Future Trends: Translational Informatics James J. Cimino Chief, Laboratory for Informatics Development Mark O. Hatfield Clinical Research Center National.

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Presentation transcript:

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 DNA Transcription Replication RNA Amino Acids Proteins Structures Pathways Translation Folding PhenomePhenome Genome

The Genomic Timeline Bacterial Genome 1995 Human Genome DNA Structure

Translational Research The application of research findings in one domain of study to another, (usually broader) domain. Type 1 Type 2 Researchers Clinicians “Type 0”

Bioinformatics The Roles of Informatics Translational Informatics Clinical Knowledge Biologic 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? Definitely genetic Not a big protein! 5 types of tissue Billions of cells Coordination in time and space How many genes? How many variants? Phylogeny Ontogeny

Genomics of a Single Disease DNA G-A-G- -Pro-Glu-Glu Hemoglobin AStructureFunction G-T-G- -Pro-Val-Glu

Why is this so hard? DNA RNA Amino Acids Proteins Pathways Structures Replication Transcription Translation Folding Other Genes Environment Factors Inhibition Activation Mutations 3 billion base pairs in the human genome 100 trillion cells in the human body Denaturation

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