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Non-synonymous variations in cancer and their effects on the human proteome: workflow for NGS data biocuration and proteome-wide analysis of TCGA data

Overview of attention for article published in BMC Bioinformatics, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
8 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
69 Mendeley
citeulike
6 CiteULike
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Title
Non-synonymous variations in cancer and their effects on the human proteome: workflow for NGS data biocuration and proteome-wide analysis of TCGA data
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-28
Pubmed ID
Authors

Charles Cole, Konstantinos Krampis, Konstantinos Karagiannis, Jonas S Almeida, William J Faison, Mona Motwani, Quan Wan, Anton Golikov, Yang Pan, Vahan Simonyan, Raja Mazumder

Abstract

Next-generation sequencing (NGS) technologies have resulted in petabytes of scattered data, decentralized in archives, databases and sometimes in isolated hard-disks which are inaccessible for browsing and analysis. It is expected that curated secondary databases will help organize some of this Big Data thereby allowing users better navigate, search and compute on it.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 2 3%
Sweden 1 1%
Sri Lanka 1 1%
Netherlands 1 1%
Unknown 61 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 29%
Student > Ph. D. Student 16 23%
Student > Bachelor 7 10%
Professor > Associate Professor 4 6%
Other 4 6%
Other 15 22%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 43%
Biochemistry, Genetics and Molecular Biology 11 16%
Computer Science 9 13%
Medicine and Dentistry 5 7%
Engineering 3 4%
Other 2 3%
Unknown 9 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 20 June 2014.
All research outputs
#3,414,394
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,439
of 5,420 outputs
Outputs of similar age
#50,324
of 249,061 outputs
Outputs of similar age from BMC Bioinformatics
#53
of 217 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 73% 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 249,061 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 79% of its contemporaries.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.