---
name: ribo-seq-riboseq-preprocessing
description: Preprocess ribosome profiling data including adapter trimming, size selection, rRNA removal, and alignment. Use when preparing Ribo-seq reads for downstream analysis of translation.
tool_type: cli
primary_tool: bowtie2
---
## Version Compatibility
Reference examples tested with: Bowtie2 2.5.3+, STAR 2.7.11+, cutadapt 4.4+, numpy 1.26+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- 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.
# Ribo-seq Preprocessing
**"Preprocess my ribosome profiling data"** -> Trim adapters, size-select ribosome-protected fragments (26-34 nt), remove rRNA contamination, and align to the transcriptome for translation analysis.
- CLI: `cutadapt` -> `bowtie2` (rRNA removal) -> `STAR` (genome alignment)
## Workflow Overview
```
Raw Ribo-seq FASTQ
|
v
Adapter trimming (cutadapt)
|
v
Size selection (25-35 nt typical)
|
v
rRNA removal (SortMeRNA/bowtie2)
|
v
Alignment to transcriptome
|
v
Quality filtered BAM
```
## Adapter Trimming
**Goal:** Remove 3' adapter sequences from ribosome footprint reads to recover the true insert.
**Approach:** Run cutadapt with the known adapter sequence and length filters to discard fragments outside the expected footprint range.
```bash
# Trim 3' adapter
cutadapt \
-a CTGTAGGCACCATCAAT \
-m 20 \
-M 40 \
-o trimmed.fastq.gz \
input.fastq.gz
```
## Size Selection
**Goal:** Retain only reads corresponding to ribosome-protected fragments (typically 28-32 nt).
**Approach:** Apply minimum and maximum length filters with cutadapt to select the footprint size range.
```bash
# Select ribosome footprint size range
# Typical: 28-32 nt (protected by ribosome)
cutadapt \
-m 28 \
-M 32 \
-o size_selected.fastq.gz \
trimmed.fastq.gz
```
## rRNA Removal
**Goal:** Deplete ribosomal RNA reads that typically constitute the majority of a Ribo-seq library.
**Approach:** Align reads against rRNA reference databases using SortMeRNA or Bowtie2 and collect only unmapped (non-rRNA) reads.
```bash
# Option 1: SortMeRNA (comprehensive)
sortmerna \
--ref rRNA_databases/silva-bac-16s-id90.fasta \
--ref rRNA_databases/silva-euk-18s-id95.fasta \
--ref rRNA_databases/silva-euk-28s-id98.fasta \
--reads size_selected.fastq.gz \
--aligned rRNA_reads \
--other non_rRNA_reads \
--fastx \
--threads 8
# Option 2: Bowtie2 to rRNA index
bowtie2 -x rRNA_index \
-U size_selected.fastq.gz \
--un non_rRNA.fastq.gz \
-S /dev/null \
-p 8
```
## Alignment to Transcriptome
**Goal:** Map cleaned ribosome footprint reads to the genome or transcriptome for positional analysis.
**Approach:** Align with STAR (spliced) or Bowtie2 (transcriptome) using stringent filters for uniquely mapped reads with few mismatches.
```bash
# STAR alignment (spliced)
STAR --runMode alignReads \
--genomeDir STAR_index \
--readFilesIn non_rRNA.fastq.gz \
--readFilesCommand zcat \
--outFilterMultimapNmax 1 \
--outFilterMismatchNmax 2 \
--alignIntronMax 1 \
--outSAMtype BAM SortedByCoordinate \
--outFileNamePrefix riboseq_
# Or bowtie2 to transcriptome
bowtie2 -x transcriptome_index \
-U non_rRNA.fastq.gz \
-S aligned.sam \
--no-unal \
-p 8
```
## Quality Metrics
**Goal:** Assess preprocessing success by checking read length distribution and mapping rates.
**Approach:** Extract read lengths from the aligned BAM and run samtools flagstat to verify expected footprint sizes and mapping efficiency.
```bash
# Check read length distribution
samtools view aligned.bam | \
awk '{print length($10)}' | \
sort | uniq -c | sort -k2n
# Expected: Peak at 28-30 nt
# Check mapping rate
samtools flagstat aligned.bam
```
## Python Preprocessing
```python
import pysam
import numpy as np
from collections import Counter
def get_length_distribution(bam_path):
'''Get read length distribution from BAM'''
lengths = Counter()
with pysam.AlignmentFile(bam_path, 'rb') as bam:
for read in bam:
if not read.is_unmapped:
lengths[read.query_length] += 1
return lengths
def filter_by_length(bam_in, bam_out, min_len=28, max_len=32):
'''Filter BAM by read length'''
with pysam.AlignmentFile(bam_in, 'rb') as infile:
with pysam.AlignmentFile(bam_out, 'wb', template=infile) as outfile:
for read in infile:
if min_len <= read.query_length <= max_len:
outfile.write(read)
```
## Related Skills
- ribosome-periodicity - Validate preprocessing quality
- read-qc/quality-reports - General quality control
- read-alignment/star-alignment - Alignment concepts