Computational Biology & Bioinformatics Initiative at Greehey Children's Cancer Research Institute

The Computational Biology and Bioinformatics Initiative (CBBI) provides computational infrastructure, bioinformatics expertise, and interdisciplinary research to manage, distribute complex data sets, develop and perform bioinformatics tasks and statistical analyses in the area of genomics and systems biology. The CBBI aims to establish Greehey CCRI, as well as UT Health Science Center, and its researchers as competitive leaders in the application of new biomedical technologies and computational methods to the varieties of biomedical study. With all their faculty members from Department of Epidemiology and Biostatistics (DEB), The CBBI also provides a wide range of biostatistical assistance.  The supports include, but not limited to,

• Data analysis and microarray data quality control for microarray-based, genome-wide profiling experiment;
• Common genomic sequence-based bioinformatics tasks, such as sequence-alignment and genomic level annotation;
• Assistance to investigators with study planning, genomic-based experiment design, sample size and power analyses;
• Development of data analysis tools that will allow investigators to generate and validate new hypotheses based on the integration of genomic and clinical data;
• Support for data resources, tools, and protocols that will enable the investigators in sharing and applying genomic data to basic and translational research. Support for clinical informatics resources, including database design and application development expertise; and
• Other computational needs for basic/laboratory science, translational research, clinical-, and population-based research, including biomedical image analysis.

CBBI, directed by Dr. Yidong Chen, is an integral part of Greehey CCRI’s mission, and fully supported by DEB. By providing access to the expertise in data-driven and model-based computational research for investigators at Greehey CCRI, the CBBI opens new areas of research, enhances the quality and consistency of high-throughput data analysis and improves the UT Health Science Center's ability to support research in this genomic and systems

biology era.