Evidence map›Paper›PMID 31877985›Full record

ArticleGenes2019

Identification of Appropriate Reference Genes for Normalizing miRNA Expression in Citrus Infected by

Shiheng Lyu, Ying Yu, Shirong Xu, Weiwei Cai, Guixin Chen, Jianjun Chen, Dongming Pan, Wenqin She

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.4field-weighted citation impact, top 37% 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

8 citing papers in PubMed, 13 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 2 institutions in 2 countries.

Shiheng LyuCollege of Horticulture, Fujian Agriculture and Forestry University, Fuijan 350002, China.
Ying YuCollege of Horticulture, Fujian Agriculture and Forestry University, Fuijan 350002, China.
Shirong XuCollege of Horticulture, Fujian Agriculture and Forestry University, Fuijan 350002, China.
Weiwei CaiCollege of Crop Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Guixin ChenCollege of Horticulture, Fujian Agriculture and Forestry University, Fuijan 350002, China.
Jianjun ChenDepartment of Environmental Horticulture and Mid-Florida Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Apopka, FL 32703, USA.
Dongming PanCollege of Horticulture, Fujian Agriculture and Forestry University, Fuijan 350002, China.
Wenqin SheCollege of Horticulture, Fujian Agriculture and Forestry University, Fuijan 350002, China.
Fujian Agriculture and Forestry University · CNUniversity of Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are short noncoding RNA molecules that regulate gene expression at the posttranscriptional level. Reverse transcription-quantitative PCR (RT-qPCR) is one of the most common methods used for quantification of miRNA expression, and the levels of expression are normalized by comparing with reference genes. Thus, the selection of reference genes is critically important for accurate quantification. The present study was intended to identify appropriate miRNA reference genes for normalizing the level of miRNA expression in

Indexed as

CitrusCitrus sinensisGene ExpressionGene Expression ProfilingMicroRNAsPlant DiseasesReal-Time Polymerase Chain ReactionReference StandardsVirulenceXanthomonasMicroRNAscitrus cankerCitrus reticulataCitrus sinensismicroRNAPoly(A) RT-qPCRreference genestem-loop RT-qPCRXanthomonas citri subsp. citri

Identifiers

PMID31877985
PMCPMC7017248
OpenAlexW2996310679

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.