How do I install the Bio Workflows Smrna Pipeline skill?

Run npx skillmds add tools-only/bio-workflows-smrna-pipeline in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Bio Workflows Smrna Pipeline safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bio Workflows Smrna Pipeline?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Bio Workflows Smrna Pipeline free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Bio Workflows Smrna Pipeline?

tools-only (@tools-only) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Workflows Smrna Pipeline by tools-only

Name: Bio Workflows Smrna Pipeline
Rating: 4.666666666666667 (3 reviews)
Author: tools-only

File contents

Version Compatibility

Reference examples tested with: DESeq2 1.42+, cutadapt 4.4+

Before using code patterns, verify installed versions match. If versions differ:

R: packageVersion('<pkg>') then ?function_name to verify parameters
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.

Small RNA-seq Pipeline

"Analyze my small RNA-seq data from FASTQ to differential miRNAs" → Orchestrate adapter trimming (cutadapt), miRNA quantification (miRDeep2/miRge3), novel miRNA discovery, differential expression (DESeq2), and target prediction (miRanda).

Pipeline Overview

FASTQ → cutadapt trim → miRDeep2 → Quantification → DESeq2 → Target prediction

Step 1: Preprocessing

# Adapter trimming and size selection
cutadapt -a TGGAATTCTCGGGTGCCAAGG \
    --minimum-length 18 --maximum-length 30 \
    -o trimmed.fastq.gz reads.fastq.gz

Step 2: miRDeep2 Analysis

# Align to genome
mapper.pl trimmed.fastq.gz -e -h -i -j -l 18 \
    -m -p genome_index -s reads_collapsed.fa \
    -t reads_collapsed_vs_genome.arf

# miRNA quantification and novel prediction
miRDeep2.pl reads_collapsed.fa genome.fa \
    reads_collapsed_vs_genome.arf \
    mature_ref.fa none hairpin_ref.fa

Step 3: Differential Expression

library(DESeq2)
counts <- read.csv('mirna_counts.csv', row.names = 1)
dds <- DESeqDataSetFromMatrix(counts, colData, ~condition)
dds <- DESeq(dds)
results <- results(dds)

Step 4: Target Prediction

# miRanda for target prediction
miranda mature_mirnas.fa target_3utrs.fa -out targets.txt

QC Checkpoints

After trimming: Size distribution should peak at 21-23nt
After alignment: >70% mapping rate expected
After DE: Check volcano plot and PCA

Related Skills

small-rna-seq/mirdeep2-analysis - Detailed miRDeep2
small-rna-seq/differential-mirna - DE analysis
small-rna-seq/target-prediction - Target analysis

---name: bio-workflows-smrna-pipelinedescription: End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.---## Version CompatibilityReference examples tested with: DESeq2 1.42+, cutadapt 4.4+Before using code patterns, verify installed versions match. If versions differ:- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters- CLI: `<tool> --version` then `<tool> --help` to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installedpackage and adapt the example to match the actual API rather than retrying.# Small RNA-seq Pipeline**"Analyze my small RNA-seq data from FASTQ to differential miRNAs"** → Orchestrate adapter trimming (cutadapt), miRNA quantification (miRDeep2/miRge3), novel miRNA discovery, differential expression (DESeq2), and target prediction (miRanda).## Pipeline Overview```FASTQ → cutadapt trim → miRDeep2 → Quantification → DESeq2 → Target prediction```## Step 1: Preprocessing```bash# Adapter trimming and size selectioncutadapt -a TGGAATTCTCGGGTGCCAAGG \ --minimum-length 18 --maximum-length 30 \ -o trimmed.fastq.gz reads.fastq.gz```## Step 2: miRDeep2 Analysis```bash# Align to genomemapper.pl trimmed.fastq.gz -e -h -i -j -l 18 \ -m -p genome_index -s reads_collapsed.fa \ -t reads_collapsed_vs_genome.arf# miRNA quantification and novel predictionmiRDeep2.pl reads_collapsed.fa genome.fa \ reads_collapsed_vs_genome.arf \ mature_ref.fa none hairpin_ref.fa```## Step 3: Differential Expression```rlibrary(DESeq2)counts <- read.csv('mirna_counts.csv', row.names = 1)dds <- DESeqDataSetFromMatrix(counts, colData, ~condition)dds <- DESeq(dds)results <- results(dds)```## Step 4: Target Prediction```bash# miRanda for target predictionmiranda mature_mirnas.fa target_3utrs.fa -out targets.txt```## QC Checkpoints1. **After trimming**: Size distribution should peak at 21-23nt2. **After alignment**: >70% mapping rate expected3. **After DE**: Check volcano plot and PCA## Related Skills- small-rna-seq/mirdeep2-analysis - Detailed miRDeep2- small-rna-seq/differential-mirna - DE analysis- small-rna-seq/target-prediction - Target analysis

Frequently asked questions