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AliGROOVE – visualization of heterogeneous sequence divergence within multiple sequence alignments and detection of inflated branch support

Overview of attention for article published in BMC Bioinformatics, August 2014
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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 (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
5 tweeters
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
35 Mendeley
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Title
AliGROOVE – visualization of heterogeneous sequence divergence within multiple sequence alignments and detection of inflated branch support
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-294
Pubmed ID
Authors

Patrick Kück, Sandra A Meid, Christian Groß, Johann W Wägele, Bernhard Misof

Abstract

Masking of multiple sequence alignment blocks has become a powerful method to enhance the tree-likeness of the underlying data. However, existing masking approaches are insensitive to heterogeneous sequence divergence which can mislead tree reconstructions. We present AliGROOVE, a new method based on a sliding window and a Monte Carlo resampling approach, that visualizes heterogeneous sequence divergence or alignment ambiguity related to single taxa or subsets of taxa within a multiple sequence alignment and tags suspicious branches on a given tree.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 9%
Brazil 2 6%
Germany 2 6%
Sweden 1 3%
France 1 3%
Unknown 26 74%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 34%
Researcher 5 14%
Professor 5 14%
Student > Master 4 11%
Other 2 6%
Other 6 17%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 63%
Biochemistry, Genetics and Molecular Biology 3 9%
Computer Science 3 9%
Arts and Humanities 2 6%
Earth and Planetary Sciences 1 3%
Other 1 3%
Unknown 3 9%

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 13 September 2019.
All research outputs
#2,768,649
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,229
of 5,420 outputs
Outputs of similar age
#38,173
of 200,180 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 6 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 77th 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 done well, scoring higher than 76% 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 200,180 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 80% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.