The Bioinformatics Team is led by Drs. David Reif and Fred Wright

The Bioinformatics Team develops novel analytic and computational tools to translate Big Data generated across high-throughput and multiscale experiments into systems-level discoveries. This group brings together biostatisticians, bioinformaticists, geneticists, and mathematicians. This Team conducts informatics analyses of large data sets from in silico, in vitro, in vivo, and population studies to address important problems in EHS.

John House David M. Reif
Dereje Jima Jung-Ying Tzeng
Alison A. Motsinger-Reif Fred Wright


Full Members

House2House, John

Research Scholar, Bioinformatics Research Center

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John House is a molecular toxicologist whose research has moved from bench science utilizing genetically engineered mice to data science involving a variety of high-dimensional genomic and environmental exposure data to assess impacts on human health. His expertise includes pulmonary function, asthma, atopy, cellular signaling and cancer biology. John works with epidemiological, genetic, epigenetic, and gene-expression data to develop novel methods for analysis and elucidate how environmental exposures interact with genetic variation to affect human health.


Jima-e1456257068352Jima, Dereje

Research Scholar, Center for Human Health and the Environment

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My research is focused on analytics at the intersection of human health and large, complex sets of data. I conduct Bioinformatics analysis and research in support of projects across the members of Center of Human Health and the Environment (CHHE) and Bioinformatics Research Center (BRC) as a collaboration. I perform consultation at the design stage of research studies to insure that the experimental design of the studies are valid, efficient, and correctly powered.   My expertise includes a broader ranges such as cancer genomics, immunology, infectious diseases, epidemiology and application development; and domain expertise includes proteomics, metabolomics, genomics, personalized medicine, and biosignature discovery. I have worked with collaborators on discoveries relating to cancer, infectious disease, immunology, environmental exposure and application development. I have analytical capabilities of whole genome/exome sequencing, RNA-Seq, ChIP-Seq, FAIRE-Seq, small/long ncRNA transcriptome, microbiome, and expression/genotype microarrays.


Motsinger-Reif, Alison A.

Associate Professor, Dept. of Statistics

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We do gene mapping to find variants that predict drug response or chemical exposure outcomes in human genetics. We also do methods development in gene-gene and gene-environment interactions.


Reif, David M.

Associate Professor, Dept. of Biological Sciences

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The overarching goal of research in the Reif Lab is to understand the complex interactions between human health and the environment. To accomplish this goal, we focus on developing bioinformatical/statistical methods, visual analytics, experimental design, and software for the integrated analysis of high-dimensional, multi-scale data from diverse sources. Data sources include epidemiological studies of human health, high-throughput screening (HTS) of environmental chemicals, in vitro studies, and model organisms.


Tzeng, Jung-Ying

Associate Professor, Dept. of Statistics

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My research interests combine the fields of statistics and genetics and I focus on developing statistical methods that can facilitate genetic epidemiologic research on human complex diseases. Some of my current research projects include statistical modeling of marker-set and gene-set association analysis for diseases and pharmacogenetics, CNV analysis, integrative analysis of multi-omic data, network-guided inference on global and local variant identification in genomic studies.


Wright, Fred

Professor, Dept. of Statistics

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The Wright group develops methods for the analysis and interpretation of genetic data, including the analysis of genome-wide association, gene expression variation, and toxicogenomics. Data from collaborators and public sources is used to inspire novel statistical methods for biological discovery. The biological problems range from assessing gene-environment interactions in disease to dissecting the heritable variation in susceptibility to environmental chemicals.