Stephen Smith

Assistant Professor, Ecology and Evolutionary Biology, LSAThe Center of Computational Medicine and Bioinformatics

Using large data to examine rates and modes of evolution

The Smith lab group is primarily interested in examining evolutionary processes using new data sources and analysis techniques. We develop new methods to address questions about the rates and modes of evolution using the large data sources that have become more common in the biological disciplines over the last ten years. In particular, we use DNA sequence data to construct phylogenetic trees and conduct additional analyses about processes of evolution on these trees. In addition to this research program, we also address how new data sources can facilitate new research in evolutionary biology. To this end, we sequence transcriptomes, primarily in plants, with the goal of better understanding where, within the genome and within the phylogeny, processes like gene duplication and loss, horizontal gene transfer, and increased rates of molecular evolution occur.

A rough draft of the first comprehensive tree of life, showing the links between all of the more than 2.3 million named species of animals, plants and microorganisms. The draft was constructed by combining more than 450 existing trees to a comprehensive taxonomy. Because the tree is large, only lineages with at least 500 species are shown. The colors correspond to the amount of publicly available DNA data for each lineage (red = high, blue = low, giving an idea of the amount of available information).
A rough draft of the first comprehensive tree of life, showing the links between all of the more than 2.3 million named species of animals, plants and microorganisms. The draft was constructed by combining more than 450 existing trees to a comprehensive taxonomy. Because the tree is large, only lineages with at least 500 species are shown. The colors correspond to the amount of publicly available DNA data for each lineage (red = high, blue = low, giving an idea of the amount of available information).

Accomplishments and Awards

COntact

(734) 764-7923

eebsmith@umich.edu

Website

Location

Ann Arbor

Methodologies

Data Mining / Statistics

Applications

Biological Sciences / Informatics