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, including TimeLapse-seq, SLAM-seq, and TUC-seq, are essential for studying transcript population dynamics and RNA processing.
• Existing bioinformatics tools for NR-seq data analysis are limited, hindering comprehensive investigations into the RNA life cycle.

Purpose of the Study:

• To develop a comprehensive bioinformatics suite, EZbakR, for advanced analysis of NR-seq data.
• To overcome current limitations in NR-seq data analysis, enabling deeper insights into RNA biology.

Main Methods:

• Developed EZbakR, an R package, and fastq2EZbakR, a Snakemake pipeline, for NR-seq data preprocessing and analysis.
• Implemented generalized modeling for multi-label NR-seq data (e.g., dual labeling with s4U and s6G).
• Enhanced dynamical systems modeling for premature mRNA processing and subcellular RNA transport analysis.

Main Results:

• The EZbakR suite generalizes NR-seq analysis workflows, allowing read assignment to diverse genomic features.
• EZbakR supports advanced mutational modeling, including multi-label analyses and improved hierarchical modeling for metabolic label incorporation.
• The suite enables flexible and powerful comparative analyses using generalized linear modeling.

Conclusions:

• The EZbakR suite significantly enhances the analytical capabilities for NR-seq data.
• Researchers can now perform more comprehensive and effective analyses of transcript dynamics and RNA processing using NR-seq data.
• This toolset empowers deeper exploration of the RNA life cycle and related regulatory mechanisms.