Version Compatibility
Reference examples tested with: BioPython 1.83+, DESeq2 1.42+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package> then help(module.function) to check signatures
- R:
packageVersion('<pkg>') then ?function_name to verify parameters
- CLI:
<tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
ORF Detection
"Detect translated ORFs from my Ribo-seq data" → Identify actively translated open reading frames including uORFs and novel ORFs using 3-nucleotide periodicity as evidence of active translation.
- CLI:
RiboCode for periodicity-based ORF detection
- R:
ORFik for ORF quantification and annotation
RiboCode Workflow
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
| File | Description |
|---|
| *_ORF_result.txt | Detected ORFs with coordinates |
| *_ORF_result.html | Interactive visualization |
| *_binomial_test.txt | Statistical test results |
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
| 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