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gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens

Overview of attention for article published in Genome Biology (Online Edition), October 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

twitter
7 tweeters

Citations

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28 Dimensions

Readers on

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50 Mendeley
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Title
gespeR: a statistical model for deconvoluting off-target-confounded RNA interference screens
Published in
Genome Biology (Online Edition), October 2015
DOI 10.1186/s13059-015-0783-1
Pubmed ID
Authors

Fabian Schmich, Ewa Szczurek, Saskia Kreibich, Sabrina Dilling, Daniel Andritschke, Alain Casanova, Shyan Huey Low, Simone Eicher, Simone Muntwiler, Mario Emmenlauer, Pauli Rämö, Raquel Conde-Alvarez, Christian von Mering, Wolf-Dietrich Hardt, Christoph Dehio, Niko Beerenwinkel

Abstract

Small interfering RNAs (siRNAs) exhibit strong off-target effects, which confound the gene-level interpretation of RNA interference screens and thus limit their utility for functional genomics studies. Here, we present gespeR, a statistical model for reconstructing individual, gene-specific phenotypes. Using 115,878 siRNAs, single and pooled, from three companies in three pathogen infection screens, we demonstrate that deconvolution of image-based phenotypes substantially improves the reproducibility between independent siRNA sets targeting the same genes. Genes selected and prioritized by gespeR are validated and shown to constitute biologically relevant components of pathogen entry mechanisms and TGF-β signaling. gespeR is available as a Bioconductor R-package.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 40%
Student > Ph. D. Student 12 24%
Student > Bachelor 6 12%
Student > Master 2 4%
Other 2 4%
Other 4 8%
Unknown 4 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 30%
Agricultural and Biological Sciences 14 28%
Engineering 3 6%
Immunology and Microbiology 3 6%
Computer Science 3 6%
Other 6 12%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 October 2015.
All research outputs
#4,415,756
of 15,282,649 outputs
Outputs from Genome Biology (Online Edition)
#2,394
of 3,304 outputs
Outputs of similar age
#72,330
of 253,637 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 15,282,649 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 3,304 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.9. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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 253,637 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them