Translational bioinformatics

Translational bioinformatics (TBI) is an emerging field in the study of health informatics, focused on the convergence of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes. Translational bioinformatics (TBI) is an emerging field in the study of health informatics, focused on the convergence of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. TBI employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes. Translational bioinformatics is a relatively young field within translational research. Google trends indicate the use of 'bioinformatics' has decreased since the mid 1990s when it was suggested as a transformative approach to biomedical research. It was coined, however, close to ten years earlier. TBI was then presented as means to facilitate data organization, accessibility and improved interpretation of the available biomedical research. It was considered a decision support tool that could integrate biomedical information into decision-making processes that otherwise would have been omitted due to the nature of human memory and thinking patterns. Initially, the focus of TBI was on ontology and vocabulary designs for searching the mass data stores. However, this attempt was largely unsuccessful as preliminary attempts for automation resulted in misinformation. TBI needed to develop a baseline for cross-referencing data with higher order algorithms in order to link data, structures and functions in networks. This went hand in hand with a focus on developing curriculum for graduate level programs and capitalization for funding on the growing public acknowledgement of the potential opportunity in TBI. When the first draft of the human genome was completed in the early 2000s, TBI continued to grow and demonstrate prominence as a means to bridge biological findings with clinical informatics, impacting the opportunities for both industries of biology and healthcare. Expression profiling, text mining for trends analysis, population-based data mining providing biomedical insights, and ontology development has been explored, defined and established as important contributions to TBI. Achievements of the field that have been used for knowledge discovery include linking clinical records to genomics data, linking drugs with ancestry, whole genome sequencing for a group with a common disease, and semantics in literature mining. There has been discussion of cooperative efforts to create cross-jurisdictional strategies for TBI, particularly in Europe. The past decade has also seen the development of personalized medicine and data sharing in pharmacogenomics. These accomplishments have solidified public interest, generated funds for investment in training and further curriculum development, increased demand for skilled personnel in the field and pushed ongoing TBI research and development. At present, TBI research spans multiple disciplines; however, the application of TBI in clinical settings remains limited. Currently, it is partially deployed in drug development, regulatory review, and clinical medicine. The opportunity for application of TBI is much broader as increasingly medical journals are mentioning the term 'informatics' and discussing bioinformatics related topics. TBI research draws on four main areas of discourse: clinical genomics, genomic medicine, pharmacogenomics, and genetic epidemiology. There are increasing numbers of conferences and forums focused on TBI to create opportunities for knowledge sharing and field development. General topics that appear in recent conferences include: (1) personal genomics and genomic infrastructure, (2) drug and gene research for adverse events, interactions and repurposing of drugs, (3) biomarkers and phenotype representation, (4) sequencing, science and systems medicine, (5) computational and analytical methodologies for TBI, and (6) application of bridging genetic research and clinical practice. With the help of bioinformaticians, biologists are able to analyze complex data, set up websites for experimental measurements, facilitate sharing of the measurements, and correlate findings to clinical outcomes. Translational bioinformaticians studying a particular disease would have more sample data regarding a given disease than an individual biologist studying the disease alone. Since the completion of the human genome, new projects are now attempting to systematically analyze all the gene alterations in a disease like cancer rather than focusing on a few genes at a time. In the future, large-scale data will be integrated from different sources in order to extract functional information. The availability of a large number of human genomes will allow for statistical mining of their relation to lifestyles, drug interactions, and other factors. Translational bioinformatics is therefore transforming the search for disease genes and is becoming a crucial component of other areas of medical research including pharmacogenomics. In a study evaluating the computational and economic characteristics of cloud computing in performing a large-scale data integration and analysis of genomic medicine, cloud-based analysis had similar cost and performance in comparison to a local computational cluster. This suggests that cloud-computing technologies might be a valuable and economical technology for facilitating large-scale translational research in genomic medicine. Vast amounts of bioinformatical data are currently available and continue to increase. For instance, the GenBank database, funded by the National Institute of Health (NHI), currently holds 82 billion nucleotides in 78 million sequences coding for 270,000 species. The equivalent of GenBank for gene expression microarrays, known as the Gene Expression Omnibus (GEO), has over 183,000 samples from 7,200 experiments and this number doubles or triples each year. The European Bioinformatics Institute (EBI) has a similar database called ArrayExpress which has over 100 000 samples from over 3,000 experiments. All together, TBI has access to more than a quarter million microarray samples at present.