Evidence map›Paper›PMID 24039761›Full record

ArticlePloS one2013

Integrative analysis of porcine microRNAome during skeletal muscle development.

Lijun Qin, Yaosheng Chen, Xiaohong Liu, Sanxing Ye, Kaifan Yu, Zheng Huang, Jingwei Yu, Xingyu Zhou, Hu Chen, Delin Mo

Abstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–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

29 citing papers in PubMed.

  1. Review
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  8. Article
  9. Genome-Wide Analysis of H3K27me3 in Porcine Embryonic Muscle Development.Frontiers in cell and developmental biology · 2021
    Article
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  14. 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

10 authors.

Lijun QinState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, P. R. China.
Yaosheng Chen
Xiaohong Liu
Sanxing Ye
Kaifan Yu
Zheng Huang
Jingwei Yu
Xingyu Zhou
Hu Chen
Delin Mo

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pig is an important agricultural animal for meat production and provides a valuable model for many human diseases. Functional studies have demonstrated that microRNAs (miRNAs) play critical roles in almost all aspects of skeletal muscle development and disease pathogenesis. To investigate the miRNAs involved in regulating different periods of skeletal muscle development, we herein performed a comprehensive research for porcine microRNAome (miRNAome) during 10 skeletal muscle developmental stages including 35, 49, 63, 77, 91 dpc (days post coitum) and 2, 28, 90, 120, 180 dpn (days postnatal) using Solexa sequencing technology. Our results extend the repertoire of pig miRNAome to 247 known miRNAs processed from 210 pre-miRNAs and 297 candidate novel miRNAs through comparison with known miRNAs in the miRBase. Expression analysis of the 15 most abundant miRNAs in every library indicated that functional miRNAome may be smaller and tend to be highly expressed. A series of muscle-related miRNAs summarized in our study present different patterns between myofibers formation phase and muscle maturation phase, providing valuable reference for investigation of functional miRNAs during skeletal muscle development. Analysis of temporal profiles of miRNA expression identifies 18 novel candidate myogenic miRNAs in pig, which might provide new insight into regulation mechanism mediated by miRNAs underlying muscle development.

Indexed as

AnimalsDatabases, GeneticFemaleGene Expression Regulation, DevelopmentalInverted Repeat SequencesMaleMicroRNAsMuscle DevelopmentMuscle, SkeletalPolymerase Chain ReactionSequence Analysis, RNASus scrofaTranscriptomeMicroRNAs

Identifiers

PMID24039761
PMCPMC3770649

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Registered trials

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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.