Abstract
RNA-seq and Ribo-seq are widespread quantitative methods for assessing transcription and translation. They can be used to detect differential expression, differential translation, and differential translation efficiency between conditions. The statistical power to detect differential genes is affected by multiple factors, such as the number of replicates, sequencing depth, magnitude of differential expression and translation, distribution of gene counts, and method for estimating biological variance. As power estimation of translational efficiency involves the combination of both RNA-seq measurements and Ribo-seq measurements, this task is particularly challenging. Here we propose a power assessment tool, called RiboSimR, based purely on data simulation. RiboSimR, produces semi-parametric simulations that generate data based on real RNA and Ribo-seq experiments, with customizable choices on baseline parameters and tool configurations. We demonstrate the usefulness of our tool by simulating data based on two published Ribo-seq datasets and analyzing various aspects of experimental design.
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Perkins, P., Stepanova, A., Alonso, J., Heber, S. (2020). RiboSimR: A Tool for Simulation and Power Analysis of Ribo-seq Data. In: Măndoiu, I., Murali, T., Narasimhan, G., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2019. Lecture Notes in Computer Science(), vol 12029. Springer, Cham. https://doi.org/10.1007/978-3-030-46165-2_10
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DOI: https://doi.org/10.1007/978-3-030-46165-2_10
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