Bio Ribo Seq Orf Detection

Goal: Detect actively translated ORFs from Ribo-seq data using 3-nucleotide periodicity as evidence of translation.

Approach: Prepare transcript annotations, then run RiboCode with specified read lengths to identify ORFs with significant periodicity.

# 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

Goal: Run the complete ORF detection pipeline in a single command without separate annotation preparation.

Approach: Use RiboCode_onestep which combines annotation preparation, offset determination, and ORF calling.

# 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

Parse RiboCode Results

Goal: Load RiboCode ORF predictions and categorize them by type (annotated, uORF, dORF, novel).

Approach: Read the tabular output into a DataFrame and split by the ORF_type column.

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

Goal: Detect translated ORFs using a machine learning classifier as an alternative to periodicity-based methods.

Approach: Run RibORF's random forest model on aligned Ribo-seq reads and genome annotation.

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

Manual ORF Detection

Goal: Find all potential ORFs in a sequence and filter by Ribo-seq coverage to identify translated ones.

Approach: Scan all three reading frames for start-to-stop codon pairs, then retain ORFs with sufficient ribosome footprint coverage.

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]
                            })
                        break

    return orfs

def detect_translated_orfs(orfs, coverage_data, min_coverage=10):
    '''Filter ORFs by Ribo-seq coverage'''
    translated = []
    for orf in orfs:
        cov = coverage_data[orf['start']:orf['end']]
        if sum(cov) >= min_coverage:
            translated.append(orf)
    return translated

uORF Analysis

Goal: Identify upstream open reading frames in the 5' UTR that may regulate main CDS translation.

Approach: Extract the 5' UTR before the annotated CDS start and scan for ORFs, classifying each as contained or overlapping.

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

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