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Future Trends: Translational Informatics James J. Cimino Chief, Laboratory for Informatics Development Mark O. Hatfield Clinical Research Center National Institutes of Health Institute for e-Health Policy, January 12, 2011 Genetics 101 Pathways Replication DNA Genome DNA Translation RNA Transcription Amino Acids Proteins Folding Structures P h e n o m e The Genomic Timeline DNA Structure Bacterial Genome Human Genome 1953 1995 2003 Translational Research The application of research findings in one domain of study to another, (usually broader) domain. “Type 0” Researchers Clinicians Type 1 Type 2 The Roles of Informatics Translational Informatics Biologic Knowledge Bioinformatics Clinical Knowledge Clinical Informatics Promise of Translational Informatics • Diseases predicted by genes • Effectiveness of prevention • Diseases indicated by activation • Appropriate testing • Drug dose, toxicity and interactions • Drug effectiveness Case Study • Patient with liver cancer and chest pain • Physician suspects pulmonary embolism • What is the best, least invasive test? • Will warfarin work to prevent further emboli? • What is the warfarin dose for this patient? • Will warfarin interact with other medications? How does the nose form? Phylogeny Phylogeny Ontogeny • • • • • • • Definitely genetic Not a big protein! 5 types of tissue Billions of cells Coordination in time and space How many genes? How many variants? Genomics of a Single Disease DNA ...16...17...18... -G-A-G- ...16...17...18... -G-T-G- Hemoglobin A Structure Function -Pro-Glu-Glu- ....5......6......7..... -Pro-Val-Glu- ....5......6......7..... 1956 1953 2003 Why is this so hard? Other Genes Pathways Activation Replication DNA DNA Denaturation Translation RNA Amino Acids Transcription Mutations Inhibition Proteins Folding Structures Environment Factors • 3 billion base pairs in the human genome • 100 trillion cells in the human body Types of Translational Informatics • Locating genetic sequences • Identifying genetic mutations • Tracking gene activation • Modeling protein folding • Simulating biologic pathways • Drug discovery • Personalized medicine The NIH and Translational Informatics • GenBank The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) – study disease-specific genetic differences • Database of Phenome and Genome (dbGAP) The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) – study disease-specific genetic differences • Database of Phenome and Genome (dbGAP) – archive of genotype-phenotype studies • Entrez The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) – study disease-specific genetic differences • Database of Phenome and Genome (dbGAP) – archive of genotype-phenotype studies • Entrez – Cross-resource search tool for translational queries • ClinSeq The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) – study disease-specific genetic differences • Database of Phenome and Genome (dbGAP) – archive of genotype-phenotype studies • Entrez – Cross-resource search tool for translational queries • ClinSeq – Complete sequencing of 1000 individuals • Biomedical Translational Research Information System (BTRIS) The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) – study disease-specific genetic differences • Database of Phenome and Genome (dbGAP) – archive of genotype-phenotype studies • Entrez – Cross-resource search tool for translational queries • ClinSeq – Complete sequencing of 1000 individuals • Biomedical Translational Research Information System (BTRIS) – reusing clinical research data (1.5 billion rows of data) • Infobuttons The NIH and Translational Informatics • GenBank – Over 100 million sequences (100 billion bases) • Genome-Wide Association Studies (GWAS) – study disease-specific genetic differences • Database of Phenome and Genome (dbGAP) – archive of genotype-phenotype studies • Entrez – Cross-resource search tool for translational queries • ClinSeq – Complete sequencing of 1000 individuals • Biomedical Translational Research Information System (BTRIS) – reusing clinical research data (1.5 billion rows of data) • Infobuttons – delivering translational knowledge to the point of care Now What? • This biology stuff is complicated • Translational research is about applying findings from one domain to another domain • Translational informatics is the key to communicating data and knowledge between domains • Translational informatics research is a new field • We still need: – Informatics research support (NCTR? NCTI? NIBI?) – Training (extramural and intramural) – Support for collaborative efforts (CTSAs) – Centralization of resources for efficiency and equity