Bioinformatics linkage of heterogeneous clinical and genomic information in support of personalized medicine

Bioinformatics Linkage of Heterogeneous Clinical and Genomic Information in Support of Personalized Medicine

Frey LJ, Maojo V, Mitchell JA

Methods Inf Med. 2007;46 Suppl 1:98-105'

The authors review the complexities of modeling a combination of clinical medicine along with the relationships between genotype, phenotype, and environment to lead to better diagnostic and therapeutic treatments in support of personalized medicine. Different data repositories will need to be integrated to understand the significance and cost effectiveness of specific biomarkers. This paper reviews research on the linkage of bioinformatics with clinical data, the current applications, and discusses issues in bridging the gap between genotype data and clinical practice.

Biological data repositories are rapidly growing and have different data consistencies and completeness. For researchers to integrate information and extract it for usable patterns, the representation of genotype-phenotype information requires standardization to overcome many differences. These differences include variation in semantics, experiments, techniques, and procedures. Common data models are needed to standardize the representation of the scientific domain knowledge about the data in a meaningful way and to create re-usable objects or objects that are easier to map across systems. There is also a lack of ontologies to map the existing gaps between heterogeneous data sources. Currently no worldwide standard exists to represent genotype-phenotype data models for information storage and exchange.

Many initiatives are underway to combine clinical and genomic data for research, clinical trials, and treatment. Overviews of several are discussed, including initiatives at the National Cancer Institute, the Advanced Clinico-Genomics Trials on Cancer by the European Commission, the NIH, an international consortium effort called the Polymorphism Markup Language, and several other organizations.

Comments: The authors provide an overview of several issues that need to be overcome to integrate genotype-phenotype data. A common domain model and ontologies are discussed as integration requirements between the various sources to store and exchange data “in scientifically meaningful and productive systems” to support research and personalized medicine.