DOTSeq

DOTSeq resolves ORF-level regulation in bulk and single-cell ribosome profiling (Ribo-seq) experiments with matched RNA-seq through a statistical framework implementing Differential ORF Usage (DOU) and ORF-level Differential Translation Efficiency (DTE).

Key Features:

• Differential ORF Usage (DOU): A beta-binomial generalized linear model with flexible dispersion modelling that quantifies changes in an ORF's relative contribution to a gene's translation output by modelling the expected proportion of Ribo-seq versus RNA-seq reads mapping to each ORF within a gene.
• ORF-level Differential Translation Efficiency (DTE): A negative binomial generalized linear model that captures changes in translation efficiency of individual ORFs across experimental conditions.
• ORF-level read summarization: Supports read summarization at the ORF level for both bulk and single-cell Ribo-seq data.
• End-to-end workflows: Provides workflows for ORF annotation, read summarisation, contrast estimation, and visualisation.

Scientific Applications:

• Genome-wide detection of differential ORF usage: Enables detection of ORF-level translational control events at scale across the genome.
• Single-cell Ribo-seq analysis: Provides ORF-level read summarisation for single-cell ribosome profiling experiments.
• Benchmarking translational regulation: Achieves superior sensitivity with near-nominal FDR across effect sizes for detecting ORF-level changes in relative translation output.

Methodology:

DOTSeq implements DOU using a beta-binomial generalized linear model with flexible dispersion modelling, and DTE using a negative binomial generalized linear model; benchmarks compare performance across effect sizes and technical noise levels.