Evidence map›Paper›PMID 30999850›Full record

ArticleBMC genomics2019

Analysis of microRNAs, phased small interfering RNAs and their potential targets in Rosarugosa Thunb.

Junqiang Guo, Qingyi Wang, Li Liu, Shuchao Ren, Shipeng Li, Peiran Liao, Zhigang Zhao, Chenyu Lu, Bingbing Jiang, Ramanjulu Sunkar and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
9.6field-weighted citation impact, top 2% 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

7 citing papers in PubMed, 27 citations in OpenAlex.

  1. Phased secondary small interfering RNAs inNAR genomics and bioinformatics · 2023
    Article
  2. Article
  3. RoseAP: an analytical platform for gene function ofFrontiers in plant science · 2023
    Article
  4. Article
  5. Article
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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

11 authors at 2 institutions in 2 countries.

Junqiang GuoFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.
Qingyi WangFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.
Li LiuFaculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, China.
Shuchao RenFaculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, China.
Shipeng LiFaculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, China.
Peiran LiaoFaculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, 650500, China.
Zhigang ZhaoYunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, China.
Chenyu LuYunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, China.
Bingbing JiangYunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, China.
Ramanjulu SunkarDepartment of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, 74078, Oklahoma, USA.
Yun ZhengFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China. zhengyun5488@gmail.com.ORCID http://orcid.org/0000-0003-4292-9806
Kunming University of Science and Technology · CNOklahoma State University · US

Funding

National Natural Science Foundation of China 31460295National Natural Science Foundation of China 31760314
6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) are small non-coding RNAs that play important roles by regulating other genes. Rosa rugosa Thunb. is an important ornamental and edible plant, yet there are only a few studies on the miRNAs and their functions in R. rugosa.

resultsWe sequenced 10 samll RNA profiles from the roots, petals, pollens, stamens, and leaves and 4 RNA-seq profiles in leaves and petals to analysis miRNA, phasiRNAs and mRNAs in R. rugosa. In addition, we acquired a degradome sequencing profile from leaf of R. rugosa to identify miRNA and phasiRNA targets using the SeqTar algorithm. We have identified 321 conserved miRNA homologs including primary transcripts for 25 conserved miRNAs, and 22 novel miRNAs. We identified 592 putative targets of the conserved miRNAs or tasiRNAs that showed significant accumulations of degradome reads. We found differential expression patterns of conserved miRNAs in five different tissues of R. rugosa. We identified three hundred and thirty nine 21 nucleotide (nt) PHAS loci, and forty nine 24 nt PHAS loci, respectively. Our results suggest that miR482 triggers generations of phasiRNAs by targeting nucleotide-binding, leucine-rich repeat (NB-LRR) disease resistance genes in R. rugosa. Our results also suggest that the deregulated genes in leaves and petals are significantly enriched in GO terms and KEGG pathways related to metabolic processes and photosynthesis.

conclusionsThese results significantly enhanced our knowledge of the miRNAs and phasiRNAs, as well as their potential functions, in R. rugosa.

Indexed as

Computational BiologyGene Expression Regulation, PlantHigh-Throughput Nucleotide SequencingMicroRNAsPlant LeavesPlant ProteinsRNA, PlantRNA, Small InterferingRosaMicroRNAsPlant ProteinsRNA, PlantRNA, Small InterferingBioinformaticsDegradomeHigh-throughput sequencingmicroRNAPhased small interfering RNA (phasiRNA)Rosa rugosa Thunb.SeqTar

Identifiers

PMID30999850
PMCPMC7394236
OpenAlexW2940397985

What OpenQuestion holds

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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.