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ETE: a python Environment for Tree Exploration

Overview of attention for article published in BMC Bioinformatics, January 2010
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
1 X user
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
344 Dimensions

Readers on

mendeley
321 Mendeley
citeulike
20 CiteULike
connotea
2 Connotea
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Title
ETE: a python Environment for Tree Exploration
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-24
Pubmed ID
Authors

Jaime Huerta-Cepas, Joaquín Dopazo, Toni Gabaldón

Abstract

Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 321 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
United Kingdom 1 <1%
Unknown 319 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 3%
Student > Bachelor 8 2%
Student > Master 4 1%
Researcher 3 <1%
Student > Doctoral Student 2 <1%
Other 6 2%
Unknown 287 89%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 2%
Medicine and Dentistry 4 1%
Chemistry 4 1%
Biochemistry, Genetics and Molecular Biology 3 <1%
Computer Science 3 <1%
Other 11 3%
Unknown 289 90%
Attention Score in Context

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 30 September 2015.
All research outputs
#7,206,491
of 22,778,347 outputs
Outputs from BMC Bioinformatics
#2,859
of 7,277 outputs
Outputs of similar age
#47,581
of 164,869 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 61 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,277 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 59% 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 164,869 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 70% of its contemporaries.
We're also able to compare this research output to 61 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 73% of its contemporaries.