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Aligning the unalignable: bacteriophage whole genome alignments

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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38 X users
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Title
Aligning the unalignable: bacteriophage whole genome alignments
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0869-5
Pubmed ID
Authors

Sèverine Bérard, Annie Chateau, Nicolas Pompidor, Paul Guertin, Anne Bergeron, Krister M. Swenson

Abstract

In recent years, many studies focused on the description and comparison of large sets of related bacteriophage genomes. Due to the peculiar mosaic structure of these genomes, few informative approaches for comparing whole genomes exist: dot plots diagrams give a mostly qualitative assessment of the similarity/dissimilarity between two or more genomes, and clustering techniques are used to classify genomes. Multiple alignments are conspicuously absent from this scene. Indeed, whole genome aligners interpret lack of similarity between sequences as an indication of rearrangements, insertions, or losses. This behavior makes them ill-prepared to align bacteriophage genomes, where even closely related strains can accomplish the same biological function with highly dissimilar sequences. In this paper, we propose a multiple alignment strategy that exploits functional collinearity shared by related strains of bacteriophages, and uses partial orders to capture mosaicism of sets of genomes. As classical alignments do, the computed alignments can be used to predict that genes have the same biological function, even in the absence of detectable similarity. The Alpha aligner implements these ideas in visual interactive displays, and is used to compute several examples of alignments of Staphylococcus aureus and Mycobacterium bacteriophages, involving up to 29 genomes. Using these datasets, we prove that Alpha alignments are at least as good as those computed by standard aligners. Comparison with the progressiveMauve aligner - which implements a partial order strategy, but whose alignments are linearized - shows a greatly improved interactive graphic display, while avoiding misalignments. Multiple alignments of whole bacteriophage genomes work, and will become an important conceptual and visual tool in comparative genomics of sets of related strains. A python implementation of Alpha, along with installation instructions for Ubuntu and OSX, is available on bitbucket ( https://bitbucket.org/thekswenson/alpha ).

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
United States 2 2%
France 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
Nepal 1 <1%
Unknown 112 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 23%
Researcher 24 20%
Student > Master 18 15%
Student > Bachelor 13 11%
Student > Doctoral Student 4 3%
Other 15 13%
Unknown 18 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 38%
Biochemistry, Genetics and Molecular Biology 26 22%
Computer Science 10 8%
Immunology and Microbiology 9 8%
Medicine and Dentistry 2 2%
Other 6 5%
Unknown 21 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 12 November 2021.
All research outputs
#1,702,259
of 23,770,218 outputs
Outputs from BMC Bioinformatics
#353
of 7,434 outputs
Outputs of similar age
#30,959
of 399,449 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 146 outputs
Altmetric has tracked 23,770,218 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,434 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 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 399,449 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.