Improving the Accuracy and Reproducibility of Genomics Measurements
With the advent of next-generation DNA sequencing technologies, the generation of genomic data continues to grow exponentially. The use of genomic data spans every field in biology, informing research in biomedicine, agriculture, evolution and ecology, neuroscience, and many other disciplines. Genomics measurements are susceptible to error and bias at essentially every step of the data generation and analysis workflows. Given these challenges, accurate and reproducible data generation requires optimized wet lab and analysis methods and appropriate controls to monitor and report on errors and bias. I will describe efforts in the University of Minnesota Genomics Center to detect and dissect biases in the process of generating next-generation sequencing data and to improve the accuracy and reproducibility of genomics measurements.
Daryl Gohl, Ph.D., leads the University of Minnesota Genomics Center’s Innovation Lab and is a Research Assistant Professor in the Department of Genetics, Cell Biology, and Development at the University of Minnesota. Dr. Gohl's work focuses on developing new techniques for genomics-based measurements and genetic manipulation of complex biological systems. Dr. Gohl has applied such methods to diverse problems, from accurately measuring microbial communities (microbiomes), to studying the nervous system, to infectious diseases such as HIV, tuberculosis, and the recent coronavirus pandemic. In addition to his academic work, Dr. Gohl is a Co-Founder and Senior Scientific Advisor of CoreBiome, Inc. (now Diversigen, Inc.) a microbiome analysis company based in New Brighton, MN.