Bio
Brett Pickett, PhD, worked to understand the bacterial diversity in antibiotic-treated poultry and hypersaline environments while an undergraduate at Brigham Young University. He then went on to complete his PhD training with Dr. Elliot Lefkowitz in Microbiology at the University of Alabama at Birmingham on the various mechanisms influencing the evolution of Flaviviruses. His postdoctoral work with Dr. Richard Scheuermann at the University of Texas Southwestern Medical Center at Dallas provided an opportunity to continue this comparative sequence analysis on viral genomes as well as additional understanding about the host immune response. Subsequent positions at both the J. Craig Venter Institute and Thomson Reuters Life Sciences enabled him to develop statistical algorithms and bioinformatics workflows to interpret transcriptomic and systems biology data through pathway analysis and protein interaction networks.
Dr. Pickett currently focuses his effort on generating large-scale pathogen genome sequences, defining the host transcriptional response during virus infection, predicting immunological distance between virus sequences, identifying immunodominant peptides that are unique to closely-related virus taxa, and performing downstream bioinformatics analysis to better understand the underlying mechanism(s) of the host response during and after infections with viral pathogens. This work has been the product of successful collaborations with many prestigious domestic and international labs, with the data and results being reported at scientific conferences and in multiple peer-reviewed publications.
Research Priorities
Perform comparative genomics analyses on viral sequence data produced under the Genomic Centers for Infectious Disease program. Specifically, examine evolutionary trends, recombination, selection pressure, phylodynamic, and genotype-phenotype correlations across multiple virus taxa including:
- Flaviviruses (Zika, West Nile, Dengue)
- Influenza virus
- Respiratory Syncytial virus
- Rotavirus
Predict, generate, and test serodiagnostic peptides capable of differentiating between prior infection with multiple mosquito-borne viruses including: Dengue 1-4, West Nile, Yellow Fever, Zika, and Chikungunya viruses.
- Incorporate high-density array technology to enable rapid and efficient screening of predicted peptides
- Apply machine learning methods to identify the most immunodominant peptides for each of the viruses
- Incorporate the best peptides into a diagnostic platform with increased specificity and sensitivity to existing methods
Apply existing statistical and mathematical methods to identify relevant signaling pathways that are significantly over-represented in human transcriptomic data
- Identify differentially-affected pathways that aid in interpreting –omics data
- Incorporate existing data for approved drug targets to predict possible repurposing of existing drugs