RNA Modification Level Estimation with pulseR.

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• RNA modifications are crucial for regulating gene expression and cellular processes.
• Existing methods for transcriptome-wide RNA modification analysis have limitations.
• The Ligase-Assisted Immunocapture sequencing (LAIC-seq) method enables separation of modified and unmodified RNA molecules.

Purpose of the Study:

• To develop a bioinformatics workflow for analyzing RNA-seq data generated by LAIC-seq.
• To infer gene-specific RNA modification levels on a transcriptome-wide scale.
• To provide estimates of uncertainty for RNA modification levels.

Main Methods:

• Utilized the pulseR package for statistical modeling of RNA-seq data from LAIC-seq experiments.
• Developed a workflow to analyze data with or without external normalization (spike-ins).
• Compared different model parametrizations, including log-odds and proportion of modified molecules.

Main Results:

• The workflow successfully infers gene-specific RNA modification levels from LAIC-seq data.
• It provides confidence intervals for gene expression and RNA modification levels, quantifying uncertainty.
• Demonstrated methods for data analysis with and without spike-in normalization.

Conclusions:

• The presented bioinformatics workflow offers a versatile approach for estimating RNA modification levels.
• It is applicable to any read-count-based experimental method for RNA modification analysis.
• The workflow enhances the characterization of RNA modification dynamics across the transcriptome.