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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | 20% |
| Norway | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
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 % |
|---|---|---|
| United States | 3 | 6% |
| Germany | 2 | 4% |
| Brazil | 2 | 4% |
| Sweden | 1 | 2% |
| France | 1 | 2% |
| Unknown | 38 | 81% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 34% |
| Researcher | 7 | 15% |
| Professor | 6 | 13% |
| Student > Bachelor | 3 | 6% |
| Student > Master | 3 | 6% |
| Other | 8 | 17% |
| Unknown | 4 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 51% |
| Biochemistry, Genetics and Molecular Biology | 5 | 11% |
| Computer Science | 4 | 9% |
| Arts and Humanities | 2 | 4% |
| Engineering | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 6 | 13% |
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 07 February 2024.
All research outputs
#5,440,656
of 22,761,738 outputs
Outputs from BMC Bioinformatics
#1,949
of 7,273 outputs
Outputs of similar age
#52,316
of 236,046 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 109 outputs
Altmetric has tracked 22,761,738 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,273 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 72% 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 236,046 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 77% of its contemporaries.
We're also able to compare this research output to 109 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 68% of its contemporaries.