Bioinformatics/Biostatistics Core

Vanderbilt University, Nashville TN

Investigators

Linked publications & trials

Abstract

The Conte Bioinformatics and Biostatistics Core has a strong history of close interaction with Conte investigators including the current Center grant funding period, as evidenced by joint publications and the joint development of novel computational algorithms and user-friendly software applications. The Core will continue to sustain the increasing bioinformatics and biostatistics needs of the supported projects and pilot activities. As proposed in the projects, global characterization of epigenomic, transcriptomic, and proteomic changes in response to 5-HT signaling across the life-span will eventually help elucidate the complex molecular mechanisms underlying multiple brain disorders. However, we face tremendous challenges in processing, analyzing, integrating, and interpreting large and heterogeneous datasets generated by multi-level, data rich approaches. A major goal of the Core is to provide computational support to expedite scientific discoveries across multidimensional data sets. We achieve the goal by providing support for data storage, data sharing, and data mining. We utilize a formal data categorization scheme with four data levels that facilitate the location and exchange of data of interest. We provide support for experimental design and statistical data analysis, ensuring that experiments are well planned and powered and pursued with the standards of good statistical design. We perform data analysis using robust, modern statistical models as well as with exploratory graphical techniques. The Core provides support for pathway and network-based data integration and analysis that will be critical for the proposed genome- and transcriptome-wide characterizations of changes induced by developmental 5-HT signaling. The Core plays a critical role in facilitating the integration of data sets generated by individual projects, which can be best achieved through pathway and network-based data analysis and takes advantage of pathway-level statistical analysis and visualization.