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Identification of MicroRNA Targets of Capsicum spp. Using MiRTrans—a Trans-Omics Approach

Overview of attention for article published in Frontiers in Plant Science, April 2017
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Title
Identification of MicroRNA Targets of Capsicum spp. Using MiRTrans—a Trans-Omics Approach
Published in
Frontiers in Plant Science, April 2017
DOI 10.3389/fpls.2017.00495
Pubmed ID
Authors

Lu Zhang, Cheng Qin, Junpu Mei, Xiaocui Chen, Zhiming Wu, Xirong Luo, Jiaowen Cheng, Xiangqun Tang, Kailin Hu, Shuai C. Li

Abstract

The microRNA (miRNA) can regulate the transcripts that are involved in eukaryotic cell proliferation, differentiation, and metabolism. Especially for plants, our understanding of miRNA targets, is still limited. Early attempts of prediction on sequence alignments have been plagued by enormous false positives. It is helpful to improve target prediction specificity by incorporating the other data sources such as the dependency between miRNA and transcript expression or even cleaved transcripts by miRNA regulations, which are referred to as trans-omics data. In this paper, we developed MiRTrans (Prediction of MiRNA targets by Trans-omics data) to explore miRNA targets by incorporating miRNA sequencing, transcriptome sequencing, and degradome sequencing. MiRTrans consisted of three major steps. First, the target transcripts of miRNAs were predicted by scrutinizing their sequence characteristics and collected as an initial potential targets pool. Second, false positive targets were eliminated if the expression of miRNA and its targets were weakly correlated by lasso regression. Third, degradome sequencing was utilized to capture the miRNA targets by examining the cleaved transcripts that regulated by miRNAs. Finally, the predicted targets from the second and third step were combined by Fisher's combination test. MiRTrans was applied to identify the miRNA targets for Capsicum spp. (i.e., pepper). It can generate more functional miRNA targets than sequence-based predictions by evaluating functional enrichment. MiRTrans identified 58 miRNA-transcript pairs with high confidence from 18 miRNA families conserved in eudicots. Most of these targets were transcription factors; this lent support to the role of miRNA as key regulator in pepper. To our best knowledge, this work is the first attempt to investigate the miRNA targets of pepper, as well as their regulatory networks. Surprisingly, only a small proportion of miRNA-transcript pairs were shared between degradome sequencing and expression dependency predictions, suggesting that miRNA targets predicted by a single technology alone may be prone to report false negatives.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 26%
Researcher 4 21%
Student > Ph. D. Student 3 16%
Student > Postgraduate 2 11%
Professor > Associate Professor 1 5%
Other 1 5%
Unknown 3 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 42%
Biochemistry, Genetics and Molecular Biology 4 21%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Computer Science 1 5%
Social Sciences 1 5%
Other 0 0%
Unknown 4 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 May 2017.
All research outputs
#14,341,817
of 22,968,808 outputs
Outputs from Frontiers in Plant Science
#8,217
of 20,396 outputs
Outputs of similar age
#173,584
of 310,147 outputs
Outputs of similar age from Frontiers in Plant Science
#285
of 556 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 20,396 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 55% 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 310,147 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 556 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.