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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Over the past decade, gene expression microarray data has become an important tool for biologists to understand molecular processes and mechanisms on the whole-genome scale. Microarray data provides a window into the inner workings of the transcriptional process that is vital for cellular maintenance, development, biological regulation, and disease progression. While a rapidly increasing amount of microarray data is being generated for a wide variety of organisms, there is a severe lack of methods designed to utilize the vast amount of data currently available. In my work, I explore several techniques to meaningfully harness large-scale collections of microarray data both to provide biologists with a greater ability to explore data repositories, and to computationally utilize these repositories to discover novel biology.
First, effective search and analysis techniques are required to guide researchers and enable their effective use of large-scale compendia. I will present a user-driven similarity search algorithm designed to both quickly locate relevant datasets in a collection and to then identify novel players related to the user’s query. Second, I will describe novel methods that allow users to simultaneously visualize multiple datasets with the goal of providing a larger biological context within which to understand these data. Finally, I will discuss how we have used these approaches to discover novel biology, including successfully directing a large-scale experimental investigation of S. cerevisiae mitochondrial organization and biogenesis.
Bio
Matthew Hibbs is a computational biologist working in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University, where he recently earned his PhD under the guidance of Olga Troyanskaya and Kai Li. Matt’s research interests are focused on incorporating expert biological knowledge into the computational analysis of high-throughput genomic data.
