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Ancestral Sequence Reconstruction with Maximum Parsimony

Overview of attention for article published in Bulletin of Mathematical Biology, October 2017
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Title
Ancestral Sequence Reconstruction with Maximum Parsimony
Published in
Bulletin of Mathematical Biology, October 2017
DOI 10.1007/s11538-017-0354-6
Pubmed ID
Authors

Lina Herbst, Mareike Fischer

Abstract

One of the main aims in phylogenetics is the estimation of ancestral sequences based on present-day data like, for instance, DNA alignments. One way to estimate the data of the last common ancestor of a given set of species is to first reconstruct a phylogenetic tree with some tree inference method and then to use some method of ancestral state inference based on that tree. One of the best-known methods both for tree inference and for ancestral sequence inference is Maximum Parsimony (MP). In this manuscript, we focus on this method and on ancestral state inference for fully bifurcating trees. In particular, we investigate a conjecture published by Charleston and Steel in 1995 concerning the number of species which need to have a particular state, say a, at a particular site in order for MP to unambiguously return a as an estimate for the state of the last common ancestor. We prove the conjecture for all even numbers of character states, which is the most relevant case in biology. We also show that the conjecture does not hold in general for odd numbers of character states, but also present some positive results for this case.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 5 24%
Student > Ph. D. Student 5 24%
Student > Master 4 19%
Lecturer 1 5%
Researcher 1 5%
Other 1 5%
Unknown 4 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 29%
Agricultural and Biological Sciences 6 29%
Chemical Engineering 2 10%
Mathematics 1 5%
Business, Management and Accounting 1 5%
Other 1 5%
Unknown 4 19%
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 11 October 2017.
All research outputs
#20,449,496
of 23,005,189 outputs
Outputs from Bulletin of Mathematical Biology
#1,003
of 1,103 outputs
Outputs of similar age
#281,716
of 322,951 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#19
of 19 outputs
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So far Altmetric has tracked 1,103 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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