bakR - Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.