RiboVIEW

RiboVIEW performs quality control, visualization, and statistical analysis of ribosome profiling data to assess translation dynamics from high-throughput sequencing of ribosome-protected mRNA footprints.

Key Features:

• RiboQC: Provides automated bioinformatic and statistical diagnostics for quality control of ribosome profiling datasets.
• Periodicity assessment: Evaluates three-nucleotide periodicity of ribosome-protected footprints.
• Ligation and digestion diagnostics: Assesses ligation efficiency and nuclease digestion of footprints.
• Reproducibility analysis: Quantifies reproducibility across biological or technical replicates.
• Batch effect detection: Detects batch effects in ribosome profiling experiments.
• Drug-artifact detection: Identifies potential drug-related artifacts affecting footprint patterns.
• Ribosome position visualization: Visualizes genome-wide ribosome positions from ribosome-protected mRNA footprints.
• RiboMine (ribosome speed estimation): Provides unbiased estimation of ribosome speed to analyze translation dynamics.
• Codon-level analysis: Analyzes codon enrichment and variability among mRNAs at the A, P, and E sites.
• Statistical analysis framework: Implements statistical methods to identify causal or confounding factors affecting translation.
• R pipeline: Implements analyses within an R pipeline.
• Output format: Generates results as HTML pages.

Scientific Applications:

• Quality control of ribosome profiling: Standardizes diagnostics for assessing data quality from ribosome-protected mRNA footprints.
• Genome-wide translational analysis: Enables detection and characterization of genome-wide changes in translation.
• Translation dynamics investigation: Uses ribosome speed estimation to study translation elongation and pausing.
• Codon and site-specific studies: Facilitates analysis of codon enrichment and variability at the A, P, and E sites.
• Artifact and batch effect identification: Detects ligation/digestion issues, drug-related artifacts, and batch effects that confound interpretation.
• Identification of causal/confounding factors: Applies statistical analyses to distinguish causal signals from confounders in translational regulation studies.

Methodology:

Automated bioinformatic and statistical diagnostics implemented in an R pipeline, with RiboQC for quality control, RiboMine for unbiased ribosome speed estimation, and results rendered as HTML pages.