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Partially local three-way alignments and the sequence signatures of mitochondrial genome rearrangements

Overview of attention for article published in Algorithms for Molecular Biology, August 2017
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Title
Partially local three-way alignments and the sequence signatures of mitochondrial genome rearrangements
Published in
Algorithms for Molecular Biology, August 2017
DOI 10.1186/s13015-017-0113-0
Pubmed ID
Authors

Marwa Al Arab, Matthias Bernt, Christian Höner zu Siederdissen, Kifah Tout, Peter F. Stadler

Abstract

Genomic DNA frequently undergoes rearrangement of the gene order that can be localized by comparing the two DNA sequences. In mitochondrial genomes different mechanisms are likely at work, at least some of which involve the duplication of sequence around the location of the apparent breakpoints. We hypothesize that these different mechanisms of genome rearrangement leave distinctive sequence footprints. In order to study such effects it is important to locate the breakpoint positions with precision. We define a partially local sequence alignment problem that assumes that following a rearrangement of a sequence F, two fragments L, and R are produced that may exactly fit together to match F, leave a gap of deleted DNA between L and R, or overlap with each other. We show that this alignment problem can be solved by dynamic programming in cubic space and time. We apply the new method to evaluate rearrangements of animal mitogenomes and find that a surprisingly large fraction of these events involved local sequence duplications. The partially local sequence alignment method is an effective way to investigate the mechanism of genomic rearrangement events. While applied here only to mitogenomes there is no reason why the method could not be used to also consider rearrangements in nuclear genomes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 50%
Student > Ph. D. Student 1 8%
Student > Doctoral Student 1 8%
Student > Master 1 8%
Professor > Associate Professor 1 8%
Other 0 0%
Unknown 2 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 25%
Biochemistry, Genetics and Molecular Biology 2 17%
Computer Science 2 17%
Mathematics 1 8%
Environmental Science 1 8%
Other 1 8%
Unknown 2 17%
Attention Score in Context

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 31 August 2017.
All research outputs
#18,569,430
of 22,999,744 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#243,352
of 317,355 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 8 outputs
Altmetric has tracked 22,999,744 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 264 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.