bakR

bakR applies Bayesian hierarchical modeling to nucleotide recoding RNA-seq (NR-seq) data to detect differential RNA synthesis and degradation kinetics transcriptome-wide.

Key Features:

• Bayesian Hierarchical Modeling: bakR employs Bayesian hierarchical modeling to analyze nucleotide recoding RNA-seq (NR-seq) data, including TimeLapse-seq and SLAM-seq.
• Information Sharing Across Transcripts: The hierarchical model shares information across transcripts to increase statistical power for identifying subtle kinetic changes.
• Differential Kinetic Analysis: Provides a framework focused on differential RNA synthesis and degradation kinetics rather than differential expression.
• Validation on Simulated and Real Data: Performance and improved detection of differential kinetics were demonstrated through analyses of simulated data and real NR-seq datasets.

Scientific Applications:

• RNA Synthesis and Degradation Dynamics: Enables transcriptome-wide identification of differential synthesis and degradation rates from NR-seq data.
• Gene Regulation Studies: Provides kinetic-level insights into gene regulation mechanisms.
• Applications in Developmental Biology, Cancer Research, and Systems Biology: Supports studies in these fields by revealing kinetic changes in RNA populations.

Methodology:

bakR applies Bayesian hierarchical modeling that integrates information across transcripts to analyze nucleotide recoding RNA-seq data (e.g., TimeLapse-seq, SLAM-seq) and was validated on simulated and real datasets.