Bio Small Rna Seq Mirge3 Analysis

name: bio-small-rna-seq-mirge3-analysis description: Fast miRNA quantification with isomiR detection and A-to-I editing analysis using miRge3. Use when quantifying known miRNAs quickly or analyzing isomiR variants and RNA editing. tool_type: python primary_tool: miRge3 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

miRge3 Analysis

Basic Quantification

# Run miRge3 on FASTQ files
miRge3.0 annotate \
    -s sample1.fastq.gz,sample2.fastq.gz \
    -lib miRge3_libs \
    -on human \
    -db mirbase \
    -o output_dir \
    -a TGGAATTCTCGGGTGCCAAGG \
    --threads 8

# Key options:
# -s: Input FASTQ files (comma-separated)
# -lib: Path to miRge3 library
# -on: Organism name
# -db: Database (mirbase or mirgenedb)
# -a: 3' adapter sequence

Install miRge3 Libraries

# Download pre-built libraries
miRge3.0 --download-library human mirbase

# Libraries include:
# - Bowtie indices for miRNAs, tRNAs, rRNAs
# - miRBase or MirGeneDB annotations
# - A-to-I editing sites

IsomiR Detection

# Enable isomiR analysis
miRge3.0 annotate \
    -s sample.fastq.gz \
    -lib miRge3_libs \
    -on human \
    -db mirbase \
    --isomir \
    -o output_dir

# IsomiRs include:
# - 5' variants (templated and non-templated)
# - 3' variants (templated and non-templated)
# - Internal modifications

A-to-I RNA Editing

# Detect A-to-I editing
miRge3.0 annotate \
    -s sample.fastq.gz \
    -lib miRge3_libs \
    -on human \
    -db mirbase \
    --AtoI \
    -o output_dir

# Outputs editing sites and frequencies

Output Files

Python API

from mirge3.annotate import annotate

# Run programmatically
annotate(
    samples=['sample1.fastq.gz', 'sample2.fastq.gz'],
    lib_path='miRge3_libs',
    organism='human',
    database='mirbase',
    adapter='TGGAATTCTCGGGTGCCAAGG',
    output_dir='results',
    threads=8
)

Parse miRge3 Output

import pandas as pd

def load_mirge3_counts(output_dir):
    '''Load miRge3 count matrix'''
    counts = pd.read_csv(f'{output_dir}/miR.Counts.csv', index_col=0)
    return counts

def load_isomirs(output_dir):
    '''Load isomiR-level counts'''
    isomirs = pd.read_csv(f'{output_dir}/isomiR.Counts.csv', index_col=0)
    return isomirs

# Filter low-expressed miRNAs
def filter_low_counts(counts, min_total=10):
    '''Keep miRNAs with total count >= threshold'''
    return counts[counts.sum(axis=1) >= min_total]

Compare Multiple Samples

def normalize_rpm(counts):
    '''Normalize to reads per million'''
    total_per_sample = counts.sum(axis=0)
    rpm = counts / total_per_sample * 1e6
    return rpm

def log_transform(rpm, pseudocount=1):
    '''Log2 transform with pseudocount'''
    import numpy as np
    return np.log2(rpm + pseudocount)

IsomiR Analysis

def summarize_isomirs(isomir_counts):
    '''Summarize isomiR diversity per miRNA'''
    # Group by canonical miRNA
    isomir_counts['miRNA'] = isomir_counts.index.str.extract(r'(hsa-\w+-\d+[a-z]*)')[0]

    summary = isomir_counts.groupby('miRNA').agg({
        'count': ['sum', 'count', lambda x: x.idxmax()]
    })
    summary.columns = ['total_reads', 'n_isomirs', 'dominant_isomir']
    return summary

Related Skills

• smrna-preprocessing - Prepare reads for miRge3
• mirdeep2-analysis - Alternative with novel miRNA discovery
• differential-mirna - DE analysis of miRge3 counts