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On the group theoretical background of assigning stepwise mutations onto phylogenies

Overview of attention for article published in Algorithms for Molecular Biology, December 2012
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Title
On the group theoretical background of assigning stepwise mutations onto phylogenies
Published in
Algorithms for Molecular Biology, December 2012
DOI 10.1186/1748-7188-7-36
Pubmed ID
Authors

Mareike Fischer, Steffen Klaere, Minh Anh Thi Nguyen, Arndt von Haeseler

Abstract

Recently one step mutation matrices were introduced to model the impact of substitutions on arbitrary branches of a phylogenetic tree on an alignment site. This concept works nicely for the four-state nucleotide alphabet and provides an efficient procedure conjectured to compute the minimal number of substitutions needed to transform one alignment site into another. The present paper delivers a proof of the validity of this algorithm. Moreover, we provide several mathematical insights into the generalization of the OSM matrix to multi-state alphabets. The construction of the OSM matrix is only possible if the matrices representing the substitution types acting on the character states and the identity matrix form a commutative group with respect to matrix multiplication. We illustrate this approach by looking at Abelian groups over twenty states and critically discuss their biological usefulness when investigating amino acids.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 17%
Unknown 5 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 67%
Lecturer 2 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 50%
Mathematics 2 33%
Computer Science 1 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 28 January 2013.
All research outputs
#18,327,422
of 22,694,633 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#216,635
of 279,025 outputs
Outputs of similar age from Algorithms for Molecular Biology
#6
of 10 outputs
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So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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