bioRxiv preprint doi: https://doi.org/10.1101/704320; this version posted November 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. XPRESSyourself: Enhancing, Standardizing, and Automating Ribosome Profiling Computational Analyses Yields Improved Insight into Data Jordan A. Berg,1∗ Jonathan R. Belyeu,2 Jeffrey T. Morgan,1 Yeyun Ouyang,1 Alex J. Bott,1 Aaron R. Quinlan,2;4;5 Jason Gertz,3 Jared Rutter1;6∗ 1Department of Biochemistry, University of Utah, Salt Lake City, UT, USA, 84112. 2Department of Human Genetics, University of Utah, Salt Lake City, UT, USA, 84112. 3Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA, 84112. 4USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT, USA, 84112. 5Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA, 84112. 6Howard Hughes Medical Institute, University of Utah, Salt Lake City, UT, USA, 84112. ∗Address correspondence to:
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[email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/704320; this version posted November 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics.