Network Systems Science and Advanced Computing

Allan Dickerman

  • Senior Scientist
Allan Dickerman headshot


Allan Dickerman is a senior scientist in the Network Systems Science and Advanced Computing division. Since 2000, first at the Virginia Bioinformatics Institute at Virginia Tech and now at the Biocomplexity Institute at the University of Virginia, Dickerman has engaged in computational data analysis across diverse research projects spanning bacteria, plant and animal biology. Dickerman has been active in analyzing high-throughput sequence data since the era of 454 Life Sciences technology through to the new long-read methods. He has contributed computational modules for genome assembly and phylogenetic inference to the PATRIC resource and he enjoys participating in the development of novel technologies.

  • In the early aughts (~2003) Dickerman developed a database called GeneTrees (published on the web at the time) where he attempted to present a gene-phylogeny interpretation of all discernable homologies among the completely sequenced organisms to date. With Kelly Williams, Dickerman developed a phylogenomics approach to whole annotated bacterial genomes to find all apparently orthologously evolving genes (e.g., excluding horizontal transfer events and complex paralogies) to assemble datasets from hundreds (now thousands) of aligned proteins in a very large species-tree reconstruction. They applied this first to alphaproteobacteria and then gammaproteobacteria, resolving higher taxonomic issues and refining the estimated origin of the mitochondrion. A student of Dickerman’s, Eric Nordberg, encapsulated this methodology further as an integrated program in Java called PEPR (Phylogenomic Estimation with Progressive Refinement) which is used to build bacterial phylogenies for the PATRIC database. More recently, Dickerman has implemented a multi-gene phylogenetic analysis system for bacteria called ‘codon tree’ which has succeeded PEPR at PATRIC. Advantages are that it exploits known homology groups rather than infer them de-novo, thus saving time and simplifying communication of which genes were analyzed, and that it analyzes both the protein and corresponding coding sequence, broken down by codon position, providing better dynamic range than either alone.

  • University of California, Irvine, CA, Postdoc Phylogenetic methods, 1998
    University of Arizona, Tucson, AZ, Postdoc Population genetics, 1997
    University of Wisconsin, Madison, WI, Ph.D. Zoology, 1992
    University of New Mexico, Albuquerque, NM, M.Sc. Biology, 1987
    Cornell University, Ithaca, NY, B.A. Biology, 1984