version: "1.0.1" name: bio-small-rna-seq-mirge3-analysis description: Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries. Use when choosing miRBase versus MirGeneDB as the reference; deciding whether to collapse isomiRs to the parent miRNA or keep 5'-isomiRs separate (they shift the seed and retarget); confirming the organism is among the six supported species; or remembering that RPM output is for display only and raw counts go to DESeq2/edgeR. tool_type: python primary_tool: miRge3

Version Compatibility

Reference examples tested with: miRge3.0 0.1.4+, numpy 1.26+, pandas 2.2+

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

• CLI: miRge3.0 annotate --help to confirm flag names (they have drifted across versions)
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

miRge3 Analysis

"Quantify my miRNAs and isomiRs fast" -> Align collapsed reads to a hierarchy of curated small-RNA libraries and tabulate per-miRNA counts, isomiR variants, tRFs, and A-to-I editing.

• CLI: miRge3.0 annotate -s sample.fastq.gz -lib LIBS -on human -db miRBase -a illumina -gff -ai -cpu 8 -o out/

The governing principle: miRge3 quantifies what is already known, fast, and isomiRs are biology

miRge3.0 does not do genome-wide de novo discovery as its main job; it Bowtie-aligns collapsed reads against small curated libraries (mature miRBase or MirGeneDB, hairpin, tRNA, rRNA, snoRNA, mRNA, spike-ins) hierarchically and assigns each read to the first matching class. That is why it is fast, and why it is the default for a routine differential-expression study on a supported species - and why it cannot help on an unsupported organism (it ships pre-built libraries for only six species: human, mouse, rat, zebrafish, nematode, fruitfly). For serious NOVEL discovery prefer miRDeep2; miRge3's optional -nmir SVM module is a convenience, not its strength.

Two judgments carry the analysis. First, isomiRs are real biology, not noise: a 5' isomiR shifts the seed (positions 2-7) and therefore the target set, so collapsing all isomiRs to the canonical miRNA can hide function - keep 5' isomiRs separate when isomiR identity is the question, and collapse to the parent only for a standard "which miRNAs changed" analysis. But the precision floor cuts the other way: low-count 3' and internal isomiRs are frequently sequencing/ligation artifacts (per-base error ~0.1-1% plus ligation bias), so filter them aggressively and demand replicate or UMI support, and trust 5' isomiRs more. A germline seed SNP (a polymiR) masquerades as an isomiR or edit; with genotypes available, fold them into the reference (e.g. OptimiR) rather than calling them isomiRs. Second, miRge3 emits both raw counts and RPM, but RPM is for display and cross-sample viewing only; differential testing takes RAW counts into DESeq2/edgeR, which model the count distribution themselves.

Decision: miRBase vs MirGeneDB reference (-db)

The reference choice changes results: counting against miRBase yields more "miRNA" rows, some of which are not bona fide miRNAs; against MirGeneDB the rows are fewer and defensible. miRge3 can emit both side by side - report which one a result came from, and pin the version.

Library installation (no built-in download command)

# miRge3.0 has NO '--download-library' subcommand. Fetch the pre-built libraries from
# SourceForge and extract them, then point -lib at the extracted directory.
wget https://sourceforge.net/projects/mirge3/files/miRge3_Lib/human.tar.gz
tar -xzf human.tar.gz          # creates a 'human' library tree
# For an unsupported organism, build a custom library with the separate miRge3_build tool.

Quantify known miRNAs (+ isomiRs, A-to-I)

Goal: Produce a per-miRNA count matrix with isomiR and editing detail for one or more samples.

Approach: Run miRge3.0 annotate with the curated library, organism, database, and adapter, switching on mirGFF3 isomiR output and A-to-I detection.

miRge3.0 annotate \
    -s sample1.fastq.gz,sample2.fastq.gz \
    -lib /path/to/miRge3_Lib \
    -on human \
    -db miRBase \
    -a illumina \
    -gff \
    -ai \
    -cpu 8 \
    -o output_dir
 
# -s: comma-separated FASTQs (raw or already adapter-known)
# -on: organism (human|mouse|rat|zebrafish|nematode|fruitfly)
# -db: miRBase or MirGeneDB
# -a: adapter as a name ('illumina') OR a raw sequence (e.g. TGGAATTCTCGGGTGCCAAGG)
# -gff: emit isomiR results in mirGFF3 (the community-standard isomiR format)
# -ai: A-to-I editing. A seed A->I edit RETARGETS the miRNA (inosine reads as G), and
#      mismatch-permissive alignment silently merges edited reads into the canonical
#      count - keep -ai on and treat seed edits as distinct species, not noise.
# -cpu: threads

UMI and novel-miRNA options

# QIAseq UMI library: -qumi removes Qiagen PCR duplicates; -umi gives the 5',3' trim lengths
miRge3.0 annotate -s qiaseq.fastq.gz -lib LIBS -on human -db miRBase \
    -a AACTGTAGGCACCATCAAT -umi 0,12 -qumi -o out_umi
 
# Optional novel-miRNA prediction (SVM); needs the genome; prefer miRDeep2 for real discovery
miRge3.0 annotate -s sample.fastq.gz -lib LIBS -on human -db miRBase -a illumina -nmir -o out_novel

Output files

Run from Python via subprocess

Goal: Orchestrate miRge3 from a Python pipeline and load its outputs.

Approach: miRge3.0 is a command-line tool with no documented Python API, so invoke it with subprocess, then read the CSV outputs with pandas.

import subprocess
def run_mirge3(samples, lib_path, out_dir, organism='human', db='miRBase', adapter='illumina', threads=8):
    cmd = ['miRge3.0', 'annotate',
           '-s', ','.join(samples),
           '-lib', lib_path,
           '-on', organism,
           '-db', db,
           '-a', adapter,
           '-gff', '-ai',
           '-cpu', str(threads),
           '-o', out_dir]
    subprocess.run(cmd, check=True)

Load and filter counts

Goal: Read the miRge3 count matrix and remove near-zero noise before downstream analysis.

Approach: Load miR.Counts.csv, then filter to miRNAs with a minimum total count (most miRBase entries are near-zero noise).

import pandas as pd
def load_mirge3_counts(output_dir):
    return pd.read_csv(f'{output_dir}/miR.Counts.csv', index_col=0)
def filter_low_counts(counts, min_total=10):
    # Lower than an mRNA threshold because miRNA libraries have fewer total counts;
    # hand the SURVIVING RAW counts (not RPM) to DESeq2/edgeR for testing.
    return counts[counts.sum(axis=1) >= min_total]

Aggregate isomiRs deliberately

Goal: Decide whether to collapse isomiRs to the parent miRNA or keep seed-shifting 5' variants separate.

Approach: Parse the mirGFF3 isomiR table, classify each variant by 5' vs 3' change, and aggregate to the parent only for variants that preserve the seed.

def summarize_isomirs(isomir_counts):
    # 5' isomiRs shift the seed and retarget -> keep separate when isomiR identity is
    # the biology; 3' isomiRs mostly tune stability -> safe to collapse to the parent.
    # KEEP the -5p/-3p arm in the parent key: the two arms have different seeds and
    # targets and must never be merged (the dominant arm also switches across tissues).
    # .values assigns positionally - index.str.extract returns a fresh RangeIndex that
    # would otherwise misalign to all-NaN against the string index.
    isomir_counts['miRNA'] = isomir_counts.index.str.extract(r'(hsa-\w+-\d+[a-z]*(?:-[35]p)?)')[0].values
    summary = isomir_counts.groupby('miRNA').agg(
        total_reads=('count', 'sum'),
        n_isomirs=('count', 'count'),
        dominant_isomir=('count', lambda x: x.idxmax()))
    return summary

Common Errors

Related Skills

• smrna-preprocessing - Adapter and UMI handling; miRge3 can also trim internally
• mirdeep2-analysis - Use when de novo novel-miRNA discovery is the goal
• differential-mirna - Differential expression from the raw count matrix
• trf-pirna-profiling - Deeper tRF/piRNA analysis beyond miRge3's tRF module

References

• Patil AH, Halushka MK. 2021. miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline. NAR Genom Bioinform 3:lqab068. doi:10.1093/nargab/lqab068
• Desvignes T, Loher P, Eilbeck K, et al. 2020. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API. Bioinformatics 36:698-703. doi:10.1093/bioinformatics/btz675
• Kozomara A, Birgaoanu M, Griffiths-Jones S. 2019. miRBase: from microRNA sequences to function. Nucleic Acids Res 47:D155-D162. doi:10.1093/nar/gky1141
• Fromm B, Domanska D, Høye E, et al. 2020. MirGeneDB 2.0: the metazoan microRNA complement. Nucleic Acids Res 48:D1172-D1180. doi:10.1093/nar/gkz885
• Tan GC, Chan E, Molnar A, et al. 2014. 5' isomiR variation is of functional and evolutionary importance. Nucleic Acids Res 42:9424-9435. doi:10.1093/nar/gku656