Identifying biological samples with the MinION: scientific challenges and potential applications

Dr. Brook Milligan is Director of the Conservation Genomics Laboratory and Professor of Biology at New Mexico State University. After earning a B.A. in physics from Dartmouth College and a Ph.D. in ecology from the University of California, Davis, Dr. Milligan worked at the University of Michigan as an N.S.F. Postdoctoral Fellow in Plant Biology. Subsequently, he has held academic positions at the University of Texas at Austin and New Mexico State University. Throughout his career, Dr. Milligan has focused his attention on the interface between population genetics and ecology/evolution, applying skills ranging from mathematical modeling to molecular genetics to field demography. Of particular interest is the challenge of extracting useful information about natural populations from patterns of genetic variation. This requires overcoming the limitation that there is little genetic information available for most natural populations, resulting in methodological, quantitative and mathematical challenges. Dr. Milligan’s recent work in genomics focuses on harnessing biomedical and other innovative technologies to achieve rapid and inexpensive processing of genomes. This allows analysis of both a larger number of samples and of a larger proportion of each sample. This expansion provides a far more robust basis for identifying distinctive genetic markers for certain species or populations. These markers drive the development of practical applications to track specimens for conservation and management, and for enforcement of policies for particular species or populations. Currently, Dr. Milligan is collaborating with the U.S. Forest Service, the Department of State and USAID to apply genetics technology to tracking the origin of legally and illegally sourced timber products. In connection with this, Dr. Milligan has represented the United States at an international meeting on timber tracking technologies.

Authors: Brook Milligan