The Rosenberg Lab focuses on the use of computational and statistical techniques for better understanding questions about the evolution of life. Our research spans a wide range of subjects within evolutionary biology and ecology, from molecular evolution to macroevolution and from single organisms to populations and ecosystems. Many issues in bioinformatics and genomics can only be evaluated in an evolutionary context; understanding the history of species, genes and the genome is essential to both measure parameters and to define patterns of mutation that lead to phenotypic differences among species or genetic disease.
We use a multi-faceted approach to computational evolutionary biology, but tend to focus on novel statistical and computer methodology (e.g., simulation, spatial statistics, meta-analysis, and geometric morphometrics) to better describe and analyze empirical phenomena.
We do not focus on a specific group of organisms, but rather study interesting aspects of evolution whatever the taxonomy. Recently, these have included projects focused on birds, wasps, fish, rattle snakes, and fiddler crabs, as well as disease-causing vectors such as HIV, tuberculosis, and leprosy.
Phylogenetics is the study of the pattern of the tree of life, i.e., reconstructing the complete history of evolutionary relationships among living organisms. In our lab we both use phylogenetics as a tool for understanding the evolutionary history of specific groups of organisms or traits of interest, and study the process of reconstructing phylogeny itself in order to understand how assumptions and methods may bias or affect our ability to accurately reconstruct our evolutionary past.
Sequence alignment is a fundamental piece of comparative genomics and bioinformatics, yet the degree and affects of alignment error on downstream analysis has been woefully understudied. We use sequence simulation as a research paradigm for examining both the accuracy of alignment methodologies and the effects of inaccurate alignments on other bioinformatic and comparative genomic methodologies, including both phylogenetic and ancestral sequence reconstruction.
We have general interests in the field of spatial analysis and statistics. These are methods designed for analyzing data with respect to their spatial (i.e., geographic) distribution. These methods are important because spatial patterning gives information about underlying processes affecting an observed phenomenon. Furthermore, spatially distributed data violated the assumption of independence common to standard statistical testing. Beyond the basic use and development of spatial analysis methods, my lab produces software for the statistical analysis of spatially distributed data called PASSaGE and the constrution of a new version of this software is currently a major focus in my lab.
Fiddler crabs (genus Uca) are a group of small, intertidal crabs in which males show a tremendous degree of body asymmetry, having one extremely large claw (containing up to half of their total mass) and a second, much smaller claw. Females have two small claws resembling the small claw of the male. Male fiddler crabs are famous for having complex signaling displays, waving the large claw in order to attract females and delimit territory. Each species has a unique wave which can be used to distinguish amongst species in the field.
In the past, we have studied the phylogenetic history and systematic of the genus, as well as the evolution of the shape and structure of the large claw. No active fiddler crab research is currently being conducted in the lab, but we keep up with research on the genus and maintain a comprehensive fiddler crab website (www.fiddlercrab.info) which includes information on every species, photographs, video, and a comprehensive reference list to all published fiddler crab research.
Meta-analysis is a set of statistical methods for combining the results of independent studies. These methods have widespread use in medical research and the social sciences and have recently become an important tool in the life sciences. Our lab had developed techniques and software for for meta-analysis (MetaWin) and has participated in working groups and led training workshops on meta-analysis in biology.