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Identification of streptococcal small RNAs that are putative targets of RNase III through bioinformatics analysis of RNA sequencing data

Overview of attention for article published in BMC Bioinformatics, December 2017
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Title
Identification of streptococcal small RNAs that are putative targets of RNase III through bioinformatics analysis of RNA sequencing data
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1897-0
Pubmed ID
Authors

Ethan C. Rath, Stephanie Pitman, Kyu Hong Cho, Yongsheng Bai

Abstract

Small noncoding regulatory RNAs (sRNAs) are post-transcriptional regulators, regulating mRNAs, proteins, and DNA in bacteria. One class of sRNAs, trans-acting sRNAs, are the most abundant sRNAs transcribed from the intergenic regions (IGRs) of the bacterial genome. In Streptococcus pyogenes, a common and potentially deadly pathogen, many sRNAs have been identified, but only a few have been studied. The goal of this study is to identify trans-acting sRNAs that can be substrates of RNase III. The endoribonuclease RNase III cleaves double stranded RNAs, which can be formed during the interaction between an sRNA and target mRNAs. For this study, we created an RNase III null mutant of Streptococcus pyogenes and its RNA sequencing (RNA-Seq) data were analyzed and compared to that of the wild-type. First, we developed a custom script that can detect intergenic regions of the S. pyogenes genome. A differential expression analysis with Cufflinks and Stringtie was then performed to identify the intergenic regions whose expression was influenced by the RNase III gene deletion. This analysis yielded 12 differentially expressed regions with >|2| fold change and p ≤ 0.05. Using Artemis and Bamview genome viewers, these regions were visually verified leaving 6 putative sRNAs. This study not only expanded our knowledge on novel sRNAs but would also give us new insight into sRNA degradation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 24%
Student > Master 3 18%
Student > Ph. D. Student 3 18%
Student > Doctoral Student 1 6%
Researcher 1 6%
Other 1 6%
Unknown 4 24%
Readers by discipline Count As %
Computer Science 3 18%
Immunology and Microbiology 3 18%
Agricultural and Biological Sciences 3 18%
Engineering 2 12%
Business, Management and Accounting 1 6%
Other 1 6%
Unknown 4 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 January 2018.
All research outputs
#20,458,307
of 23,015,156 outputs
Outputs from BMC Bioinformatics
#6,890
of 7,315 outputs
Outputs of similar age
#377,608
of 441,976 outputs
Outputs of similar age from BMC Bioinformatics
#121
of 143 outputs
Altmetric has tracked 23,015,156 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,315 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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