Evidence map›Paper›PMID 24973888›Full record

ArticleMolecular biology reports2014

Identification of suitable endogenous control genes for quantitative RT-PCR analysis of miRNA in bovine solid tissues.

Dongwei Li, Hongyu Liu, Yunsheng Li, Manman Yang, Changqing Qu, Yunhai Zhang, Ya Liu, Xiaorong Zhang

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular biology reports, 2014. 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
1.1field-weighted citation impact, top 22% 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, 23 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Dongwei LiCollege of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, China.
Hongyu Liu
Yunsheng Li
Manman Yang
Changqing Qu
Yunhai Zhang
Ya Liu
Xiaorong Zhang
Anhui Agricultural University · CNAnhui Water Conservancy and Hydropower Survey and Design Institute · CNFuyang Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) has become the preferred technique for studying low-abundance RNA expression. Proper normalization is a critical but often underappreciated aspect of quantitative gene expression analysis; popular endogenous control genes are usually selected with little knowledge of their real suitability. To date, there are very few reports regarding the general validation of endogenous control genes for microRNA (miRNA) expression analysis in bovine tissue. In the present study, eight candidate reference genes (U6, 18S rRNA, GAPDH, ACTB, miR-191, miR-15a, miR-18a, let-7f) were tested for use as normalizers of bovine miRNA in RT-qPCR assays. Their selection was based on publicly available data concerning normalization, hierarchical clustering and sequencing. Three of the genes (miR-191, U6-1 and let-7f) were found to be highly consistent in their expression across eight different bovine solid tissues. It is commonly accepted that gene expression studies should be normalized using more than one endogenous control gene. Based on our results, we propose using the combined results for miR-191, U6-1 and let-7f as the endogenous control for normalization of miRNA levels in qRT-PCR analysis of diverse bovine tissues. This result could act a guideline for future work on bovine miRNA expression.

Indexed as

Gene Expression ProfilingAnimalsCattleGene ExpressionMicroRNAsOrgan SpecificityRNA StabilityTranscriptomeMicroRNAs

Identifiers

PMID24973888
OpenAlexW2097045181

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.