Due to the recent explosion of datasets featuring epigenomics and molecular data, computational methods and quantitative genomics play an increasing role in providing effective approaches to analyzing and summarizing data. Computational tools are critical not only to directing the selection of key experiments, but also in formulating new testable hypotheses through detailed analysis of complex molecular information that is not achievable using traditional approaches alone.
The Harvard School of Public Health (HSPH) is taking a leading role in interdisciplinary research involving the computational analysis of complex relationships between genes and their environment as well as basic biological and quantitative sciences. The laboratory is dynamically involved in the activity of the Computational Epigenomics Working Group (coordinated by Drs. Lin and Baccarelli), which is dedicated to developing and applying novel approaches for genome-scale epigenomic analysis. Recent activity included establishing a standardized enhanced pipeline for bioinformatic and biostatistical analysis of 450K Methylation BeadChip data, as well as for reduced representation bisulfite sequencing (RRBS) data. The lab computational activities are conducted in collaboration with the HSPH Center for Health Bioinformatics and the Program in Quantitative Genomics. The Center for Health Bioinformatics is integral to the development and application of computational biology methods at HSPH, providing support in all aspects of data storage and management, data analysis (sequencing, array, biological context), methods development, training, and outreach. The Program of Quantitative Genomics is a school-wide interdisciplinary program in response to an increasing quantitative need in handling massive genetic, genomic and omic data. The program goal is to provide bioinformatic and biostatistical expertise and tools to help investigators at the Harvard School of Public Health in the development and application of quantitative methods especially for high-throughput omic data, and through interdisciplinary training in quantitative genomics.