Evidence map›Paper›PMID 28379566›Full record

ArticleNucleic acids research2017

LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells.

Jiajian Zhou, Suyang Zhang, Huating Wang, Hao Sun

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.4field-weighted citation impact, top 21% 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

12 citing papers in PubMed, 25 citations in OpenAlex.

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  6. Long non-coding RNAMolecular therapy. Nucleic acids · 2021
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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

4 authors at 2 institutions in 1 country.

Jiajian ZhouDepartment of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China.
Suyang ZhangDepartment of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China.
Huating WangLi Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China.
Hao SunDepartment of Chemical Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR, China.
Chinese University of Hong Kong · CNPrince of Wales Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long noncoding RNAs (lncRNAs) are key regulators of diverse cellular processes. Recent advances in high-throughput sequencing have allowed for an unprecedented discovery of novel lncRNAs. To identify functional lncRNAs from thousands of candidates for further functional validation is still a challenging task. Here, we present a novel computational framework, lncFunNet (lncRNA Functional inference through integrated Network) that integrates ChIP-seq, CLIP-seq and RNA-seq data to predict, prioritize and annotate lncRNA functions. In mouse embryonic stem cells (mESCs), using lncFunNet we not only recovered most of the functional lncRNAs known to maintain mESC pluripotency but also predicted a plethora of novel functional lncRNAs. Similarly, in mouse myoblast C2C12 cells, applying lncFunNet led to prediction of reservoirs of functional lncRNAs in both proliferating myoblasts (MBs) and differentiating myotubes (MTs). Further analyses demonstrated that these lncRNAs are frequently bound by key transcription factors, interact with miRNAs and constitute key nodes in biological network motifs. Further experimentations validated their dynamic expression profiles and functionality during myoblast differentiation. Collectively, our studies demonstrate the use of lncFunNet to annotate and identify functional lncRNAs in a given biological system.

Indexed as

SoftwareAlgorithmsAnimalsCell DifferentiationGene Expression RegulationMiceMicroRNAsMolecular Sequence AnnotationMouse Embryonic Stem CellsMuscle Fibers, SkeletalMyoblastsNucleotide MotifsProtein BindingRNA, Long NoncodingTranscription FactorsMicroRNAsRNA, Long NoncodingTranscription Factors

Identifiers

PMID28379566
PMCPMC5499579
OpenAlexW2604820680

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.