ReddRadar
Agent skill
bio-workflows-riboseq-pipeline
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-riboseq-pipeline
SKILL.md
name: bio-workflows-riboseq-pipeline description: End-to-end Ribo-seq analysis from FASTQ to translation efficiency and ORF detection. Use when analyzing ribosome profiling data to study translation. tool_type: mixed primary_tool: Plastid measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Ribo-seq Pipeline
Pipeline Overview
FASTQ → Preprocessing → rRNA removal → Alignment → P-site → TE → ORF calling
Step 1: Preprocessing
# 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
# 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
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
# 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
# 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
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