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Computational prediction of miRNAs and their targets in Phaseolus vulgaris using simple sequence repeat signatures

Overview of attention for article published in BMC Plant Biology, June 2015
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Title
Computational prediction of miRNAs and their targets in Phaseolus vulgaris using simple sequence repeat signatures
Published in
BMC Plant Biology, June 2015
DOI 10.1186/s12870-015-0516-3
Pubmed ID
Authors

Chandran Nithin, Nisha Patwa, Amal Thomas, Ranjit Prasad Bahadur, Jolly Basak

Abstract

MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99 % probability range derived from the available data. We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80 % sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20. There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 2%
Brazil 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 31%
Researcher 10 20%
Student > Bachelor 7 14%
Student > Master 5 10%
Student > Doctoral Student 3 6%
Other 4 8%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 41%
Biochemistry, Genetics and Molecular Biology 12 24%
Computer Science 3 6%
Physics and Astronomy 1 2%
Earth and Planetary Sciences 1 2%
Other 2 4%
Unknown 11 22%
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 27 January 2016.
All research outputs
#14,815,222
of 22,811,321 outputs
Outputs from BMC Plant Biology
#1,275
of 3,245 outputs
Outputs of similar age
#145,992
of 264,930 outputs
Outputs of similar age from BMC Plant Biology
#28
of 62 outputs
Altmetric has tracked 22,811,321 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,245 research outputs from this source. They receive a mean Attention Score of 3.0. This one has gotten more attention than average, scoring higher than 54% 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 264,930 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 62 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.