Stratagene (www.stratagene.com) is addressing the issue of gaining more accurate and complete information from gene expression data through a gene set enrichment-analysis approach. The algorithms are based on those from the Broad Institute (www.broad.mit.edu) and look at sets of statistically significant gene changes between experiments or between the subjects and controls, according to David Edwards, Ph.D., director of software solutions.
The benefit, Dr. Edwards says, is that rather than focus on individual gene expression, you can look at a gene with some known biological function first and then locate commonalities with other genes or pathways. Dr. Edwards uses Broad subsets related to specific biopathways and diseases, and to physical location. “This method gives more clues faster and is an alternative to existing methods.” But, he emphasizes, “researchers should use multiple approaches to understanding biological function.”
Stratagene added Gene Set Enrichment Analysis to its software applications in August, Dr. Edwards says, offering the benefit of performing multiple analyses within one application. When he investigated biological function in lung cancer he also used gene ontology, standard expression analysis, and copy-variation analysis, he says.
The next endeavor, according to Dr. Edwards, is to combine data to look at overlapping groups and pathways, and to perform different experiments in the same system.
JMP® Genomics, statistical discovery software from SAS (www.sas.com), leverages the SAS and JMP, a business unit of SAS, platforms for genomics-specific analysis. The application was developed in response to the increasingly large data sets and more complex modeling needs of biologists, chemists, and biostatisticians. JMP Genomics enables researchers to run prebuilt SAS analytical methods on the desktop, according to Shannon Conners, Ph.D., JMP Genomics product manager. “JMP Genomics is a marriage of JMP and SAS,” she says.
The marriage yields “better visualization and heavy data manipulation, and you don’t need to know programming or SAS,” she says of the point-and-click interface. Dr. Conners notes, though, that “biostatisticians can look inside and see what we’re doing” and adapt existing SAS code to run customized programs.
JMP Genomics is used to identify patterns in high-throughput genetics, copy number, expression microarrays, and proteomics data. More than 100 analytical procedures help researchers generate a clearer vision of data quality and then apply sophisticated statistical modeling methods to determine relationships between experimental variables. Scientists can merge annotation from various sources or link directly to Ingenuity Systems’ (www.ingenuity.com) Pathways Analysis software for further functional analysis of results.
Because JMP is a platform, Dr. Conners emphasizes, “you aren’t limited to a specific analytic work flow letting users employ additional analyses beyond our prebuilt ones.” JMP Genomics features dynamically interactive graphics and analysis dialog boxes so researchers can explore data relationships using traditional and advanced statistical algorithms, Dr. Conners explains.
“In the future, users will combine data sets from multiple experiments, and those data sets will be really big,” she says. For IT departments, the ongoing migration of CPUs to a 64-bit environment will help deal with the larger data sets, and a 64-bit version of JMP Genomics is being planned. Another option is the expansion of the JMP Genomics platform to take advantage of a grid-computing environment for even faster, more efficient processing power.