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

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Nucleotide recoding RNA sequencing (NR-seq) methods like TimeLapse-seq, SLAM-seq, and TUC-seq are vital for studying RNA dynamics and life cycle regulation.
• Existing bioinformatic tools present limitations for comprehensive NR-seq data analysis.
• There is a need for advanced computational tools to fully leverage the potential of NR-seq data.

Purpose of the Study:

• To develop and introduce the EZbakR suite, comprising an R package (EZbakR) and a Snakemake pipeline (fastq2EZbakR).
• To provide a generalized and comprehensive workflow for preprocessing and analyzing NR-seq data.
• To enable advanced analyses, including multi-labeling, improved variance modeling, and dynamical systems modeling of RNA processing and localization.

Main Methods:

• Development of the fastq2EZbakR Snakemake pipeline for flexible preprocessing of NR-seq datasets.
• Creation of the EZbakR R package for comprehensive NR-seq data analysis.
• Implementation of generalized linear modeling for comparative analyses of estimated parameters.

Main Results:

• The EZbakR suite generalizes many aspects of the NR-seq analysis workflow.
• fastq2EZbakR enables read assignment to diverse genomic features (genes, exons, splice junctions).
• EZbakR supports multi-label analyses, improved hierarchical modeling for metabolic label incorporation variance, and generalized dynamical systems modeling for mRNA processing and subcellular transport.

Conclusions:

• The EZbakR suite significantly enhances the analytical capabilities for NR-seq data.
• Researchers can now perform more comprehensive and flexible analyses of RNA population dynamics and life cycle regulation.
• This suite empowers scientists to maximize the utility and insights derived from NR-seq experiments.