive and integrative data for all aspects of these versatile molecules. Recently, a variety of public data repositories that specialized in lncRNAs have been developed, which make use of huge high-throughput data particularly from next-generation sequencing (NGS) approaches. Here, we provide an overview of current lncRNA databases covering basic and functional annotation, lncRNA expression and regulation, interactions with other biomolecules, and genomic variants influencing the structure and function of lncRNAs. The prominent lncRNA antisense noncoding RNA in the INK4 locus (ANRIL), which has been unequivocally associated with coronary artery disease through genome-wide association studies (GWAS), serves as an example to demonstrate the features of each individual database.

Indexed as

Databases, GeneticCardiovascular DiseasesGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansResearchRNA, Long NoncodingSequence Analysis, RNARNA, Long NoncodingANRILCardiovascular diseaseDatabaseGene regulationlncRNANon-coding

Identifiers

PMID27049585
PMCPMC4996844
OpenAlexW2340343609

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.