bio-ribo-seq-translation-efficiency

name	bio-ribo-seq-translation-efficiency
description	Calculate translation efficiency (TE) as the ratio of ribosome occupancy to mRNA abundance. Use when comparing translational regulation between conditions or identifying genes with altered translation independent of transcription.
tool_type	mixed
primary_tool	riborex

Version Compatibility

Reference examples tested with: DESeq2 1.42+, numpy 1.26+, 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

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Translation Efficiency

"Calculate translation efficiency from my Ribo-seq and RNA-seq" → Compute the ratio of ribosome occupancy to mRNA abundance per gene to identify translational regulation independent of transcription changes.

• R: riborex for differential TE with DESeq2 backend
• Python: Ribo-seq/RNA-seq count ratio with statistical testing

Concept

Translation Efficiency (TE) = Ribo-seq reads / RNA-seq reads

• TE > 1: Efficiently translated (more ribosomes per mRNA)
• TE < 1: Poorly translated
• Changes in TE indicate translational regulation

Calculate TE with Plastid

from plastid import BAMGenomeArray, GTF2_TranscriptAssembler
import pandas as pd
import numpy as np

def calculate_te(riboseq_bam, rnaseq_bam, gtf_path):
    '''Calculate translation efficiency per gene'''
    # Load transcripts
    transcripts = list(GTF2_TranscriptAssembler(gtf_path))

    # Load alignments
    ribo = BAMGenomeArray(riboseq_bam)
    rna = BAMGenomeArray(rnaseq_bam)

    results = []
    for tx in transcripts:
        if tx.cds_start is None:
            continue

        # Get CDS region
        cds = tx.get_cds()

        # Count reads
        ribo_counts = ribo.count_in_region(cds)
        rna_counts = rna.count_in_region(tx)  # Full transcript for RNA-seq

        # Normalize by length
        cds_length = sum(len(seg) for seg in cds)
        tx_length = tx.length

        ribo_rpk = ribo_counts / (cds_length / 1000)
        rna_rpk = rna_counts / (tx_length / 1000)

        if rna_rpk > 0:
            te = ribo_rpk / rna_rpk
        else:
            te = np.nan

        results.append({
            'gene': tx.get_gene(),
            'transcript': tx.get_name(),
            'ribo_counts': ribo_counts,
            'rna_counts': rna_counts,
            'te': te
        })

    return pd.DataFrame(results)

Differential TE with riborex

library(riborex)

# Load count matrices
# Rows = genes, columns = samples
ribo_counts <- read.csv('ribo_counts.csv', row.names = 1)
rna_counts <- read.csv('rna_counts.csv', row.names = 1)

# Sample information
sample_info <- data.frame(
    sample = colnames(ribo_counts),
    condition = factor(c('control', 'control', 'treated', 'treated'))
)

# Run riborex
results <- riborex(
    rnaCntTable = rna_counts,
    riboCntTable = ribo_counts,
    rnaCond = sample_info$condition,
    riboCond = sample_info$condition
)

# Significant differential TE
sig_te <- results[results$padj < 0.05, ]

Using DESeq2 Interaction Model

Goal: Test for differential translation efficiency between conditions using a formal statistical framework that separates transcriptional from translational regulation.

Approach: Combine Ribo-seq and RNA-seq counts into one matrix, fit a DESeq2 model with a condition-by-assay interaction term, and extract the interaction coefficient which represents differential TE.

library(DESeq2)

# Combine Ribo-seq and RNA-seq counts
counts <- cbind(ribo_counts, rna_counts)

# Design matrix with interaction term
coldata <- data.frame(
    condition = factor(rep(c('ctrl', 'ctrl', 'treat', 'treat'), 2)),
    assay = factor(rep(c('ribo', 'rna'), each = 4)),
    row.names = colnames(counts)
)

dds <- DESeqDataSetFromMatrix(
    countData = counts,
    colData = coldata,
    design = ~ condition + assay + condition:assay
)

dds <- DESeq(dds)

# The interaction term tests for differential TE
res_te <- results(dds, name = 'conditiontreat.assayribo')

Normalize Counts

def normalize_counts(counts_df, method='tpm'):
    '''Normalize count matrix'''
    if method == 'tpm':
        # TPM normalization
        rpk = counts_df.div(counts_df['length'] / 1000, axis=0)
        scale = rpk.sum(axis=0) / 1e6
        tpm = rpk.div(scale, axis=1)
        return tpm

    elif method == 'rpkm':
        # RPKM normalization
        total = counts_df.sum(axis=0)
        rpm = counts_df / total * 1e6
        rpkm = rpm.div(counts_df['length'] / 1000, axis=0)
        return rpkm

def calculate_te_matrix(ribo_tpm, rna_tpm):
    '''Calculate TE from normalized matrices'''
    # Add pseudocount to avoid division by zero
    te = (ribo_tpm + 0.1) / (rna_tpm + 0.1)
    return np.log2(te)  # Log2 TE

Interpretation

Log2 TE Change	Interpretation
> 1	Strong translational activation
0.5 - 1	Moderate activation
-0.5 - 0.5	No significant change
-1 - -0.5	Moderate repression
< -1	Strong translational repression

Related Skills

• rna-quantification - Get RNA-seq counts
• differential-expression - Compare expression
• orf-detection - Identify translated ORFs