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AlignStat: a web-tool and R package for statistical comparison of alternative multiple sequence alignments

Overview of attention for article published in BMC Bioinformatics, October 2016
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Title
AlignStat: a web-tool and R package for statistical comparison of alternative multiple sequence alignments
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1300-6
Pubmed ID
Authors

Thomas Shafee, Ira Cooke

Abstract

Alternative sequence alignment algorithms yield different results. It is therefore useful to quantify the similarities and differences between alternative alignments of the same sequences. These measurements can identify regions of consensus that are likely to be most informative in downstream analysis. They can also highlight systematic differences between alignments that relate to differences in the alignment algorithms themselves. Here we present a simple method for aligning two alternative multiple sequence alignments to one another and assessing their similarity. Differences are categorised into merges, splits or shifts in one alignment relative to the other. A set of graphical visualisations allow for intuitive interpretation of the data. AlignStat enables the easy one-off online use of MSA similarity comparisons or into R pipelines. The web-tool is available at AlignStat.Science.LaTrobe.edu.au. The R package, readme and example data are available on CRAN and GitHub.com/TS404/AlignStat.

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The data shown below were collected from the profiles of 4 X users 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 3%
France 1 3%
Australia 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 19%
Researcher 7 19%
Student > Master 5 14%
Student > Ph. D. Student 5 14%
Student > Doctoral Student 3 8%
Other 3 8%
Unknown 6 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 31%
Agricultural and Biological Sciences 7 19%
Immunology and Microbiology 2 6%
Business, Management and Accounting 1 3%
Computer Science 1 3%
Other 5 14%
Unknown 9 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 October 2016.
All research outputs
#14,547,784
of 23,298,349 outputs
Outputs from BMC Bioinformatics
#4,818
of 7,379 outputs
Outputs of similar age
#179,567
of 315,436 outputs
Outputs of similar age from BMC Bioinformatics
#63
of 121 outputs
Altmetric has tracked 23,298,349 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,379 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.