small-rna-seq-mirdeep2-analysis

name	small-rna-seq-mirdeep2-analysis
description	Discover novel miRNAs and quantify known miRNAs using miRDeep2 de novo prediction from small RNA-seq data. Use when identifying new miRNAs or performing comprehensive miRNA profiling with discovery.
tool_type	cli
primary_tool	miRDeep2

Version Compatibility

Reference examples tested with: pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• 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.

miRDeep2 Analysis

"Discover novel miRNAs from my small RNA-seq data" -> Identify known and novel miRNAs by mapping reads to the genome and scoring precursor hairpin structures using a probabilistic model.

• CLI: mapper.pl for read mapping, miRDeep2.pl for de novo miRNA prediction

Workflow Overview

Collapsed reads (FASTA)
    |
    v
mapper.pl ---------> Align to genome, create ARF file
    |
    v
miRDeep2.pl -------> Predict novel miRNAs, quantify known
    |
    v
quantifier.pl -----> Quantify known miRNAs only (optional)

Step 1: Prepare Genome Index

Goal: Build a bowtie index from the reference genome for miRDeep2 read mapping.

Approach: Run bowtie-build on the genome FASTA to create the index files required by mapper.pl.

# Build bowtie index for miRDeep2 mapper
bowtie-build genome.fa genome_index

Step 2: Map Reads with mapper.pl

Goal: Collapse identical reads and align them to the reference genome.

Approach: Use mapper.pl to clip adapters, filter by length, collapse duplicates, and map with bowtie to produce ARF alignment files.

# Collapse reads and map to genome
mapper.pl reads.fastq \
    -e \
    -h \
    -i \
    -j \
    -k TGGAATTCTCGGGTGCCAAGG \
    -l 18 \
    -m \
    -p genome_index \
    -s reads_collapsed.fa \
    -t reads_vs_genome.arf \
    -v

# Key options:
# -e: Input is FASTQ
# -h: Parse Illumina headers
# -k: Clip 3' adapter
# -l 18: Discard reads < 18 nt
# -m: Collapse reads
# -p: Bowtie index prefix
# -s: Output collapsed FASTA
# -t: Output ARF alignment file

Step 3: Run miRDeep2 Prediction

Goal: Predict novel miRNAs and quantify known miRNAs from aligned small RNA reads.

Approach: Run miRDeep2.pl with collapsed reads, genome, alignments, and miRBase references to score candidate miRNA loci.

# Predict novel miRNAs
miRDeep2.pl \
    reads_collapsed.fa \
    genome.fa \
    reads_vs_genome.arf \
    mature_ref.fa \
    mature_other.fa \
    hairpin_ref.fa \
    -t Human \
    2> report.log

# Arguments:
# 1. Collapsed reads FASTA
# 2. Genome FASTA
# 3. Alignment ARF file
# 4. Known mature miRNAs (same species)
# 5. Known mature miRNAs (other species, for conservation)
# 6. Known hairpin precursors
# -t: Species for miRBase lookup

Prepare miRBase References

Goal: Download and extract species-specific miRNA references from miRBase.

Approach: Fetch mature and hairpin FASTA files from miRBase, then grep species-specific entries by prefix.

# Download from miRBase
wget https://www.mirbase.org/download/mature.fa
wget https://www.mirbase.org/download/hairpin.fa

# Extract species-specific sequences
grep -A1 ">hsa-" mature.fa > mature_human.fa
grep -A1 ">hsa-" hairpin.fa > hairpin_human.fa

Step 4: Quantify Known miRNAs Only

Goal: Quantify expression of known miRNAs without running novel discovery.

Approach: Run quantifier.pl with hairpin and mature references against collapsed reads for fast quantification.

# If not doing novel discovery
quantifier.pl \
    -p hairpin_human.fa \
    -m mature_human.fa \
    -r reads_collapsed.fa \
    -t hsa

# Output: miRNAs_expressed_all_samples.csv

Output Files

File	Description
result_*.html	Interactive results report
result_*.csv	Predicted novel miRNAs with scores
miRNAs_expressed_all_samples*.csv	Expression quantification
pdfs_*.pdf	Secondary structure plots

Interpret miRDeep2 Scores

Score interpretation:
>10: High confidence novel miRNA
5-10: Medium confidence
1-5: Low confidence, needs validation
<1: Likely false positive

Key metrics:
- miRDeep2 score: Overall confidence
- Total read count: Expression level
- Mature/star ratio: Strand bias (expect asymmetry)
- Randfold p-value: Structural stability

Parse Results in Python

Goal: Load miRDeep2 prediction and quantification results into pandas DataFrames.

Approach: Parse tab-delimited output files and filter novel miRNA predictions by confidence score threshold.

import pandas as pd

def parse_mirdeep2_results(csv_path):
    '''Parse miRDeep2 novel miRNA predictions'''
    df = pd.read_csv(csv_path, sep='\t', skiprows=1)

    # Filter high-confidence predictions
    # Score > 10 indicates high confidence novel miRNA
    high_conf = df[df['miRDeep2 score'] > 10]

    return high_conf

# Parse quantification results
def parse_quantifier_output(csv_path):
    '''Parse quantifier.pl expression matrix'''
    df = pd.read_csv(csv_path, sep='\t')
    return df

Related Skills

• smrna-preprocessing - Prepare reads for miRDeep2
• mirge3-analysis - Faster quantification alternative
• differential-mirna - DE analysis of miRNA counts