Developing integrative analytical foundations for research

Research in the Computational Biology Section involves the application and development of advanced methods for analysis of genetic, genomic, and metabolomics data as well as applications of those methods to new datasets and data types. As part of data analysis and interpretation activities, we are responsible for gathering and managing the data, performing initial quality control, and integrating data sources with data analysis tools across the DMPI research spectrum. The Section of Computational Biology includes the DMPI Informatics Core that is available for collaboration on complex projects integrating multiple ‘omics modalities in cross-sectional or longitudinal settings. The core houses a DMPI designed and maintained database, PEDIGENE®, that facilitates data analysis. This integrative approach allows us to work in teams, bringing together experts in different areas and challenging us to learn new science that often requires development of new quantitative methods.

Our research focuses on identifying the best computational models for the underlying molecular physiology of human disease.  We have extensive experience in developing quantitative genetic models of human disease. These investigations often involve extensive simulation studies for which we have developed robust software.  We are extending methods development and evaluation to other ‘omics areas with a particular emphasis on multivariate data, such as that produced in metabolomics or gene expression experiments.   The scope and throughput of genetics data has increased rapidly with the advent of next-generation sequencing technologies.  We are actively involved in evaluation of statistical methods for multiple types of genetic sequence data to provide high confidence in the results of our ongoing analyses. 

In order to drive the integration across the advanced ‘omics platforms in the DMPI we are actively pursuing development of new bioinformatics and computational biology methods.  The goal of these methods is to efficiently handle the massive datasets and provide tools for interpreting and displaying the results effectively.  In addition, computational biology provides an in silico experimental platform for molecular physiology research. The extensive data already generated by the DMPI labs provides the substrate for these in silico experiments, as well as providing support for new laboratory experiments. The Computational Biology Section provides a critical backbone for the DMPI research programs, facilitating the flow of information from the laboratories into the database, and analysis by well-vetted techniques.  The informatics platforms, software, and analysis methods fosters the kind of team science that drives integration and innovation in the DMPI.

New software tools and simulations from the Computational Biology Section are available for download from the DMPI Website (1). We are an active group in methodology development and evaluation (2,3). Our analytical expertise has contributed to many research projects including the investigation of cardiovascular disease (4), diabetes, autism (5), cancer, and ophthalmic disease (6).