Evidence map›Paper›PMID 29764387›Full record

ArticleBMC genomics2018

Genome-wide identification and comprehensive analysis of microRNAs and phased small interfering RNAs in watermelon.

Li Liu, Shuchao Ren, Junqiang Guo, Qingyi Wang, Xiaotuo Zhang, Peiran Liao, Shipeng Li, Ramanjulu Sunkar, Yun Zheng

Abstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Phased secondary small interfering RNAs inNAR genomics and bioinformatics · 2023
    Article
  4. Are herbal sRNAs really novel precision medicines?Science China. Life sciences · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
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

9 authors.

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.
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.
Xiaotuo ZhangSchool of Life Sciences, Fudan University, Shanghai, 200433, China.
Peiran LiaoFaculty 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.
Ramanjulu SunkarDepartment of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, 74078, OK, USA.
Yun ZhengYunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, 650500, China. zhengyun5488@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) are a class of endogenous small non-coding RNAs involved in the post-transcriptional gene regulation and play a critical role in plant growth, development and stress responses. Watermelon (Citrullus lanatus L.) is one of the important agricultural crops worldwide. However, the watermelon miRNAs and phasiRNAs and their functions are not well explored.

resultsHere we carried out computational and experimental analysis of miRNAs and phased small interfering RNAs (phasiRNAs) in watermelon by analyzing 14 small RNA profiles from roots, leaves, androecium, petals, and fruits, and one published small RNA profile of mixed tissues. To identify the targets of miRNAs and phasiRNAs, we generated a degradome profile for watermelon leaf which is analyzed using the SeqTar algorithm. We identified 97 conserved pre-miRNAs, of which 58 have not been reported previously and 348 conserved mature miRNAs without precursors. We also found 9 novel pre-miRNAs encoding 18 mature miRNAs. One hundred and one 21 nucleotide (nt) PHAS loci, and two hundred and forty one 24 nt PHAS loci were also identified. We identified 127 conserved targets of the conserved miRNAs and TAS3-derived tasiRNAs by analyzing a degradome profile of watermelon leaf.

conclusionsThe presented results provide a comprehensive view of small regulatory RNAs and their targets in watermelon.

Indexed as

CitrullusComputational BiologyFlowersFruitGene Expression Regulation, PlantGenome, PlantHigh-Throughput Nucleotide SequencingMicroRNAsPlant LeavesRNA, PlantRNA, Small InterferingSequence Analysis, RNAMicroRNAsRNA, PlantRNA, Small InterferingBioinformaticsCitrullus lanatus L.High-throughput sequencingmicroRNAPHASPhased small interfering RNA (phasiRNA)

Identifiers

PMID29764387
PMCPMC5954288

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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.