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
| File |
Description |
| miR.Counts.csv |
Raw read counts per miRNA |
| miR.RPM.csv |
RPM normalized counts |
| isomiR.Counts.csv |
IsomiR-level counts |
| isomiR.summary.csv |
IsomiR summary per miRNA |
| annotation.report.html |
Interactive QC report |
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
tools-only