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

Translation Efficiency

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  resultsresultspadj   

Using DESeq2 Interaction Model

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  conditionassay


dds  DESeqdds


res_te  resultsdds name  

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