Facility Details

The Sylvester Biostatistics and Bioinformatics Shared Resource (BBSR) provides expertise and analysis support for basic and translational research, clinical trials, and both interventional and non-interventional population-based studies. The BBSR offers support for data-intensive and large-scale data analysis projects. BSSR services include support for data analysis and interpretation; software access; algorithm and database development; support of scientific review, monitoring, and oversight of clinical and population science research protocols; consultation, training, educational workshops, lectures and seminars. The BBSR enables investigators to take advantage of advances in data acquisition in oncology research by employing cutting-edge data analysis tools and methods. Biostatistics services include support for the statistical design of clinical trials, population studies, and laboratory experiments; sample size determination and justification; statistical analysis plans; statistical support for data and safety monitoring of clinical trials; data analysis and interpretation; prediction model development using machine learning techniques; and database design and data management. Bioinformatics services include project design for high-throughput genomics studies; support for next generation sequencing data analysis (including whole transcriptome profiling; targeted region gene expression and single-cell RNA-seq; epigenomics profiling such as ChIP-seq and ATAC-seq; and exome or whole genome sequencing analysis); pathway analysis; microbiome data analysis; functional metagenomics analysis; drug sensitivity and genomic association analysis; genomic data integration using public databases; integration analysis of genomic data across multiple platforms; genomic data visualization; and bioinformatics methods, analysis pipeline, software and algorithm development. BBSR services can be utilized for QA/QC, including quality assessment of sequencing data, sample outlier detection, batch effect determination, evaluation of spike-in control methods, randomized block design, and other strategies for data normalization. T