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Ribotricer Detect ORFs Specifications

Process Details

  • Name: ribotricer_detect_orfs
  • Process UUID: g009fu04su04cj2get6z0c1i1lcqlb
  • Process Group: ribosome_profiling

Overview

This process uses Ribotricer to detect actively translated open reading frames (ORFs) from ribosome profiling data. The tool identifies ORFs by analyzing ribosome footprint patterns and calculating phase scores to distinguish genuine translation events from background noise. The process automatically selects genome-specific cutoff values optimized for different species and genome builds.

This process is implemented in Bash, which invokes the Ribotricer command-line tool for ORF detection analysis.

Key Functionality

  • ORF Detection: Identifies actively translated open reading frames from ribosome profiling BAM files
  • Phase Score Analysis: Calculates phase scores to assess translation quality and filter genuine ORFs
  • Genome-Specific Optimization: Applies pre-optimized cutoff values tailored to different species and genome builds
  • Multi-Species Support: Compatible with human, mouse, rat, zebrafish, C. elegans, Drosophila, and yeast genomes

Input/Output Specification

Inputs

Required Inputs

  • Input File TSV

    • Description: Tab-separated values file containing metadata or configuration information
    • Format: TSV

  • BAM File

    • Description: Set of aligned ribosome profiling sequencing reads in BAM format
    • Format: BAM

Outputs

  • Output File TXT
    • Description: Text file containing detected ORFs with their coordinates, phase scores, and translation metrics
    • Format: TXT

Parameters & Settings

This process uses pre-configured genome-specific phase score cutoffs that are automatically selected based on the genome build being analyzed. No user-configurable parameters are available in the Foundry UI.

References & Resources

  • Tool Documentation: Contact the team for details on Ribotricer implementation
  • Related Papers: Choudhary, S., Li, W. & Smith, A.D. Accurate detection of short and long active ORFs using Ribo-seq data. Bioinformatics 36, 2053–2059 (2020). https://doi.org/10.1093/bioinformatics/btz878