Abstract: Recent advances in large scale sequencing of microbial DNA without culturing or isolation gives us easy and direct access to “wild” microbial metagenomes that are otherwise virtually impossible to study. However, because these data sets are large and relatively unstructured, analyzing them requires advancing data science techniques that include both new conceptual approaches as well as substantial practical software engineering.

 

Our work has focused on data-intensive approaches to studying ecology and genomics of microbial genomes and “population” pan-genomes in environmental samples, using tools and approaches developed in our lab and in collaboration with others. We see many ways in which microbial genomes do not particularly resemble our naive expectations, which is leading us into some productive confusion around known knowns, known unknowns, and unknown unknowns in environmental microbial systems.

 

Our work relies heavily on novel methods and technical infrastructure development. In this talk I will present a series of vignettes on our techniques and some of the results, aimed at a general scientific audience.

 

This talk is part of the RED Talk series sponsored by the Data Science Initiative at NC State. Our goal is to raise awareness of the breadth and depth of data science across campus as well as to continue to grow and engage the data science community.

Short Bio: C. Titus Brown is an Associate Professor in the School of Veterinary Medicine at UC Davis, where he works on methods in data intensive research across the biosciences, computer science, and software engineering. He is surprisingly passionate about open science and sociotechnical systems for community development of research infrastructure, and blogs regularly on these topics at ivory.idyll.org/blog/. All of his software is open source at GitHub.com/dib-lab/, and he is on Twitter at @ctitusbrown.