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npx versuz@latest install freedomintelligence-openclaw-medical-skills-skills-bio-ribo-seq-translation-efficiencygit clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.gitcp OpenClaw-Medical-Skills/SKILL.MD ~/.claude/skills/freedomintelligence-openclaw-medical-skills-skills-bio-ribo-seq-translation-efficiency/SKILL.md---
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
```python
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
```r
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.
```r
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
```python
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