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X Demographics
Mendeley readers
Attention Score in Context
Title |
Systematic exploration of guide-tree topology effects for small protein alignments
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Published in |
BMC Bioinformatics, October 2014
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DOI | 10.1186/1471-2105-15-338 |
Pubmed ID | |
Authors |
Fabian Sievers, Graham M Hughes, Desmond G Higgins |
Abstract |
Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 5% |
Spain | 1 | 5% |
Sri Lanka | 1 | 5% |
Brazil | 1 | 5% |
Unknown | 15 | 79% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 5 | 26% |
Student > Ph. D. Student | 4 | 21% |
Student > Bachelor | 3 | 16% |
Student > Master | 2 | 11% |
Other | 1 | 5% |
Other | 2 | 11% |
Unknown | 2 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 6 | 32% |
Biochemistry, Genetics and Molecular Biology | 5 | 26% |
Computer Science | 4 | 21% |
Engineering | 1 | 5% |
Unknown | 3 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 04 October 2014.
All research outputs
#19,193,056
of 23,785,843 outputs
Outputs from BMC Bioinformatics
#6,479
of 7,439 outputs
Outputs of similar age
#184,011
of 255,743 outputs
Outputs of similar age from BMC Bioinformatics
#89
of 108 outputs
Altmetric has tracked 23,785,843 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,439 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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 255,743 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.