Agent skill

bio-workflows-riboseq-pipeline

End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/riboseq-pipeline

SKILL.md

Ribo-seq Pipeline

Pipeline Overview

FASTQ → Preprocessing → rRNA removal → Alignment → P-site → TE → ORF calling

Step 1: Preprocessing

bash
# Remove adapters
cutadapt -a CTGTAGGCACCATCAAT \
    --minimum-length 25 --maximum-length 35 \
    -o trimmed.fastq.gz reads.fastq.gz

# Remove rRNA
bowtie2 -x rRNA_index --un non_rrna.fastq.gz -U trimmed.fastq.gz

Step 2: Alignment

bash
# Align to transcriptome
STAR --genomeDir star_index \
    --readFilesIn non_rrna.fastq.gz \
    --readFilesCommand zcat \
    --outFilterMismatchNmax 2 \
    --alignEndsType EndToEnd \
    --outSAMtype BAM SortedByCoordinate

Step 3: P-site Calibration

python
from plastid import BAMGenomeArray

# Build metagene profile
metagene_generate annotation.gtf ribo.bam metagene_output/

# Calculate P-site offsets
psite annotation.gtf metagene_output/profile.txt psite_offsets.txt

Step 4: Translation Efficiency

python
# TE = Ribo-seq RPKM / RNA-seq RPKM
from plastid import BAMGenomeArray
import numpy as np

ribo_counts = count_reads(ribo_bam, genes)
rna_counts = count_reads(rna_bam, genes)
te = ribo_counts / rna_counts

Step 5: ORF Detection

bash
# RiboCode for ORF calling
RiboCode -a annotation.gtf -c config.txt -o ribocoded_orfs

Related Skills

  • ribo-seq/ - Individual Ribo-seq analysis skills
  • differential-expression - For differential TE

Didn't find tool you were looking for?

Be as detailed as possible for better results