Bioinformatics

A core resource for the CTSI community, the Bioinformatics Program helps translational and clinical researchers at BU apply biomedical informatics methods and tools to their research. The Bioinformatics Division has two principal components—a Translational Bioinformatics component, led by Avrum Spira, MD, MSc, and a Clinical Research Informatics component, led by William Adams, MD. The program’s design is user-friendly and interfaces with NIH and other institutions’ systems thanks to the open source, standards-based informatics for Integrating Biology and the Bedside (i2b2) platform.

The Bioinformatics Program aims to support informatics across the translational continuum by:

• Developing, demonstrating, and disseminating new solutions
• Collaborating with stakeholders in the CTSI hub community and across the CTSA network
• Adopting informatics best practices from other centers

Translational Bioinformatics

The Translational Bioinformatics Program helps researchers apply bioinformatics methods to their research. Our faculty strives to improve the diagnosis, prognosis, and treatment of human disease by applying computational approaches that leverage high-throughput molecular data.

The goals of the program are to:

• Offer expertise in applying bioinformatics to translational research.
• Establish a facility devoted to translational bioinformatics resources that supports the computational needs of researchers.
• Develop a translational bioinformatics education and training program that enables scientists in the biological sciences to apply computational tools to advance research.

The Translational Bioinformatics group includes:

• Avrum Spira, MD, MSc, Director of BU CTSI Clinical Research Informatics
• Marc Lenburg, PhD, Deputy Director of BU CTSI Clinical Research Informatics
• Adam Gower, PhD, Bioinformatics Scientist

openSESAME (Search of Expression Signatures Across Many Experiments)

CT

The openSESAME resource is being expanded to include all raw data from GEO that was generated using Affymetrix microarray platforms in samples from humans or from several mammalian model organisms, resulting in a resource with far greater potential for identifying conditions that give rise to shared gene coexpression. In addition, methods are in development to facilitate the identification of experimental variables that are associated with the regulation of a gene expression signature in a given dataset.

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