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RES-Scanner: a software package for genome-wide identification of RNA-editing sites

Overview of attention for article published in Giga Science, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
8 X users
peer_reviews
1 peer review site
facebook
1 Facebook page

Citations

dimensions_citation
56 Dimensions

Readers on

mendeley
85 Mendeley
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Title
RES-Scanner: a software package for genome-wide identification of RNA-editing sites
Published in
Giga Science, August 2016
DOI 10.1186/s13742-016-0143-4
Pubmed ID
Authors

Zongji Wang, Jinmin Lian, Qiye Li, Pei Zhang, Yang Zhou, Xiaoyu Zhan, Guojie Zhang

Abstract

High-throughput sequencing (HTS) provides a powerful solution for the genome-wide identification of RNA-editing sites. However, it remains a great challenge to distinguish RNA-editing sites from genetic variants and technical artifacts caused by sequencing or read-mapping errors. Here we present RES-Scanner, a flexible and efficient software package that detects and annotates RNA-editing sites using matching RNA-seq and DNA-seq data from the same individuals or samples. RES-Scanner allows the use of both raw HTS reads and pre-aligned reads in BAM format as inputs. When inputs are HTS reads, RES-Scanner can invoke the BWA mapper to align reads to the reference genome automatically. To rigorously identify potential false positives resulting from genetic variants, we have equipped RES-Scanner with sophisticated statistical models to infer the reliability of homozygous genotypes called from DNA-seq data. These models are applicable to samples from either single individuals or a pool of multiple individuals if the ploidy information is known. In addition, RES-Scanner implements statistical tests to distinguish genuine RNA-editing sites from sequencing errors, and provides a series of sophisticated filtering options to remove false positives resulting from mapping errors. Finally, RES-Scanner can improve the completeness and accuracy of editing site identification when the data of multiple samples are available. RES-Scanner, as a software package written in the Perl programming language, provides a comprehensive solution that addresses read mapping, homozygous genotype calling, de novo RNA-editing site identification and annotation for any species with matching RNA-seq and DNA-seq data. The package is freely available.

X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 85 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 22%
Student > Master 12 14%
Researcher 11 13%
Student > Bachelor 8 9%
Student > Doctoral Student 4 5%
Other 12 14%
Unknown 19 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 39%
Agricultural and Biological Sciences 20 24%
Computer Science 4 5%
Immunology and Microbiology 2 2%
Engineering 2 2%
Other 3 4%
Unknown 21 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 31 October 2017.
All research outputs
#1,862,810
of 25,394,764 outputs
Outputs from Giga Science
#353
of 1,168 outputs
Outputs of similar age
#32,415
of 354,687 outputs
Outputs of similar age from Giga Science
#7
of 11 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,168 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one has gotten more attention than average, scoring higher than 69% 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 354,687 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.