Agilent Technologies Awards Two Integrated-Biology Research Grants

10-25-2011: Agilent Technologies Inc. announced the winners of the 2011 eMerging Insights grant program: Dr. Michael J. MacCoss of the University of Washington and Dr. Peter J. Park of Harvard University. Each will be awarded $75,000 toward their ongoing research on open source data-integration tools.

The first grant, for “Validating Protein Pathway Information: Integrating Proteomic Data with Transcriptomic or Metabolomic Data Sets,” was awarded to MacCoss, an assistant professor in the department of genome sciences at the University of Washington. His research is focused on stable isotope and mass spectrometry methods for studying biology at the molecular, cellular, and whole organism level. MacCoss’ lab developed and maintains the Skyline Targeted Proteomics Environment —a widely used, open-source proteomics software platform for building selected reaction-monitoring/multiple reaction-monitoring methods and analyzing the resulting data.

For the eMerging Insights Program, MacCoss and his team will expand Skyline’s capabilities to allow seamless integration with Agilent’s GeneSpring software. Their vision is to update Skyline to allow researchers to first analyze genome-wide association data, gene expression data, or discovery proteomics and metabolomics data using GeneSpring, and then use this information to rapidly generate a list of candidate proteins for further verification by mass spectrometry.
 
The second grant, for “Modeling Disease Progression: Combining Gene Expression and Copy Number Variation Data,” was awarded to Dr. Park, an associate professor of pediatrics at the Center for Biomedical Informatics at Harvard Medical School. He has developed a method, based on biclustering, to identify genes whose expression is driven by copy number variations (CNV).
 
For the eMerging Insights Program, Park and his team will extend this work to compare tumors at different grades, to gain insight into how CNV and gene expression drive disease progression. They will also develop this tool into a browser-based application that will allow biologists and clinicians to perform similar analyses with their own data sets and easily visualize and interpret the results. The proposed tool will enable accurate and robust integration of CNV and gene expression data, allowing the research community to gain greater insight into how changes at the sequence level affect gene expression and disease progression.