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A reference guide for tree analysis and visualization

Overview of attention for article published in BioData Mining, February 2010
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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)

Mentioned by

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1 X user
patent
1 patent
wikipedia
1 Wikipedia page

Readers on

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721 Mendeley
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10 CiteULike
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2 Connotea
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Title
A reference guide for tree analysis and visualization
Published in
BioData Mining, February 2010
DOI 10.1186/1756-0381-3-1
Pubmed ID
Authors

Georgios A Pavlopoulos, Theodoros G Soldatos, Adriano Barbosa-Silva, Reinhard Schneider

Abstract

The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis.

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 721 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 2%
United Kingdom 8 1%
Brazil 6 <1%
Germany 5 <1%
Belgium 4 <1%
Netherlands 3 <1%
Japan 3 <1%
Australia 3 <1%
Spain 2 <1%
Other 18 2%
Unknown 654 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 159 22%
Researcher 124 17%
Student > Bachelor 97 13%
Student > Master 96 13%
Other 38 5%
Other 106 15%
Unknown 101 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 272 38%
Biochemistry, Genetics and Molecular Biology 103 14%
Computer Science 54 7%
Engineering 38 5%
Environmental Science 21 3%
Other 116 16%
Unknown 117 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 27 September 2022.
All research outputs
#4,317,203
of 23,414,653 outputs
Outputs from BioData Mining
#102
of 314 outputs
Outputs of similar age
#18,224
of 95,324 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 23,414,653 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 314 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 67% 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 95,324 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them