DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Transcriptomics typically analyzes mature spliced mRNA.
• Investigating both spliced and unspliced (precursor) mRNA offers insights into gene regulation and expression dynamics.
• Current methods for spliced/unspliced inference often lack group comparison capabilities and struggle with quantification uncertainty.

Purpose of the Study:

• To develop a robust method for comparing spliced and unspliced mRNA abundance between experimental conditions.
• To address and model the high degree of quantification uncertainty inherent in spliced/unspliced mRNA data.
• To provide a flexible tool applicable to both bulk and single-cell RNA sequencing.

Main Methods:

• Developed DifferentialRegulation, a Bayesian hierarchical model.
• Employed a latent variable approach to model quantification uncertainty by allocating reads to their gene/transcript and splice version.
• Designed benchmarks to evaluate performance against existing methods.

Main Results:

• DifferentialRegulation demonstrates strong performance in sensitivity and error control in benchmark tests.
• The method effectively handles quantification uncertainty arising from multi-mapping reads.
• The tool is validated for use with both bulk and single-cell RNA sequencing data.

Conclusions:

• DifferentialRegulation provides a powerful new approach for differential analysis of spliced and unspliced mRNA.
• The method enhances the ability to study gene regulation and expression changes across different experimental conditions.
• The Bioconductor R package offers a flexible and accurate solution for the research community.