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Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE

Overview of attention for article published in BMC Genomics, July 2013
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  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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Title
Cloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE
Published in
BMC Genomics, July 2013
DOI 10.1186/1471-2164-14-494
Pubmed ID
Authors

Quang M Trinh, Fei-Yang Arthur Jen, Ziru Zhou, Kar Ming Chu, Marc D Perry, Ellen T Kephart, Sergio Contrino, Peter Ruzanov, Lincoln D Stein

Abstract

Funded by the National Institutes of Health (NIH), the aim of the Model Organism ENCyclopedia of DNA Elements (modENCODE) project is to provide the biological research community with a comprehensive encyclopedia of functional genomic elements for both model organisms C. elegans (worm) and D. melanogaster (fly). With a total size of just under 10 terabytes of data collected and released to the public, one of the challenges faced by researchers is to extract biologically meaningful knowledge from this large data set. While the basic quality control, pre-processing, and analysis of the data has already been performed by members of the modENCODE consortium, many researchers will wish to reinterpret the data set using modifications and enhancements of the original protocols, or combine modENCODE data with other data sets. Unfortunately this can be a time consuming and logistically challenging proposition.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

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 %
United States 2 5%
China 1 2%
Brazil 1 2%
Japan 1 2%
Russia 1 2%
Unknown 37 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 33%
Student > Ph. D. Student 8 19%
Professor > Associate Professor 7 16%
Student > Master 6 14%
Professor 3 7%
Other 3 7%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 60%
Computer Science 6 14%
Biochemistry, Genetics and Molecular Biology 3 7%
Engineering 2 5%
Sports and Recreations 1 2%
Other 3 7%
Unknown 2 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 29 November 2013.
All research outputs
#13,859,387
of 23,881,329 outputs
Outputs from BMC Genomics
#4,935
of 10,793 outputs
Outputs of similar age
#105,208
of 200,844 outputs
Outputs of similar age from BMC Genomics
#47
of 120 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 52% 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 200,844 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.