↓ Skip to main content

Direct maximum parsimony phylogeny reconstruction from genotype data

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

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
40 Mendeley
citeulike
2 CiteULike
connotea
3 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 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 3%
Germany 1 3%
Peru 1 3%
Unknown 33 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 30%
Student > Ph. D. Student 9 23%
Student > Bachelor 5 13%
Professor > Associate Professor 4 10%
Student > Master 3 8%
Other 6 15%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 70%
Biochemistry, Genetics and Molecular Biology 4 10%
Computer Science 3 8%
Mathematics 2 5%
Chemical Engineering 2 5%
Other 1 3%