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Direct maximum parsimony phylogeny reconstruction from genotype data

Overview of attention for article published in BMC Bioinformatics, December 2007
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Citations

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Title
Direct maximum parsimony phylogeny reconstruction from genotype data
Published in
BMC Bioinformatics, December 2007
DOI 10.1186/1471-2105-8-472
Pubmed ID
Authors

Srinath Sridhar, Fumei Lam, Guy E Blelloch, R Ravi, Russell Schwartz

Abstract

Maximum parsimony phylogenetic tree reconstruction from genetic variation data is a fundamental problem in computational genetics with many practical applications in population genetics, whole genome analysis, and the search for genetic predictors of disease. Efficient methods are available for reconstruction of maximum parsimony trees from haplotype data, but such data are difficult to determine directly for autosomal DNA. Data more commonly is available in the form of genotypes, which consist of conflated combinations of pairs of haplotypes from homologous chromosomes. Currently, there are no general algorithms for the direct reconstruction of maximum parsimony phylogenies from genotype data. Hence phylogenetic applications for autosomal data must therefore rely on other methods for first computationally inferring haplotypes from genotypes.

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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 10%
United Kingdom 1 2%
Germany 1 2%
Peru 1 2%
Unknown 34 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 29%
Student > Ph. D. Student 9 22%
Student > Bachelor 5 12%
Professor > Associate Professor 4 10%
Student > Master 4 10%
Other 7 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 68%
Biochemistry, Genetics and Molecular Biology 4 10%
Computer Science 4 10%
Mathematics 2 5%
Chemical Engineering 2 5%
Other 1 2%

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 13 February 2015.
All research outputs
#3,368,552
of 4,755,858 outputs
Outputs from BMC Bioinformatics
#2,376
of 2,782 outputs
Outputs of similar age
#119,692
of 170,955 outputs
Outputs of similar age from BMC Bioinformatics
#125
of 138 outputs
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