Cancer research continues to become increasingly data-driven and many investigative studies underway at Rutgers Cancer Institute of New Jersey rely upon analysis of multi-dimensional data sets, high-resolution imaging, next generation sequencing and other information-intensive technologies.
The Biomedical Informatics division shared resource, Bioinformatics, addresses these challenges through the use of high-throughput instrumentation, advanced data management systems, machine-learning technologies, high-performance cloud computing environments and state-of-the-art super-computing capabilities.
The overarching mission of Bioinformatics is to provide leading-edge data acquisition and analysis tools, computational informatics expertise, data analysis, and intensive training to foster advances in research and discovery in investigative oncology.
Application of these activities to genomic data from patient samples is enhancing patient care and initiating and sustaining productive collaborations among Rutgers Cancer Institute investigators and throughout the clinical and basic research community.
To optimize the support we provide to our basic, clinical and population research programs, Bioinformatics is organized into the following sections:
Genome Scale Analyses
Bioinformatics has made a sustained investment in computational hardware and software development to manage and process large data sets and provide new perspectives and informed insights. Bioinformatics developed pipelines to analyze RNA-Seq, Chip-Seq, and whole genome sequencing data using in-house shared memory computational platforms. Analysis includes the conduct of massively parallel sequence alignment/assembly studies through the utilization of data visualization tools (Galaxy, IGV from the Broad Institute) on shared memory and massively parallel supercomputers through shared resources at the Rutgers Discovery Informatics Institute.
Microarray Database and Analysis
Bioinformatics supports experimental design and analysis of microarray data, often generated by the Comprehensive Genomics shared resource, including pathway analysis and molecular modeling. We deploy and manage data backup/storage (dedicated server and disk/tape devices) to ensure that the data collected in a PI laboratory are automatically copied to dedicated server storage at the CINJ Data Center every night. The data are migrated monthly to an encrypted tape facility and shipped to a remote site.
Bioinformatics provides software and algorithm development for quantitative analysis of imaged pathology and radiology for clinical and pre-clinical imaging applications. This enables investigators to detect, quantify, and track tumor response to therapy in clinical trials to stratify patient populations and guide clinical decisions.
Clinical and Research IT
Bioinformatics planned and implemented a robust clinical data warehouse and data mining and discovery tools, including the development of interfaces to pathology reports and electronic patient records. Bioinformatics provides cyber-infrastructure support to telepathology pilot studies, linking instrumentation for remote viewing of tissue samples to support expert consultations and educational activities. Bioinformatics provides hardware maintenance, system administration, and data backup/disaster recovery of all imaging data.
Warehouse Services/Integrative Cancer Biology and Data Mining
Effective aggregation and management of data resources are critical to advancing clinical and translational science. Rutgers Cancer Institute has invested in and now leads several key translational and clinical projects that require advanced informatics infrastructure, resources, and technologies. Our enterprise-wide clinical data warehouse reliably integrates data from EMRs; CTMS, tumor registries, biospecimen repositories, radiology and pathology archives and next-generation sequencing. Together these assets provide data aggregation, management, warehousing, analysis, and visualization to advance precision medicine, data mining, outcomes assessment, and drug discovery.
Chemical Informatics Analysis
Bioinformatics uses small molecule/peptide databases for in silico screening studies of key enzymes and receptors to identify lead targets for design and development of novel therapeutics. Molecular dynamics techniques are applied to develop a broader understanding of the biophysical significance of a given mutation and the estimation of drug-receptor complex binding free energies. Software packages used for docking (in silico screening) include Autodock, UCSF DOCK, Gold, and Vina. Software used for molecular dynamics includes Amber, Gromacs, and NAMD. Quantum mechanics programs like Gaussian and Spartan are used to study physical properties of small molecule drugs. The resource uses the Modeler program for homology modeling and the Rosetta package for ab initio protein folding.