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In the following case studies, investigators describe how they use GenePattern to automate analyses and enable reproducible research:

If you've a GenePattern story to share, we'd love to hear about it: email the GenePattern team.

 

GenePattern at Harvard-Partners Center for Genetics and Genomics

The Gateway for Integrated Genomics-Proteomics Applications and Data (GIGPAD) is a software platform that allows investigators and clinicians at the Harvard-Partners Center for Genetics and Genomics (HPCGG) to share data and analysis results without compromising patient confidentiality. GIGPAD relies on GenePattern to provide the computational analysis framework for HPCGG laboratories. Eugene Clark, HPCGG senior software architect, explains why his team chose to incorporate GenePattern rather than build an in-house system: "Using GenePattern allows us to decouple bioinformatics from our main application infrastructure, thereby providing our biologists greater freedom to innovate without being constrained by formal software development practices."

"The GIGPAD-GenePattern integration centralizes data access, reduces processing time, and simplifies maintenance."

Prior to GIGPAD, each HPCGG lab maintained unique processes that required manual intervention, custom scripts, and IT support. Data and analysis results were difficult to share, manual processes time consuming, and parallel IT support expensive. Today the labs are fully automated. GIGPAD receives raw data files from the lab machines and sends the files to GenePattern for processing. GenePattern runs selected computational analysis pipelines and forwards the results to GIGPAD. HPCGG associates can review the raw data and analysis results without compromising patient confidentiality. The GIGPAD-GenePattern integration centralizes data access, reduces processing time, and simplifies maintenance.

"We wanted the labs to retain their independence, but enable collaboration by having a central location for data and analysis results," explains Clark. The computational analysis framework was a critical component of the laboratory infrastructure. The framework had to be flexible enough to allow each lab to adapt its own methodologies, rigorous enough to enable reproducible research, extensible, and maintainable. Clark chose GenePattern based on the benefits it offered:

Integrating GenePattern with GIGPAD makes it easy for the IT team to build, deploy, and maintain customized computational analysis pipelines for individual HPCGG laboratories.



GenePattern Used to Teach Atomistic Modeling at MIT

"A fracture in a concrete bridge doesn't begin as a long jagged scar; it starts off as a vibration at the atomic scale and progresses," explains Professor Markus Buehler of MIT's Department of Civil and Environmental Engineering. In his research, Buehler uses complex modeling algorithms to study how the molecular structure of a material relates to the material's response in large scale structures. As a professor, Buehler wants his students to understand and explore how building blocks at the nonscale define material properties at the macroscale. However, the complexity of the modeling algorithms and technical details of running the methods make them difficult for students to access. Working with Ivica Ceraj, a software developer in MIT's Office of Educational Information Technology, Professor Buehler found a solution to the problem: GenePattern.

"Now, students can learn the basics of atomistic modeling quickly... and adopt the methods for their own applications."

Prior to using GenePattern, atomistic simulations were difficult to carry out and required students to learn technical details of operating a Linux workstation before they could get to the heart of the numerical method. Ceraj created an interface between GenePattern and the software code Buehler uses in his own research on materials as disparate as collagen and concrete. The combination provides a simple-to-use, but very accurate tool for modeling the behavior of materials under extreme loading. Now, students can learn the basics of atomistic modeling quickly, apply the technique to predict the mechanical properties of various materials, and adopt the methods for their own applications.

For more information, see the article Think small! Think quickly! Atomistic model helps students visualize nanoscale problems published by the MIT News Office.

Updated on July 22, 2012 15:28