File contents name: bio-workflows-smrna-pipeline
description: End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.
tool_type: mixed
primary_tool: miRDeep2
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
Small RNA-seq Pipeline
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 21-23ntAfter alignment : >70% mapping rate expectedAfter 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
1 --- 2 name: bio-workflows-smrna-pipeline 3 description: <!-- 4 --- 5 <!-- 6 # COPYRIGHT NOTICE 7 # This file is part of the "Universal Biomedical Skills" project. 8 # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> 9 # All Rights Reserved. 10 # 11 # This code is proprietary and confidential. 12 # Unauthorized copying of this file, via any medium is strictly prohibited. 13 # 14 # Provenance: Authenticated by MD BABU MIA 15 16 --> 17 18 --- 19 name: bio-workflows-smrna-pipeline 20 description: End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data. 21 tool_type: mixed 22 primary_tool: miRDeep2 23 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. 24 allowed-tools: 25 - read_file 26 - run_shell_command 27 --- 28 29 # Small RNA-seq Pipeline 30 31 ## Pipeline Overview 32 33 ``` 34 FASTQ → cutadapt trim → miRDeep2 → Quantification → DESeq2 → Target prediction 35 ``` 36 37 ## Step 1: Preprocessing 38 39 ```bash 40 # Adapter trimming and size selection 41 cutadapt -a TGGAATTCTCGGGTGCCAAGG \ 42 --minimum-length 18 --maximum-length 30 \ 43 -o trimmed.fastq.gz reads.fastq.gz 44 ``` 45 46 ## Step 2: miRDeep2 Analysis 47 48 ```bash 49 # Align to genome 50 mapper.pl trimmed.fastq.gz -e -h -i -j -l 18 \ 51 -m -p genome_index -s reads_collapsed.fa \ 52 -t reads_collapsed_vs_genome.arf 53 54 # miRNA quantification and novel prediction 55 miRDeep2.pl reads_collapsed.fa genome.fa \ 56 reads_collapsed_vs_genome.arf \ 57 mature_ref.fa none hairpin_ref.fa 58 ``` 59 60 ## Step 3: Differential Expression 61 62 ```r 63 library(DESeq2) 64 counts <- read.csv('mirna_counts.csv', row.names = 1) 65 dds <- DESeqDataSetFromMatrix(counts, colData, ~condition) 66 dds <- DESeq(dds) 67 results <- results(dds) 68 ``` 69 70 ## Step 4: Target Prediction 71 72 ```bash 73 # miRanda for target prediction 74 miranda mature_mirnas.fa target_3utrs.fa -out targets.txt 75 ``` 76 77 ## QC Checkpoints 78 79 1. **After trimming**: Size distribution should peak at 21-23nt 80 2. **After alignment**: >70% mapping rate expected 81 3. **After DE**: Check volcano plot and PCA 82 83 ## Related Skills 84 85 - small-rna-seq/mirdeep2-analysis - Detailed miRDeep2 86 - small-rna-seq/differential-mirna - DE analysis 87 - small-rna-seq/target-prediction - Target analysis 88 89 90 <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
commit 25422cdfa5
Frequently asked questions How do I install the Bio Workflows Smrna Pipeline skill? Run npx skillmds@latest add freedomintelligence/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.
What does the Bio Workflows Smrna Pipeline skill do? <!-- It is listed under DevOps & Infra on SkillMD.
Is Bio Workflows Smrna Pipeline safe to use? This skill has not completed SkillMD's automated safety review yet. 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? FreedomIntelligence (@freedomintelligence) published this skill. Their other Agent Skills are listed on their SkillMD profile.
FreedomIntelligence