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A Comprehensive Prescription for Plant miRNA Identification

Overview of attention for article published in Frontiers in Plant Science, January 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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Title
A Comprehensive Prescription for Plant miRNA Identification
Published in
Frontiers in Plant Science, January 2017
DOI 10.3389/fpls.2016.02058
Pubmed ID
Authors

Burcu Alptekin, Bala A. Akpinar, Hikmet Budak

Abstract

microRNAs (miRNAs) are tiny ribo-regulatory molecules involved in various essential pathways for persistence of cellular life, such as development, environmental adaptation, and stress response. In recent years, miRNAs have become a major focus in molecular biology because of their functional and diagnostic importance. This interest in miRNA research has resulted in the development of many specific software and pipelines for the identification of miRNAs and their specific targets, which is the key for the elucidation of miRNA-modulated gene expression. While the well-recognized importance of miRNAs in clinical research pushed the emergence of many useful computational identification approaches in animals, available software and pipelines are fewer for plants. Additionally, existing approaches suffers from mis-identification and annotation of plant miRNAs since the miRNA mining process for plants is highly prone to false-positives, particularly in cereals which have a highly repetitive genome. Our group developed a homology-based in silico miRNA identification approach for plants, which utilizes two Perl scripts "SUmirFind" and "SUmirFold" and since then, this method helped identify many miRNAs particularly from crop species such as Triticum or Aegliops. Herein, we describe a comprehensive updated guideline by the implementation of two new scripts, "SUmirPredictor" and "SUmirLocator," and refinements to our previous method in order to identify genuine miRNAs with increased sensitivity in consideration of miRNA identification problems in plants. Recent updates enable our method to provide more reliable and precise results in an automated fashion in addition to solutions for elimination of most false-positive predictions, miRNA naming and miRNA mis-annotation. It also provides a comprehensive view to genome/transcriptome-wide location of miRNA precursors as well as their association with transposable elements. The "SUmirPredictor" and "SUmirLocator" scripts are freely available together with a reference high-confidence plant miRNA list.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 <1%
United States 1 <1%
Unknown 125 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 27%
Researcher 24 19%
Student > Bachelor 14 11%
Student > Master 11 9%
Student > Doctoral Student 7 6%
Other 15 12%
Unknown 22 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 48%
Biochemistry, Genetics and Molecular Biology 24 19%
Computer Science 6 5%
Engineering 2 2%
Environmental Science 2 2%
Other 3 2%
Unknown 29 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2021.
All research outputs
#12,820,770
of 22,947,506 outputs
Outputs from Frontiers in Plant Science
#5,218
of 20,366 outputs
Outputs of similar age
#195,337
of 419,047 outputs
Outputs of similar age from Frontiers in Plant Science
#136
of 510 outputs
Altmetric has tracked 22,947,506 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 20,366 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 419,047 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 510 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.