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 21-23nt
- 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
By