The Phenotyping Core promotes functional genomics and other preclinical translational science at Johns Hopkins. We assist and collaborate in the characterization and use of genetically and phenotypically relevant animal models of disease and gene function.

The Feinberg Laboratory studies the epigenetic basis of normal development and disease, including cancer, aging and neuropsychiatric illness. Early work from our group involved the discovery of altered DNA methylation in cancer as well as common epigenetic (methylation and imprinting) variants in the population that may be responsible for a significant population-attributable risk of cancer. Over the last few years, we have pioneered the field of epigenomics (i.e., epigenetics at a genome-scale level), founding the first NIH-supported NIH epigenome center in the country and developing many novel tools for molecular and statistical analysis. Current research examines the mechanisms of epigenetic modification, the epigenetic basis of cancer, the invention of new molecular, statistical, and epidemiological tools for genome-scale epigenetics and the epigenetic basis of neuropsychiatric disease, including schizophrenia and autism.

Research in the Alan Scott Lab involves several important areas of genomics. Our team collaborates on a study to investigate the exon and genome sequence variants that determine phenotype, with a specific focus on the genetic bases of cleft lip and palate. We are also involved in assessing and improving genomic technologies to provide next-generation sequencing and analysis of sequence data to the clinical environment. In addition, we have a longstanding interest in the problem of gene annotation and the evolutionary genomics of vertebrates, especially endangered species.

Work in the Christopher Chute Lab involves the management of clinical data to enable effective evidence-based clinical practice and translational research. Recently, we developed an EHR-based genetic testing knowledge base to be integrated into the genetic testing ontology (GTO) and identified potential barriers to pharmacogenomics clinical decision support (CDS) implementation.

Research in the Casey Overby Lab focuses on the intersection of public health genomics and biomedical informatics. We’re currently developing applications to support the translation of genomic research to clinical and population-based health care settings. We’re also working to develop knowledge-based ways to use big data — including electronic health records — to improve population health.

The Gail Geller Lab primarily conducts empirical quantitative and qualitative research on the ethical and social implications of genetic testing in the adult, pediatric and family contexts. We have focused on clinical-patient communication under conditions of uncertainty; professionalism and humanism in medical education; cross-cultural variation in concepts of health and disease; and clinician suffering and moral distress. We explore these topics in a range of health care contexts, including genomics, complementary and alternative medicine (CAM) and palliative care. Our researchers have a longstanding interest in medical socialization, provider-patient communication under conditions of uncertainty and cultural differences in attitudes toward health and disease. We also explore the intersection of CAM and bioethics, as well as the role of palliative care in chronic diseases, such as muscular dystrophy and sickle cell disease.

The goal of research in the Beer Lab is to understand how gene regulatory information is encoded in genomic DNA sequence. Our work uses functional genomics DNase-seq, ChIP-seq, RNA-seq, and chromatin state data to computationally identify combinations of transcription factor binding sites that operate to define the activity of cell-type specific enhancers. We are currently focused on improving SVM methodology by including more general sequence features and constraints predicting the impact of SNPs on enhancer activity (delta-SVM) and GWAS association for specific diseases, experimentally assessing the predicted impact of regulatory element mutation in mammalian cells, systematically determining regulatory element logic from ENCODE human and mouse data, and using this sequence based regulatory code to assess common modes of regulatory element evolution and variation.

The Shenderov Lab focuses on the elucidation of the mechanisms of immune response and resistance to immunotherapy in Prostate Cancer. This has led to clinical and basic research investigating the presumptive checkpoint inhibitor B7-H3. In pursuit of understanding biomarkers or resistance and response, and regulatory molecules of immune response, we utilize artificial intelligence, immunogenomics, and spatial proteomics and transcriptomics in the laboratory and at the bedside using clinical trial correlative samples.

The Li Gao Lab researches functional genomics, molecular genetics and epigenetics of complex cardiopulmonary and allergic diseases, with a focus on translational research applying fundamental genetic insight into the clinical setting. Current research includes implementation of high-throughput technologies in the fields of genome-wide association studies (GWAS), massively parallel sequencing, gene expression analysis, epigenetic mapping and integrative genomics in ongoing research of complex lung diseases and allergic diseases including asthma, atopic dermatitis (AD), pulmonary arterial hypertension, COPD, sepsis and acute lung injury/ARDS; and epigenetic contributions to pulmonary arterial hypertension associated with systemic sclerosis.

Research in the Liliana Florea Lab applies computational techniques toward modeling and problem solving in biology and genetic medicine. We work to develop computational methods for analyzing large-scale sequencing data to help characterize molecular mechanisms of diseases. The specific application areas of our research include genome analysis and comparison, cDNA-to-genome alignment, gene and alternative splicing annotation, RNA editing, microbial comparative genomics, miRNA genomics and computational vaccine design. Our most recent studies seek to achieve accurate and efficient RNA-seq correction and explore the role of HCV viral miRNA in hepatocellular carcinoma.