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Instability in progressive multiple sequence alignment algorithms

Overview of attention for article published in Algorithms for Molecular Biology, October 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 264)
  • High Attention Score compared to outputs of the same age (87th percentile)

Mentioned by

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1 news outlet
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8 X users

Citations

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20 Dimensions

Readers on

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32 Mendeley
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Title
Instability in progressive multiple sequence alignment algorithms
Published in
Algorithms for Molecular Biology, October 2015
DOI 10.1186/s13015-015-0057-1
Pubmed ID
Authors

Kieran Boyce, Fabian Sievers, Desmond G. Higgins

Abstract

Progressive alignment is the standard approach used to align large numbers of sequences. As with all heuristics, this involves a tradeoff between alignment accuracy and computation time. We examine this tradeoff and find that, because of a loss of information in the early steps of the approach, the alignments generated by the most common multiple sequence alignment programs are inherently unstable, and simply reversing the order of the sequences in the input file will cause a different alignment to be generated. Although this effect is more obvious with larger numbers of sequences, it can also be seen with data sets in the order of one hundred sequences. We also outline the means to determine the number of sequences in a data set beyond which the probability of instability will become more pronounced. This has major ramifications for both the designers of large-scale multiple sequence alignment algorithms, and for the users of these alignments.

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X Demographics

The data shown below were collected from the profiles of 8 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 31 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 25%
Student > Master 8 25%
Student > Ph. D. Student 6 19%
Student > Bachelor 3 9%
Professor 2 6%
Other 2 6%
Unknown 3 9%
Readers by discipline Count As %
Computer Science 10 31%
Biochemistry, Genetics and Molecular Biology 8 25%
Agricultural and Biological Sciences 7 22%
Immunology and Microbiology 1 3%
Chemistry 1 3%
Other 1 3%
Unknown 4 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 December 2015.
All research outputs
#2,349,895
of 23,321,213 outputs
Outputs from Algorithms for Molecular Biology
#14
of 264 outputs
Outputs of similar age
#34,307
of 279,866 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 2 outputs
Altmetric has tracked 23,321,213 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 95% 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 279,866 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 87% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.