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Bootstrapping phylogenies inferred from rearrangement data

Overview of attention for article published in Algorithms for Molecular Biology, August 2012
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Title
Bootstrapping phylogenies inferred from rearrangement data
Published in
Algorithms for Molecular Biology, August 2012
DOI 10.1186/1748-7188-7-21
Pubmed ID
Authors

Yu Lin, Yu Lin, Vaibhav Rajan, Bernard ME Moret

Abstract

Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 50%
United States 1 50%
Germany 1 50%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 250%
Student > Ph. D. Student 3 150%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 250%
Computer Science 2 100%
Biochemistry, Genetics and Molecular Biology 1 50%

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 15 September 2012.
All research outputs
#10,014,397
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#146
of 177 outputs
Outputs of similar age
#95,176
of 112,679 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 5 outputs
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