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JACUSA: site-specific identification of RNA editing events from replicate sequencing data

Overview of attention for article published in BMC Bioinformatics, January 2017
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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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
9 tweeters

Citations

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18 Dimensions

Readers on

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63 Mendeley
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Title
JACUSA: site-specific identification of RNA editing events from replicate sequencing data
Published in
BMC Bioinformatics, January 2017
DOI 10.1186/s12859-016-1432-8
Pubmed ID
Authors

Michael Piechotta, Emanuel Wyler, Uwe Ohler, Markus Landthaler, Christoph Dieterich

Abstract

RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution (A→I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature. We present JACUSA, a versatile one-stop solution to detect single nucleotide variant positions from comparing RNA-DNA and/or RNA-RNA sequencing samples. The performance of JACUSA has been carefully evaluated and compared to other variant callers in an in silico benchmark. JACUSA outperforms other algorithms in terms of the F measure, which combines precision and recall, in all benchmark scenarios. This performance margin is highest for the RNA-RNA comparison scenario. We further validated JACUSA's performance by testing its ability to detect A→I events using sequencing data from a human cell culture experiment and publicly available RNA-seq data from Drosophila melanogaster heads. To this end, we performed whole genome and RNA sequencing of HEK-293 cells on samples with lowered activity of candidate RNA editing enzymes. JACUSA has a higher recall and comparable precision for detecting true editing sites in RDD comparisons of HEK-293 data. Intriguingly, JACUSA captures most A→I events from RRD comparisons of RNA sequencing data derived from Drosophila and HEK-293 data sets. Our software JACUSA detects single nucleotide variants by comparing data from next-generation sequencing experiments (RNA-DNA or RNA-RNA). In practice, JACUSA shows higher recall and comparable precision in detecting A→I sites from RNA-DNA comparisons, while showing higher precision and recall in RNA-RNA comparisons.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Italy 1 2%
Unknown 61 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 24%
Researcher 13 21%
Student > Bachelor 12 19%
Student > Master 7 11%
Professor > Associate Professor 3 5%
Other 7 11%
Unknown 6 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 49%
Agricultural and Biological Sciences 17 27%
Computer Science 3 5%
Engineering 2 3%
Medicine and Dentistry 1 2%
Other 2 3%
Unknown 7 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 24 August 2017.
All research outputs
#1,020,212
of 11,653,629 outputs
Outputs from BMC Bioinformatics
#413
of 4,286 outputs
Outputs of similar age
#43,682
of 322,856 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 140 outputs
Altmetric has tracked 11,653,629 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,286 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 90% 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 322,856 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.