Papolarity

Papolarity computes transcript-level polarity metrics and comparative regression slopes from Ribo-Seq coverage to quantify ribosome positional preferences and changes in ribosome distribution.

Key Features:

• Polarity Metric Computation: Computes a classic polarity metric for each transcript from Ribo-Seq data, reflecting ribosome positional preferences along mRNA.
• Relative Linear Regression Slope Estimation: Estimates a relative linear regression slope of coverage across transcript length when comparing against control samples to characterize changes in ribosome distribution due to perturbations such as stress, antibiotics, or genetic modifications affecting the translation machinery.
• De-noising and Profile Segmentation: Applies a Poisson model-based profile segmentation approach using pasio to de-noise Ribo-Seq coverage and aggregate signal within segmented profiles for more reliable slope estimation.

Scientific Applications:

• Translation dynamics analysis: Analyzing ribosome positional distributions to study translation dynamics at transcript resolution.
• Perturbation impact assessment: Quantifying how environmental stresses, antibiotics, or genetic modifications alter ribosome distribution along transcripts.
• Translation efficiency and fidelity inference: Inferring changes in translation efficiency and fidelity from shifts in polarity metrics and regression slopes.

Methodology:

Computes per-transcript polarity metrics, estimates relative linear regression slopes against control samples, and performs Poisson model-based profile segmentation via pasio to de-noise and aggregate Ribo-Seq coverage.