Contact
brett_pickett@byu.edu
801.422.2506
3141 LSB
Education
Ph.D., Microbiology, University if Alabama at Birmingham, 2010
B.S., Microbiology, Brigham Young University, 2005
Research Interests
The research in our group can be divided into two distinct but complementary categories: comparative genomics of pathogens, and the human transcriptional response to a stimulus (i.e. infection, cancer, etc).
To perform comparative genomics, we begin by either assembling consensus genomes or retrieving them from public databases. We then generate multiple sequence alignments, reconstruct phylogenetic trees, identify significant changes between groups of sequences, and apply other relevant methods. Our goal is to better understand how the pathogens evolve over time, what advantages certain mutations may confer, and whether we can predict future changes before they occur.
To analyze the human transcriptional during acute infection (e.g. influenza, Streptococcus) or chronic disease (e.g. cancer, autoimmunity), we begin by retrieving raw sequencing data from the National Center for Biotechnology Information. These sequences are then subjected to an analytical pipeline that quantifies how many times each mRNA in the sample is present. We can then run statistics to identify significant 1) differentially-expressed genes, 2) functional enrichment of terms, and 3) signaling pathways that are represented. The signaling pathway results can then be cross-referenced to other data sources to find drugs that target host processes that the infection or chronic condition generally affects. By targeting such "key pathways", we can potentially reduce the severity, symptoms, and/or progression of these acute or chronic diseases.
The results from either research category can then be combined and subjected to machine learning algorithms. These types of analyses can identify prominent and/or subtle trends that can then be tested for biological relevance and function in the laboratory.The research in our group can be divided into two distinct but complementary categories: comparative genomics of pathogens, and the human transcriptional response to a stimulus (i.e. infection, cancer, etc).
To perform comparative genomics, we begin by either assembling consensus genomes or retrieving them from public databases. We then generate multiple sequence alignments, reconstruct phylogenetic trees, identify significant changes between groups of sequences, and apply other relevant methods. Our goal is to better understand how the pathogens evolve over time, what advantages certain mutations may confer, and whether we can predict future changes before they occur.
To analyze the human transcriptional during acute infection (e.g. influenza, Streptococcus) or chronic disease (e.g. cancer, autoimmunity), we begin by retrieving raw sequencing data from the National Center for Biotechnology Information. These sequences are then subjected to an analytical pipeline that quantifies how many times each mRNA in the sample is present. We can then run statistics to identify significant 1) differentially-expressed genes, 2) functional enrichment of terms, and 3) signaling pathways that are represented. The signaling pathway results can then be cross-referenced to other data sources to find drugs that target host processes that the infection or chronic condition generally affects. By targeting such "key pathways", we can potentially reduce the severity, symptoms, and/or progression of these acute or chronic diseases.
The results from either research category can then be combined and subjected to machine learning algorithms. These types of analyses can identify prominent and/or subtle trends that can then be tested for biological relevance and function in the laboratory.