bio-workflows-smrna-pipeline

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

# Adapter trimming and size selection. Small-RNA inserts (~22 nt) are shorter than the read,
# so the 3' adapter is on EVERY real read -> --discard-untrimmed drops adapter-dimer / no-insert
# junk (the inverse of genomic DNA, where no-adapter reads are the good ones). See read-qc/adapter-trimming.
cutadapt -a TGGAATTCTCGGGTGCCAAGG \
    --minimum-length 18 --maximum-length 30 --discard-untrimmed \
    -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