The Gutenkunst Group

Mission

We study the function and evolution of the complex molecular networks that comprise life. To do so, we integrate computational population genomics, bioinformatics, and molecular evolution. We also prepare group members for fulfilling professional lives. Our group is interdisciplinary and collaborative, with an atmosphere that promotes creativity.

Group Handbook

To learn more about the scientific and professional aspirations and expectations of the group, see our handbook.

Location

We are centered in the Department of Molecular and Cellular Biology at the University of Arizona. But we come from and collaborate with units across campus, including Applied Mathematics, Ecology and Evolutionary Biology, Genetics, and Statistics.

Site Administration



LAYOUT IDEA ONE



 

Publication updates

By Ryan Gutenkunst | February 19, 2021
 

I’m pleased to report that Paul’s paper on Hyde-CNN has been accepted for publication in Molecular Ecology Resources, and the dadi.CUDA paper is in press at Molecular Biology and Evolution.

We also just posted a major update of our joint DFE preprint. Postdoc Xin Huang did a great job leading this revision. The main content difference is that new large-scale simulations with background selection demonstrate that such linked selection does not bias our inference. Xin also carefully replicated the other results in the paper, consolidating work that had been done by multiple undergraduates over the years to ensure correctness and consistency.

 

Congratulations Paul!

By Ryan Gutenkunst | December 14, 2020
 

Paul Blischak, a Postdoctoral Fellow working with our group and Mike Barker’s group, just started as Data Scientist at Bayer Crop Science. This is a great position for Paul, combining interesting applied science with the ability to work remotely from anywhere.

 

Welcome Jenny!

By Ryan Gutenkunst | August 31, 2020
 

We welcome new postdoc Jennifer James to our group. Jenny is coming from Joanna Masel’s group, where she worked on fundamental questions of protein evolution. In our group she’ll be working on applications of joint DFE inference, a callback to her PhD studies with Adam Eyre-Walker.

 

New preprint: dadi on GPUs

By Ryan Gutenkunst | August 3, 2020
 

We’ve long known that the core numerical algorithm in dadi is in principle amenable to acceleration through GPU computing. We have now implemented GPU computing in dadi, and the results are spectacular. We see speed ups of over 100x comparing GPUs and CPUs on the same systems. And enabling this speed requires only a single user command. The new feature is described in our bioRxiv preprint, and it is available in dadi 2.1.0.

Image
GPUspeed-1024x545


LAYOUT IDEA TWO



 

Publication updates

I’m pleased to report that Paul’s paper on Hyde-CNN has been accepted for publication in Molecular Ecology Resources, and the dadi.CUDA paper is in press at Molecular Biology and Evolution.

We also just posted a major update of our joint DFE preprint. Postdoc Xin Huang did a great job leading this revision. The main content difference is that new large-scale simulations with background selection demonstrate that such linked selection does not bias our inference. Xin also carefully replicated the other results in the paper, consolidating work that had been done by multiple undergraduates over the years to ensure correctness and consistency.

Congratulations Paul!

Paul Blischak, a Postdoctoral Fellow working with our group and Mike Barker’s group, just started as Data Scientist at Bayer Crop Science. This is a great position for Paul, combining interesting applied science with the ability to work remotely from anywhere.

Welcome Jenny!

We welcome new postdoc Jennifer James to our group. Jenny is coming from Joanna Masel’s group, where she worked on fundamental questions of protein evolution. In our group she’ll be working on applications of joint DFE inference, a callback to her PhD studies with Adam Eyre-Walker.

New preprint: dadi on GPUs

We’ve long known that the core numerical algorithm in dadi is in principle amenable to acceleration through GPU computing. We have now implemented GPU computing in dadi, and the results are spectacular. We see speed ups of over 100x comparing GPUs and CPUs on the same systems. And enabling this speed requires only a single user command. The new feature is described in our bioRxiv preprint, and it is available in dadi 2.1.0.

GPUspeed-1024x545