Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite

Abstract

Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR (https://github.com/isaacvock/EZbakR), an R package to facilitate a mor comprehensive set of NR-seq analyses, and fastq2EZbakR (https://github.com/ isaacvock/fastq2EZbakR), a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data. Metabolic labeling is a powerful tool for tracking the rate at which RNAs are made, processed, and degraded. It involves treating cells with a modified nucleotide that is added as a building block into RNAs during the cell’s exposure to the nucleotide. While the modified building block is largely invisible to the cell, the identity of the labeled RNA molecules can be revealed using chemistry that recodes the hydrogen bonds of the label, thereby introducing apparent mutations in sequencing reads from labeled RNA. These nucleotide recoding/conversion RNA-seq (NR-seq) experiments require software tools to process and analyze NR-seq data, but existing tools lack the flexibility needed to best use these data. To address this gap, we developed the EZbakR suite, comprising a data processing pipeline (fastq2EZbakR) and an R package (EZbakR). The EZbakR suite helps users analyze their data in many ways (e.g., at the level of genes or isoforms), improves the quantification of labeled RNA, and supports complex experimental designs (e.g., subcellular fractionation, multi-factor perturbations). Its modularity also affords the flexibility to further extend and improve NR-seq analyses. We thus anticipate that the EZbakR suite will allow analyses of a wide range of NR-seq experiments to uncover new RNA biology.