Our work is motivated by the desire to understand molecular evolution, from the origin of mutations in individuals to their fixation in evolving lineages. To achieve this, we combine comparative methods, field work, genome sequencing, and evolutionary theory with the development of new methods and software.
Reading the history of mutations in individual plants
We are working on new methods to meausure and understand mutations that accumulate within individual plants. As most organisms grow they accumulate somatic mutations - changes to the genome that persist in the body's tissues. In plants, somatic mutations can be heritable and this has huge implications for plant ecology and evolution, as well as agriculture and plant industry. We are developing empirical methods to identify, map, and date the entire history of somatic mutations across the genome of an individual plant. This is an exciting area of research that combines beautiful field work with cutting-edge genome sequencing methods to illuminate a fascinating and underexplored area of biology. We are actively building collaborations and recruiting students in this area, so if you are interested, please get in touch.
Understanding the causes and consequences of molecular evolution
Why do some species evolve faster than others? Do rates of molecular evolution drive rates of speciation? What proportion of genomic changes are adaptive, deleterious, or neutral? Questions like these are fundamental to our understanding of evolution, genomics, and biodiversity. To answer them, we wrangle together large databases of genetic, environmental, and life-history information for whole clades of organisms (e.g. mammals, birds, plants), and use them to test hypotheses about the causes and consequences of molecular evolution. These broad scale comparative approaches are extremely rewarding, allowing important glimpses into some of the most important causes and consequences of molecular evolution.
Making robust inferences from DNA datasets
Developing software and writing code is incredibly useful and very rewarding. A lot of people come to the lab specifically to learn how to code. The biggest ongoing software project in the lab is PartitionFinder, which is a piece of software for model selection in phylogenetics. We are also developing tools to better understand phylogenetic trees (RWTY), and to analyse the behaviour of scientists by looking at both distributions of published P-values (pcurver) and aspects of gender bias. Our primary languages are Python and R, and all of our code is open source. To see all of lab's software projects, please see Rob's GitHub page.