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The Medical Faculty Mannheim provides clinical training to medical students at Ruprecht Karls University of Heidelberg and is also the faculties’ central provider of research services.
For more than 20 years, Gretz has directed a team of ZMF researchers examining the molecular basis of polycystic kidney disease (PKD) – a condition characterized by the growth of multiple cysts in both kidneys. PKD is genetically transmitted and afflicts 1 in 1,000 people in the general population. Left untreated, it progresses to end-stage renal failure in 50 percent of patients afflicted by the most severe forms of the disease. As the disease progresses, patients today have limited treatment options, the most common being either dialysis or kidney transplant.
Gretz began using JMP Genomics for microarray analysis in 2007, soon after it was introduced to the market. In the first 18 months, ZMF scientists used JMP Genomics from SAS to process and analyze nearly 5 terabytes of raw data. Adoption of JMP Genomics “was a logical evolution,” Gretz says. For nearly 30 years, Gretz had used SAS® software to analyze the vast volumes of data associated with genomic research. JMP Genomics combines the high-powered analytics of SAS with the point-and-click ease and interactive data visualization capabilities of JMP, a desktop product from SAS. It’s a combination that works well for Gretz.
“The output is easier to handle, the graphs are more convenient, and you still have the functionality of SAS,” he says. “It’s really a complete workflow.”
For Gretz’s team, JMP Genomics helps in five steps of the research process:
Verification of microarray data quality.
Incorporation of updated probe set annotation to keep up with advances in the understanding of genome structure and gene organization.
Assessment of the statistical significance of detected differences using flexible mixed models.
Principal component analysis and hierarchical clustering of samples and genes.
Pathway analysis – a “crucial step” that assesses whether certain gene sets clustered in functional pathways show more expression changes than would be expected by chance.
“With JMP Genomics we can quickly advance investigations from raw microarray data to analysis results,” Gretz says. “Formerly, the validation of results took several weeks to achieve, but today we get the results in just a few hours. We can very easily implement additional analysis processes, giving us a decisive advantage in our daily research.”
For more than 20 years, Gretz has directed a team of ZMF researchers examining the molecular basis of polycystic kidney disease (PKD) – a condition characterized by the growth of multiple cysts in both kidneys. PKD is genetically transmitted and afflicts 1 in 1,000 people in the general population. Left untreated, it progresses to end-stage renal failure in 50 percent of patients afflicted by the most severe forms of the disease. As the disease progresses, patients today have limited treatment options, the most common being either dialysis or kidney transplant.
Gretz began using JMP Genomics for microarray analysis in 2007, soon after it was introduced to the market. In the first 18 months, ZMF scientists used JMP Genomics from SAS to process and analyze nearly 5 terabytes of raw data. Adoption of JMP Genomics “was a logical evolution,” Gretz says. For nearly 30 years, Gretz had used SAS® software to analyze the vast volumes of data associated with genomic research. JMP Genomics combines the high-powered analytics of SAS with the point-and-click ease and interactive data visualization capabilities of JMP, a desktop product from SAS. It’s a combination that works well for Gretz.
“The output is easier to handle, the graphs are more convenient, and you still have the functionality of SAS,” he says. “It’s really a complete workflow.”
For Gretz’s team, JMP Genomics helps in five steps of the research process:
Verification of microarray data quality.
Incorporation of updated probe set annotation to keep up with advances in the understanding of genome structure and gene organization.
Assessment of the statistical significance of detected differences using flexible mixed models.
Principal component analysis and hierarchical clustering of samples and genes.
Pathway analysis – a “crucial step” that assesses whether certain gene sets clustered in functional pathways show more expression changes than would be expected by chance.
“With JMP Genomics we can quickly advance investigations from raw microarray data to analysis results,” Gretz says. “Formerly, the validation of results took several weeks to achieve, but today we get the results in just a few hours. We can very easily implement additional analysis processes, giving us a decisive advantage in our daily research.”
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