Andrew Doxey

Due to continual advances in sequencing and other “omics” technologies, biology is experiencing a big data revolution. Computational methods are required not only to interpret newly sequenced genomes, but search through a vast quantity of existing biological information to reveal previously uncharacterized functionality.

The Doxey lab is interested in both the development and application of computational methods to predict novel molecular functions (protein-coding and non-coding) from genomic, structural, and other high-throughput datasets. We explore three separate but overlapping areas:

*Predicting novel protein families and functions: We develop methods that combine sequence analysis with structural bioinformatics to predict and experimentally validate protein functions of interest. Currently, we are focusing efforts on predicting new families and functions of bacterial flagellins, clostridial toxins, and proteolytic enzymes.

*Predicting evolutionary adaptations in genes and genomes: We combine sequence analysis, phylogenetics and structural modeling to pinpoint adaptive events in genes and genomes. We are interested in inferring mechanisms of neofunctionalization and functional divergence in protein families as well as non-coding regulatory elements.

*Comparative functional metagenomics: The Doxey Lab is also developing computational approaches to functionally annotate metagenomes and detect biologically relevant differences between them. Recent work includes the development of the MetAnnotate framework for combined taxonomic and functional profiling of metagenomic datasets.

Professor Doxey actively seeks eager and talented graduate or undergraduate students interested in bioinformatics, genomics, or molecular biology.