Jonathan Pevsner, Ph.D.
Dr. Jonathan Pevsner is a professor and research scientist at the Kennedy Krieger Institute. He also holds a primary faculty appointment in the Department of Psychiatry and Behavioral Sciences at the Johns Hopkins University School of Medicine.
Dr. Pevsner received his bachelor's degree in psychology from Haverford College and his doctoral degree in pharmacology and molecular sciences from the Johns Hopkins School of Medicine. He pursued post-doctoral training at the Stanford University School of Medicine, and joined the faculty of Kennedy Krieger Institute in 1995.
The Pevsner Lab studies the molecular basis of childhood and adult brain disorders. Studies include the following diseases, applying various tools of bioinformatics genomics.
- Sturge-Weber syndrome: In 2013, the Pevsner Lab reported a somatic mutation that causes SWS (a rare neurocutaneous disorder) and port-wine birthmarks (affected 1:300 people). They achieved this using whole genome sequencing. They are currently using biochemical approaches to understand the consequence of this mutation which occurs in GNAQ encoding a G protein alpha subunit.
- Bipolar disorder: Using genomics approaches to characterize somatic and germline mutations as well as transcriptional changes.
- Autism spectrum disorder: Analyzing genomic data, particularly in children with severe autism and behavioral conditions such as self-injurious behavior.
- Schizophrenia (SZ): Characterizing transcriptional changes in SZ by RNA-seq.
Dr. Pevsner is author of a textbook, Bioinformatics and Functional Genomics (John Wiley & Sons, 3rd edition, 2015). He teaches courses and workshops in bioinformatics and genomics.
The Pevsner Lab is involved in bioinformatics research. Bioinformatics is the interface between molecular biology and computers. The lab has developed the following programs:  "Database Referencing of Array Genes ONline" (DRAGON), a web-accessible database that helps researchers analyze the data obtained from microarrays or other gene expression experiments. Once a microarray experiment indicates the expression levels of tens of thousands of genes, researchers must identify the biological characteristics associated with all of those genes and their encoded proteins. Looking online for information about each gene individually is time-consuming and impractical. DRAGON compiles information from a variety of public databases. It then rapidly supplies information pertaining to a range of the biological characteristics of the genes in any microarray data set.  "Standardization and Normalization of Microarray Data" (SNOMAD), a set of web-accessible tools for the analysis of genes that are significantly regulated in microarray data sets. SNOMAD includes non-linear transformations to correct for bias and variance in array data.  "Statistical Analysis and Visualization of Annotated Gene Expression data" (SAVAGE), a data visualization tool that uses principal components analysis to graphically display functionally annotated gene expression data sets.
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