My research is primarily in computational evolutionary biology and bioinformatics.
In general, I am interested in understanding the processes that control diversity and similarity among
organisms; this ranges across a broad array of subjects within ecology and evolution,
from phylogenetics and macroevolution to molecular evolution 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 necessary to both measure parameters and to define
patterns of mutation that lead to phenotypic differences among species or genetic disease.
My research uses a multi-faceted approach to computational evolutionary biology, but tends 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.
My lab currently has two primary research foci: (1) examining the role of sequence alignment in evolutionary analysis, and (2) developing methods and software for biological spatial analysis. Secondarily, we are also involved with general research on phylogenetics, fiddler crabs, and meta-analysis.
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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.
I have general interests in the field of spatial analysis and statistics. These are methods designed for analyzing data with respect to their spatial distribution. These analyses 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, I have authored 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 (containg 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 wave the large claw in order to attract females and delimit territory; each species has a unique wave.
While I am not actively studying fiddler crabs at this time, I still find them a fascinating group of organisms and am quite interested in fiddler crab research, particularly studies of their evolution, systematics, sexual selection, combat, and claw morphology. I 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. I am the primary author of the
meta-analytic software MetaWin, and have
been participated in working groups and led training workshops on meta-analysis in biology.