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SAS Unveils JMP® Software for Genomics Applications
Product News

SAS Unveils JMP® Software for Genomics Applications

SAS Unveils JMP® Software for Genomics Applications
Product News

SAS Unveils JMP® Software for Genomics Applications

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SAS has announced the release of JMP® Genetics, JMP® Microarray, and JMP® Proteomics, three specialized genomics products for desktop statistical analysis of DNA, RNA and protein data.

The suite of products is designed to provide an integrated environment for accessing, subsetting, analyzing and exploring data patterns that can lead to the identification of new drugs.

The products use JMP software as a dynamic data visualization and statistical analysis desktop client to SAS.

85 genomics processes employ the JMP Scripting Language (JSL) to launch customizable SAS® macro programs in the background, enabling unparalleled data processing and statistical capabilities.

The suite of genomics products offers Design of Experiments (DOE) tools for creating efficient and unconfounded experiments; a wide range of input processes for 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.

JMP Genetics is designed to provide a rich 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 Microarray offers an extensive and flexible 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.

Early adopters have used the products since October 2005. "JMP is the strongest statistical tool I've seen for exploratory data analysis," said Dr. Jason Osborne, assistant statistics professor at North Carolina State University.

"For example, I can select a subset of genes that exhibit significant expression across certain conditions and then examine their behavior in a variety of different graphs and plots to see if they behave similarly."