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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


Select Publications

Identification of Dezidougou Virus in a DAK AR 41524 Zika Virus Stock.
Genome announcements. 2017-07-27; 5.30:
PMID: 28751385
Identification of diagnostic peptide regions that distinguish Zika virus from related mosquito-borne Flaviviruses.
PloS one. 2017-01-01; 12.6: e0178199.
PMID: 28562637
Diversifying Selection Analysis Predicts Antigenic Evolution of 2009 Pandemic H1N1 Influenza A Virus in Humans.
Journal of virology. 2015-05-01; 89.10: 5427-40.
PMID: 25741011
A RESTful API for Access to Phylogenetic Tools via the CIPRES Science Gateway.
Evolutionary bioinformatics online. 2015-01-01; 11.43-8.
PMID: 25861210
Toward a method for tracking virus evolutionary trajectory applied to the pandemic H1N1 2009 influenza virus.
Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2014-12-01; 28.351-7.
PMID: 25064525
Metadata-driven comparative analysis tool for sequences (meta-CATS): an automated process for identifying significant sequence variations that correlate with virus attributes.
Virology. 2013-12-01; 447.1-2: 45-51.
PMID: 24210098
Virus pathogen database and analysis resource (ViPR): a comprehensive bioinformatics database and analysis resource for the coronavirus research community.
Viruses. 2012-11-19; 4.11: 3209-26.
PMID: 23202522
Influenza research database: an integrated bioinformatics resource for influenza research and surveillance.
Influenza and other respiratory viruses. 2012-11-01; 6.6: 404-16.
PMID: 22260278
Influenza virus sequence feature variant type analysis: evidence of a role for NS1 in influenza virus host range restriction.
Journal of virology. 2012-05-01; 86.10: 5857-66.
PMID: 22398283
ViPR: an open bioinformatics database and analysis resource for virology research.
Nucleic acids research. 2012-01-01; 40.Database issue: D593-8.
PMID: 22006842
A phylogenetic analysis using full-length viral genomes of South American dengue serotype 3 in consecutive Venezuelan outbreaks reveals a novel NS5 mutation.
Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2011-12-01; 11.8: 2011-9.
PMID: 21964598
Evidence for separation of HCV subtype 1a into two distinct clades.
Journal of viral hepatitis. 2011-09-01; 18.9: 608-18.
PMID: 20565573