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SILVA tree viewer: interactive web browsing of the SILVA phylogenetic guide trees

Overview of attention for article published in BMC Bioinformatics, September 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

twitter
42 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
47 Mendeley
citeulike
1 CiteULike
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Title
SILVA tree viewer: interactive web browsing of the SILVA phylogenetic guide trees
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1841-3
Pubmed ID
Authors

Alan Beccati, Jan Gerken, Christian Quast, Pelin Yilmaz, Frank Oliver Glöckner

Abstract

Phylogenetic trees are an important tool to study the evolutionary relationships among organisms. The huge amount of available taxa poses difficulties in their interactive visualization. This hampers the interaction with the users to provide feedback for the further improvement of the taxonomic framework. The SILVA Tree Viewer is a web application designed for visualizing large phylogenetic trees without requiring the download of any software tool or data files. The SILVA Tree Viewer is based on Web Geographic Information Systems (Web-GIS) technology with a PostgreSQL backend. It enables zoom and pan functionalities similar to Google Maps. The SILVA Tree Viewer enables access to two phylogenetic (guide) trees provided by the SILVA database: the SSU Ref NR99 inferred from high-quality, full-length small subunit sequences, clustered at 99% sequence identity and the LSU Ref inferred from high-quality, full-length large subunit sequences. The Tree Viewer provides tree navigation, search and browse tools as well as an interactive feedback system to collect any kinds of requests ranging from taxonomy to data curation and improving the tool itself.

Twitter Demographics

The data shown below were collected from the profiles of 42 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 15%
Researcher 7 15%
Student > Master 7 15%
Student > Bachelor 5 11%
Student > Postgraduate 3 6%
Other 10 21%
Unknown 8 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 26%
Biochemistry, Genetics and Molecular Biology 9 19%
Immunology and Microbiology 4 9%
Social Sciences 2 4%
Environmental Science 2 4%
Other 8 17%
Unknown 10 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 26 February 2022.
All research outputs
#1,242,648
of 21,903,307 outputs
Outputs from BMC Bioinformatics
#195
of 7,063 outputs
Outputs of similar age
#26,616
of 298,417 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 32 outputs
Altmetric has tracked 21,903,307 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,063 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 done particularly well, scoring higher than 97% 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 298,417 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 91% of its contemporaries.
We're also able to compare this research output to 32 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 96% of its contemporaries.