bio-workflows-smrna-pipeline

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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

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

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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