DNA Microarray Shared Resource

DNA微阵列共享资源（DMSR）的目标是促进前沿技术的应用

The goal of the DNA Microarray Shared Resource (DMSR) is to facilitate the application of cutting-edge microarray technology and analysis techniques by Cancer Center members. Services provided include the production of catalog and custom spotted microarrays, support of the use of multiple formats of Stanford and commercial microarrays, assistance with experimental design, archiving of array data and bioinformatics tools for the annotation, analysis, visualization and publication of microarray data. DMSR combines the resources of the Stanford Functional Genomics Facility, which develops new technology for genomics, and the Stanford Microarray Database, which is the largest and mostly widely used public database supporting microarray research. DMSR provides added value to Cancer Center members by decreasing the financial barriers associated with microarray experiments and providing the necessary training and expertise needed for the design, execution, analysis and publication of microarray experiments. Our strongly integrated genomics and informatics groups routinely interact with Cancer Center members to ensure that the microarray data generated are high quality and securely archived for analysis and interpretation as well as publication and long-term access. The current success of this Shared Resource is demonstrated by the high-impact publications that have been supported. As of July 2005, 59 cancer studies supported by the Shared Resource have been published, 14 of which were published in 2005. In fiscal year 2005, use of the microarray and data services totaled 15,937, of which 51% are by Cancer Center members from nine out of ten Research Programs. Future plans for this Shared Resource include expansion of the genomics efforts into all forms of microarray-based assays including antibody arrays, protein arrays, and reverse-phase cell lysate arrays. In addition, our bioinformatics capabilities will be significantly expanded by the incorporation of novel approaches for quality assessment, data analysis and annotation of data with cancer-related information.