Ribosome profiling delivers quantitative information on the number and behavior of translating ribosomes, and gives profiles of gene expression much more closely linked to actual protein levels. The ribosomal profiling technology, however, is not yet widely adopted, and a major reason is that essential computational methods for analyzing ribosomal profiling data are lacking. Through this project we will develop a computational framework to address a set of specific problems in analyzing ribosomal profiling data, including data-driven ORF identification, correcting measures of translation for the influence of ribosome stalling, and a probabilistic model to estimate ribosome elongation speed from time-series data. Specific experiments will be conducted to support and challenge the development of computational methods, and major refinements to existing experimental technology for conducting ribosomal foot-printing will also be developed. Ultimately, through completing the aims of this proposal, we will take major steps in bringing ribosomal profiling technology into broader use in defining molecular phenotypes of cells.
Translational regulation is a major component of gene expression. Ribosomal profiling is a new technology that enables scientists to study the activities and functions of ribosomes during translation. Computational methods are lacking to properly analyze ribosomal profiling data, and this project will develop the required computational methods to fill this need.
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