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Agent skill

Bio Small Rna Seq Mirge3 Analysis

by GPTomics in GPTomics/bioSkills

Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries.

MITAuto-check passedResearch & Science

Install Bio Small Rna Seq Mirge3 Analysis

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirge3-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-small-rna-seq-mirge3-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/small-rna-seq/mirge3-analysis .claude/skills/bio-small-rna-seq-mirge3-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-small-rna-seq-mirge3-analysis
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.7k tokens
SKILL.md length
951 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries.

  • Choosing miRBase versus MirGeneDB as the reference
  • SKILL.md covers Version Compatibility, The governing principle:…, Decision: miRBase vs MirGeneDB… and Library installation (no…, plus 9 more sections
  • Runs Python scripts from its folder; calls wget and pip; reaches sourceforge.net
  • Deciding whether to collapse isomiRs to the parent miRNA

What it does

Bio Small Rna Seq Mirge3 Analysis is an agent skill from GPTomics/bioSkills. 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.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/mirge3_quantify.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Choosing miRBase versus MirGeneDB as the reference
  • Deciding whether to collapse isomiRs to the parent miRNA
  • Keep 5-isomiRs separate (they shift the seed and retarget)
  • Confirming the organism is among the six supported species

Example prompts

  • “Use the bio-small-rna-seq-mirge3-analysis skill to quantify known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed…”
  • “/bio-small-rna-seq-mirge3-analysis”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • wget
    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • sourceforge.net

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Small Rna Seq Mirge3 Analysis loads about 2.7k tokens when it runs. Until then it costs about 128 tokens; SKILL.md has 951 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~128
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 951 words, ~2,740 tokens.

Download SKILL.mdSave it as .claude/skills/bio-small-rna-seq-mirge3-analysis/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
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)

ReferenceSizeCharacterChoose when
miRBase (v22)large (~1900 human miRNAs)permissive; includes many dubious entries (mis-annotated tRFs/fragments)maximizing recall / comparability with legacy studies
MirGeneDBsmall (~550 human genes)conservatively curated; every entry passes the biogenesis signatureconservative, high-confidence claims; cleaner DE feature set

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)

bash
# 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.

bash
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

bash
# 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

FileDescription
miR.Counts.csvRaw read counts per miRNA (this feeds DESeq2/edgeR)
miR.RPM.csvRPM-normalized counts (display only, NOT for DE testing)
*.gff3isomiR variants in mirGFF3 (with -gff)
annotation.report.html / .csvRNA-class composition and QC report
a2i / editing reportA-to-I editing sites and frequencies (with -ai)
Show full SKILL.md (374 more words)Show less

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.

python
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).

python
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.

python
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

SymptomCauseFix
unrecognized arguments: --isomirFlag does not existisomiR counts are produced by default; use -gff for mirGFF3 output
unrecognized arguments: --download-libraryNo such subcommandDownload libraries from SourceForge and tar -xzf; point -lib at the tree
ModuleNotFoundError: mirge3.annotateNo documented Python APICall the CLI with subprocess.run([...])
Empty or tiny count matrixWrong -on, wrong -db case, or wrong adapterConfirm a supported species; -db miRBase/MirGeneDB; check the adapter name/sequence
Organism not supportedOnly six species ship librariesBuild a custom library with miRge3_build, or use miRDeep2/sRNAbench
Inflated DE significance on tiny miRNAsRPM fed to the DE testFeed RAW miR.Counts.csv, not miR.RPM.csv, to DESeq2/edgeR
  • 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

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in small-rna-seq/mirge3-analysis of GPTomics/bioSkills.

  • SKILL.md
  • examples/mirge3_quantify.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Bio Small Rna Seq Mirge3 Analysis

What does Bio Small Rna Seq Mirge3 Analysis do?

Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries. Bio Small Rna Seq Mirge3 Analysis is an agent skill from GPTomics/bioSkills.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries.

When should I use Bio Small Rna Seq Mirge3 Analysis?

Bio Small Rna Seq Mirge3 Analysis fits situations like: choosing miRBase versus MirGeneDB as the reference; deciding whether to collapse isomiRs to the parent miRNA; keep 5-isomiRs separate (they shift the seed and retarget); confirming the organism is among the six supported species.

How do I install Bio Small Rna Seq Mirge3 Analysis in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirge3-analysis -a claude-code`. Or copy the skill folder (small-rna-seq/mirge3-analysis in GPTomics/bioSkills) into .claude/skills/bio-small-rna-seq-mirge3-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Bio Small Rna Seq Mirge3 Analysis in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirge3-analysis -a codex`. Or copy the skill folder (small-rna-seq/mirge3-analysis in GPTomics/bioSkills) into .agents/skills/bio-small-rna-seq-mirge3-analysis in your project. Codex loads it when a task matches its description.

Can I use Bio Small Rna Seq Mirge3 Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirge3-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-small-rna-seq-mirge3-analysis, .gemini/skills/bio-small-rna-seq-mirge3-analysis, .github/skills/bio-small-rna-seq-mirge3-analysis and .opencode/skills/bio-small-rna-seq-mirge3-analysis in your project.

What does Bio Small Rna Seq Mirge3 Analysis need to run?

Going by SKILL.md and its folder, Bio Small Rna Seq Mirge3 Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (wget and pip). Our summary lists: Python 3.

Does Bio Small Rna Seq Mirge3 Analysis access the network?

SKILL.md names 1 domain. In commands or code: sourceforge.net; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Bio Small Rna Seq Mirge3 Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Small Rna Seq Mirge3 Analysis use?

Bio Small Rna Seq Mirge3 Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Small Rna Seq Mirge3 Analysis use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Small Rna Seq Mirge3 Analysis?

Skills that share tags, products or a category with Bio Small Rna Seq Mirge3 Analysis: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Small Rna Seq Mirge3 Analysis?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.