Related Links
The Duke Bioinformatics Shared Resource
(DBSR) collaborates with Cancer Center investigators on projects using genomic data, including transcriptome (microarray, SAGE) data, proteomic data, and sequence data. The goal of the DBSR is to consult and collaborate on the statistical design and analysis of cancer projects that include these and other types of genomic data, including linking these data to clinical data. Another goal is to provide or develop algorithms and computational tools for data manipulation, annotation, and comparison with public data. A further goal is to provide education for researchers and biostatisticians. The DBSR will continue to organize the annual Critical Assessment of Microarray Data Analysis (CAMDA) conference, and will work closely with Cancer Center Information Systems (CCIS) on the National Cancer Insistute (NCI) effort to construct a cancer based biomedical framework, Cancer Biomedical Informatics Grid (caBIG program). The interaction with other Cancer Center cores and the proposed cores is critical. We also propose a close interaction with the CALGB Statistical Center, located at Duke and directed by Dr. Stephen George. Technical support for hardware and software will be provided by CCIS.
TIGR TM4 Microarray Software Suite (developed at TIGR)
The TM4 suite of tools consist of four major applications, Microarray Data Manager (MADAM), TIGR_Spotfinder, Microarray Data Analysis System (MIDAS), and Multiexperiment Viewer (MeV), as well as a Minimal Information About a Microarray Experiment (MIAME)-compliant MySQL database, all of which are freely available to the scientific research community at TIGR's Software Download Site. Although these software tools were developed for spotted two-color arrays, many of the components can be easily adapted to work with single-color formats such as filter arrays and GeneChips™(Affymetrix).
dChip
A program developed by Wing Wang's lab at Harvard known as DNA-Chip Analyzer (dChip). dChip is a model-based analysis of oligonucleotide expression arrays that uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes. dChip is described in Genome Biol. 2001;2(8):RESEARCH0032 and J Cell Biochem Suppl 2001;Suppl 37:120-5.
Resourcerer 5.0 (developed at TIGR)
RESOURCERER (Genome Biology 2001 PDF) provides annotation based on the TIGR Gene Indices (TGI) for commomly available microarray resources, including widely used clone sets and Affymetrix GeneChip Arrays. RESOURCERER also allows comparisons between resources from the same species using either TGI or UniGene and between species using the EGO database.
Affymetrix analysis tools
The NetAffx- Analysis Center is the most comprehensive resource of integrated array contents and functional annotations available. The flexible query capabilities provided help you retrieve biological information for specific probe sets. Go to this web site and register to use the annotation tool.
Affymetrix also has a Gene Ontology (GO) tool. Gene Ontology (Ashburner, Ball et al. 2000) is widely accepted as the standard for vocabulary describing the biological process, molecular function, and cellular component for genes. The Gene Ontology Mining Tool maps GeneChip probe sets to these hierarchical vocabularies. In addition to providing the GO terms for annotated genes, it provides graphical, interactive views of probe set representation within the biological process, molecular function, or cellular component hierarchies. These graphs allow the user to visualize input probe lists in the context of biological information and to visually determine the relationships among probe sets based on their locations in the GO graph, which aids in the biological interpretation of a complex set of results. As the graph is hierarchical, users can view their results within a context of broad or detailed GO categories. By clicking on a specific GO term in the output graph, a list of probe sets with annotations at or downstream to this term will be retrieved. This functionality allows the partitioning of probe sets based on the molecular function, biological process or cellular component of genes. Go to this web site and view how to use their tool.