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Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology

Overview of attention for article published in PeerJ, September 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
39 tweeters
peer_reviews
1 peer review site
facebook
2 Facebook pages
googleplus
3 Google+ users

Citations

dimensions_citation
101 Dimensions

Readers on

mendeley
171 Mendeley
citeulike
4 CiteULike
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Title
Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology
Published in
PeerJ, September 2013
DOI 10.7717/peerj.167
Pubmed ID
Authors

Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz, Leighton Pritchard, Cock PJ, Grüning BA, Paszkiewicz K, Pritchard L, Björn A. Grüning

Abstract

The Galaxy Project offers the popular web browser-based platform Galaxy for running bioinformatics tools and constructing simple workflows. Here, we present a broad collection of additional Galaxy tools for large scale analysis of gene and protein sequences. The motivating research theme is the identification of specific genes of interest in a range of non-model organisms, and our central example is the identification and prediction of "effector" proteins produced by plant pathogens in order to manipulate their host plant. This functional annotation of a pathogen's predicted capacity for virulence is a key step in translating sequence data into potential applications in plant pathology. This collection includes novel tools, and widely-used third-party tools such as NCBI BLAST+ wrapped for use within Galaxy. Individual bioinformatics software tools are typically available separately as standalone packages, or in online browser-based form. The Galaxy framework enables the user to combine these and other tools to automate organism scale analyses as workflows, without demanding familiarity with command line tools and scripting. Workflows created using Galaxy can be saved and are reusable, so may be distributed within and between research groups, facilitating the construction of a set of standardised, reusable bioinformatic protocols. The Galaxy tools and workflows described in this manuscript are open source and freely available from the Galaxy Tool Shed (http://usegalaxy.org/toolshed or http://toolshed.g2.bx.psu.edu).

Twitter Demographics

The data shown below were collected from the profiles of 39 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 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Brazil 4 2%
United Kingdom 2 1%
France 2 1%
Sweden 1 <1%
Germany 1 <1%
Switzerland 1 <1%
Argentina 1 <1%
Colombia 1 <1%
Other 0 0%
Unknown 154 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 52 30%
Student > Ph. D. Student 32 19%
Student > Master 18 11%
Student > Bachelor 17 10%
Other 11 6%
Other 24 14%
Unknown 17 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 79 46%
Biochemistry, Genetics and Molecular Biology 35 20%
Computer Science 16 9%
Engineering 7 4%
Environmental Science 4 2%
Other 9 5%
Unknown 21 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 08 October 2018.
All research outputs
#725,319
of 17,351,915 outputs
Outputs from PeerJ
#875
of 10,267 outputs
Outputs of similar age
#8,163
of 174,614 outputs
Outputs of similar age from PeerJ
#2
of 12 outputs
Altmetric has tracked 17,351,915 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,267 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.7. This one has done particularly well, scoring higher than 91% 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 174,614 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.