Strategically located between the University of North Carolina at Chapel Hill, North Carolina State University, and Duke University, Research Triangle Park (RTP) has been a cornerstone of the biotechnology industry since its earliest days. More recently, advancements in biotechnology have created a bottleneck of information. Trying to find tangible, druggable targets has proved difficult, tying up much of researchers’ valuable time. New visualization techniques can make it easier and provide more insight into these large and complex sets of data.
It should come as no surprise that a park that boasts large pharmas and biotechs, such as GlaxoSmithKline(www.gsk.com) and Biogen IDEC(www.biogen.com), alongside IT powerhouses, such as IBM (www.ibm.com) and SAS (www.sas.com), would emerge as a leader in bioinformatics.
The Research Triangle Regional Partnership (RTRP) is a public-private organization that promotes economic development for the 13-county Research Triangle region of North Carolina. The region comprises the counties of Chatham, Durham, Franklin, Granville, Harnett, Johnston, Lee, Moore, Orange, Person, Vance, Wake and Warren.
The RTRP fosters the collaborations among universities, industry, and government that have become the hallmark of North Carolina’s bioscience industry. The park has more than 140 organizations with disciplines ranging from information technology, biotechnology, and materials sciences. The diverse industry of RTP includes biotechnology/biopharmaceuticals, pharmaceuticals, chemistry, and computer software/hardware.
More than 70 universities, technology firms, clinical research organizations, software companies, and others, based in RTP and around the state, have formed the North Carolina Genomics and Bioinformatics Consortium. The Consortium includes IBM, SAS Institute, Quintiles(www.quintiles.com), Sigma-Aldrich(www.sigma-aldrich.com), Duke, and Wake Forest University.
RTP is home to the IBM BladeCenter solution for bioinformatics, an innovative solution designed to deliver affordable, high-throughput computing with greater ease of deployment. Until now, maximizing system throughput for an unpredictable bioinformatics environment may have strained the budgets of companies needing to maximize their IT investment.
The IBM BladeCenter solution for bioinformatics is designed to reduce the overall cost of computing. The PowerPC 970 processor-based blade servers are cost-effective, less power-hungry, and easier to setup than traditional server alternatives, IBM reports. The Linux operating system, as well as open-source applications and tools, can also help minimize cost.
According to IBM, a plug-and-play integrated infrastructure makes it easy to add capacity and consolidate sprawling server farms, storage, and networking using just a few superdense racks. The modular design of the BladeCenter solution for bioinformatics is used for high-throughput workloads. It adapts to handle mixed job streams and application downloads. Jobs can be initiated on servers when they become available, avoiding the constraints associated with dedicating jobs to specific servers. This same modular design enables the scale-up of processors, storage, and networking capacity.
Applications can be mapped to the hardware architecture that works best. IBM Director manages the entire system, helping to simplify and automate IT tasks. IBM Director also enables the user to deploy, configure, manage, and maintain dozens or hundreds of blade servers.
SAS’ JMP® Genetics, JMP® Microarray, and JMP® Proteomics are three specialized genomics products for desktop statistical analysis of DNA, RNA, and protein data. The suite of products provides an integrated environment for accessing, subsetting, analyzing, and exploring data patterns that can lead to the identification of promising new drugs. The new products use JMP software as a data visualization and statistical analysis desktop client to SAS.
More than 85 genomics processes employ the JMP Scripting Language (JSL) to launch customizable SAS® macro programs in the background, enabling data processing and statistical capabilities.
The suite of genomics products offers DOE tools for creating efficient and unconfounded experiments; a wide range of input processes for popular genomic instrumentation; deep and broad statistical methods that optimize tradeoffs between sensitivity and specificity; and integrated links to many bioinformatics annotation tools and Web sites, according to SAS.
JMP Genetics provides a class of methods for characterizing genetic variability and evaluating its association with biological phenotypes such as quantitative traits, chemical response, or small molecule expression. JMP Micro-array offers a library of statistical capabilities for making transcript abundance discoveries. JMP Proteomics facilitates the analysis of spectral data and the identification of biomarkers associated with biological effects such as disease or adverse events.