bio-workflows-smrna-pipeline

$npx mdskill add GPTomics/bioSkills/bio-workflows-smrna-pipeline

Analyzes small RNA-seq data from FASTQ files to identify differential miRNA expression

  • Processes miRNA, piRNA, and other small RNA sequencing data for differential expression analysis
  • Uses cutadapt for trimming, miRDeep2 for quantification, and DESeq2 for differential analysis
  • Automates adapter trimming, genome alignment, quantification, and target prediction workflows
  • Delivers results through structured output files and integrates with downstream analysis tools
SKILL.md
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---
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
---

## 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

```bash
# 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

```bash
# 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

```r
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

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

## QC Checkpoints

1. **After trimming**: Size distribution should peak at 21-23nt
2. **After alignment**: >70% mapping rate expected
3. **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
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