agentsclimarketplace
Sign in

Riboseq pipeline

Skill BioTender-max/awesome-bio-agent-skills/skills/bioskills/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.From its SKILL.md

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill riboseq-pipeline

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

SKILL.md

2.4 KB, 590 tokens by cl100k_base, as published. Nobody here has run it

Version Compatibility

Reference examples tested with: Bowtie2 2.5.3+, STAR 2.7.11+, cutadapt 4.4+, numpy 1.26+

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 Pipeline

"Analyze my ribosome profiling data from FASTQ to translation efficiency" → Orchestrate adapter trimming, rRNA depletion, genome alignment, periodicity QC, ORF detection (RiboCode), stalling analysis, and translation efficiency estimation (riborex).

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

What ships with it: 2 files

4.0 KB alongside SKILL.md, 1 of them executable

examples/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.