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David A. Turissini

Researcher at University of North Carolina at Chapel Hill

Publications -  28
Citations -  1317

David A. Turissini is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Population & Reproductive isolation. The author has an hindex of 17, co-authored 26 publications receiving 1026 citations. Previous affiliations of David A. Turissini include University of Chicago.

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Adaptive genic evolution in the Drosophila genomes.

TL;DR: The theories and data pertaining to the interpretation of adaptive evolution in genomic studies are discussed and the low level of polymorphism observed in regions of reduced recombination may not be driven primarily by positive selection.
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A Test of the Snowball Theory for the Rate of Evolution of Hybrid Incompatibilities

TL;DR: Two studies support the theory that the number of genes involved in hybrid incompatibility increases faster than linearly with the divergence time between species, and show that a hybrid snowball effect is found in both plants and animals.
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Species boundaries in the human pathogen Paracoccidioides

TL;DR: It is hypothesized and further demonstrated that the mito-nuclear incongruence in the brasiliensis species complex results from interspecific hybridization and mitochondrial introgression, a common phenomenon in eukaryotes.
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Genome Sequences Reveal Cryptic Speciation in the Human Pathogen Histoplasma capsulatum.

TL;DR: A genome-wide population genetics and phylogenetic analyses with 30 Histoplasma isolates representing four recognized areas where histoplasmosis is endemic show that the histoplasma genus is composed of at least four species that are genetically isolated and rarely interbreed and propose a taxonomic rearrangement of the genus.
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Fine scale mapping of genomic introgressions within the Drosophila yakuba clade.

TL;DR: The genomic extent of introgression between the three species of the Drosophila yakuba clade is quantified and a new statistical framework, using a hidden Markov approach, is developed and applied to identify introgressed segments.