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DEApp: an interactive web interface for differential expression analysis of next generation sequence data

Overview of attention for article published in Source Code for Biology and Medicine, February 2017
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Mentioned by

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2 tweeters

Citations

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20 Dimensions

Readers on

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43 Mendeley
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Title
DEApp: an interactive web interface for differential expression analysis of next generation sequence data
Published in
Source Code for Biology and Medicine, February 2017
DOI 10.1186/s13029-017-0063-4
Pubmed ID
Authors

Yan Li, Jorge Andrade

Abstract

A growing trend in the biomedical community is the use of Next Generation Sequencing (NGS) technologies in genomics research. The complexity of downstream differential expression (DE) analysis is however still challenging, as it requires sufficient computer programing and command-line knowledge. Furthermore, researchers often need to evaluate and visualize interactively the effect of using differential statistical and error models, assess the impact of selecting different parameters and cutoffs, and finally explore the overlapping consensus of cross-validated results obtained with different methods. This represents a bottleneck that slows down or impedes the adoption of NGS technologies in many labs. We developed DEApp, an interactive and dynamic web application for differential expression analysis of count based NGS data. This application enables models selection, parameter tuning, cross validation and visualization of results in a user-friendly interface. DEApp enables labs with no access to full time bioinformaticians to exploit the advantages of NGS applications in biomedical research. This application is freely available at https://yanli.shinyapps.io/DEAppand https://gallery.shinyapps.io/DEApp.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 10 23%
Student > Master 6 14%
Student > Bachelor 5 12%
Professor > Associate Professor 2 5%
Other 7 16%
Unknown 2 5%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 33%
Agricultural and Biological Sciences 11 26%
Computer Science 4 9%
Engineering 3 7%
Immunology and Microbiology 2 5%
Other 4 9%
Unknown 5 12%

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 13 February 2017.
All research outputs
#4,920,551
of 9,059,795 outputs
Outputs from Source Code for Biology and Medicine
#76
of 118 outputs
Outputs of similar age
#175,343
of 310,283 outputs
Outputs of similar age from Source Code for Biology and Medicine
#2
of 5 outputs
Altmetric has tracked 9,059,795 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 118 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 28th percentile – i.e., 28% 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 310,283 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.