bio-ribo-seq-orf-detection

name	bio-ribo-seq-orf-detection
description	Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.
tool_type	mixed
primary_tool	RiboCode

ORF Detection

RiboCode Workflow

# Step 1: Prepare annotation
prepare_transcripts \
    -g annotation.gtf \
    -f genome.fa \
    -o ribocode_annot

# Step 2: Run RiboCode
RiboCode \
    -a ribocode_annot \
    -c config.txt \
    -l 27,28,29,30 \
    -o output_prefix

# config.txt format:
# SampleName  AlignmentFile  Stranded
# sample1     sample1.bam    yes

One-Step RiboCode

# All-in-one command
RiboCode_onestep \
    -g annotation.gtf \
    -r riboseq.bam \
    -f genome.fa \
    -l 27,28,29,30 \
    -o output_dir

RiboCode Output

File	Description
*_ORF_result.txt	Detected ORFs with coordinates
*_ORF_result.html	Interactive visualization
*_binomial_test.txt	Statistical test results

Parse RiboCode Results

import pandas as pd

def load_ribocode_orfs(filepath):
    '''Load RiboCode ORF predictions'''
    df = pd.read_csv(filepath, sep='\t')

    # ORF categories
    categories = {
        'annotated': df[df['ORF_type'] == 'annotated'],
        'uORF': df[df['ORF_type'] == 'uORF'],
        'dORF': df[df['ORF_type'] == 'dORF'],
        'novel': df[df['ORF_type'].isin(['novel', 'noncoding'])]
    }

    return df, categories

Alternative: RibORF

# RibORF uses random forest classifier
RibORF.py \
    -f genome.fa \
    -r riboseq.bam \
    -g annotation.gtf \
    -o output_dir

Manual ORF Detection

from Bio import SeqIO
from Bio.Seq import Seq

def find_orfs(sequence, min_length=30):
    '''Find all ORFs in a sequence'''
    start_codon = 'ATG'
    stop_codons = ['TAA', 'TAG', 'TGA']

    orfs = []
    seq = str(sequence).upper()

    for frame in range(3):
        for i in range(frame, len(seq) - 2, 3):
            codon = seq[i:i+3]
            if codon == start_codon:
                # Find next stop codon
                for j in range(i + 3, len(seq) - 2, 3):
                    if seq[j:j+3] in stop_codons:
                        orf_length = j - i + 3
                        if orf_length >= min_length:
                            orfs.append({
                                'start': i,
                                'end': j + 3,
                                'frame': frame,
                                'length': orf_length,
                                'sequence': seq[i:j+3]
                            })
                        

     orfs

 ():
    
    translated = []
     orf  orfs:
        cov = coverage_data[orf[]:orf[]]
         (cov) >= min_coverage:
            translated.append(orf)
     translated

uORF Analysis

def find_uorfs(transcript, cds_start):
    '''Find upstream ORFs before main CDS'''
    utr5 = transcript[:cds_start]
    uorfs = find_orfs(utr5)

    # Classify uORFs
    for uorf in uorfs:
        if uorf['end'] <= cds_start:
            uorf['type'] = 'contained'  # Fully in 5' UTR
        else:
            uorf['type'] = 'overlapping'  # Overlaps main CDS

    return uorfs

ORF Categories

Type	Description
annotated	Known CDS in annotation
uORF	Upstream of main CDS
dORF	Downstream of main CDS
internal	Within CDS, different frame
noncoding	In annotated non-coding RNA
novel	Unannotated region

ORFquant for ORF Quantification

ORFquant provides transcript-level and ORF-level quantification from Ribo-seq data.

Installation

# Install from Bioconductor
BiocManager::install('ORFik')
# ORFquant is part of the ORFik ecosystem

Basic ORF Quantification

library(ORFik)
library(GenomicFeatures)

# Load annotation
txdb <- makeTxDbFromGFF('annotation.gtf')

# Load Ribo-seq data
riboseq <- fimport('riboseq.bam')

# Get CDS regions
cds <- cdsBy(txdb, by = 'tx', use.names = TRUE)

# Calculate ORF-level RPKM
# fpkm: Fragments Per Kilobase per Million mapped reads
orf_counts <- countOverlaps(cds, riboseq)
orf_lengths <- sum(width(cds))
total_reads <- length(riboseq)
orf_fpkm <- (orf_counts * 1e9) / (orf_lengths * total_reads)

P-site Corrected Quantification

library(ORFik)

# Load with P-site offset correction
# p_offsets=c(12,12,12): P-site offset for 28-30nt reads. Determine from metagene.
riboseq <- fimport('riboseq.bam', p_offsets = c(12, 12, 12), lengths = 28:30)

# Count P-sites per ORF
psite_counts <- countOverlaps(cds, riboseq)

Detect and Quantify Novel ORFs

library(ORFik)

# Find candidate ORFs in 5' UTRs
utr5 <- fiveUTRsByTranscript(txdb, use.names = TRUE)
uorf_candidates <- findORFs(utr5, startCodon = 'ATG', longestORF = FALSE,
                            minimumLength = 9)  # 9 codons minimum

# Quantify uORFs
uorf_counts <- countOverlaps(uorf_candidates, riboseq)

# Filter by coverage
# min_count=10: Minimum reads for confident detection.
active_uorfs <- uorf_candidates[uorf_counts >= 10]

ORFquant Output Interpretation

# Create ORF summary table
orf_summary <- data.frame(
    orf_id = names(cds),
    length = sum(width(cds)),
    counts = orf_counts,
    fpkm = orf_fpkm
)

# Classify by expression
# fpkm>1: Low expression threshold. Adjust based on library depth.
orf_summary$expressed <- orf_summary$fpkm > 1

write.csv(orf_summary, 'orf_quantification.csv', row.names = FALSE)

Compare ORF Expression Across Conditions

library(DESeq2)

# Build count matrix for multiple samples
orf_count_matrix <- cbind(
    sample1 = countOverlaps(cds, riboseq1),
    sample2 = countOverlaps(cds, riboseq2),
    sample3 = countOverlaps(cds, riboseq3),
    sample4 = countOverlaps(cds, riboseq4)
)

# Run DESeq2 for differential translation
coldata <- data.frame(condition = c('control', 'control', 'treatment', 'treatment'))
dds <- DESeqDataSetFromMatrix(orf_count_matrix, coldata, ~ condition)
dds <- DESeq(dds)
results <- results(dds)

Related Skills

• ribosome-periodicity - Validate ORF calling
• translation-efficiency - Quantify ORF translation
• differential-expression - Compare ORF expression