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Agent skill

Bio Small Rna Seq Mirdeep2 Analysis

by GPTomics in GPTomics/bioSkills

Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature.

MITAuto-check passedResearch & Science

Install Bio Small Rna Seq Mirdeep2 Analysis

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirdeep2-analysis -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-small-rna-seq-mirdeep2-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/mirdeep2-analysis .claude/skills/bio-small-rna-seq-mirdeep2-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-mirdeep2-analysis
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.8k tokens
SKILL.md length
998 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature.

  • Works in 5 steps: Build the genome index (bowtie 1) → Map reads with mapper.pl → Prepare miRBase references → …
  • Deciding whether a study needs de novo discovery at all versus known-miRNA quantification
  • SKILL.md covers Version Compatibility, The governing principle: a…, Decision: is miRDeep2 the… and Workflow overview, plus 11 more sections
  • Runs Shell scripts from its folder; calls wget and pip; reaches mirbase.org

What it does

Bio Small Rna Seq Mirdeep2 Analysis is an agent skill from GPTomics/bioSkills. Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature. Use when deciding whether a study needs de novo discovery at all versus known-miRNA quantification; choosing the species and related-species miRBase references; reading the miRDeep2 score as a signal-to-noise hypothesis rather than a fixed cutoff; or filtering novel candidates against tRNA/rRNA loci to reject the classic false positives.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/mirdeep2_workflow.sh` 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

  • Deciding whether a study needs de novo discovery at all versus known-miRNA quantification
  • Choosing the species and related-species miRBase references
  • Reading the miRDeep2 score as a signal-to-noise hypothesis rather than a fixed cutoff
  • Filtering novel candidates against tRNA/rRNA loci to reject the classic false positives

Example prompts

  • “Use the bio-small-rna-seq-mirdeep2-analysis skill to discover novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read…”
  • “/bio-small-rna-seq-mirdeep2-analysis”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Build the genome index (bowtie 1)
  2. Map reads with mapper.pl
  3. Prepare miRBase references
  4. Run discovery with miRDeep2.pl
  5. Known-miRNA quantification only (skip discovery)

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 (Shell), 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:

    • mirbase.org

    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 Mirdeep2 Analysis loads about 2.8k tokens when it runs. Until then it costs about 131 tokens; SKILL.md has 998 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~131
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

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). 998 words, ~2,768 tokens.

Download SKILL.mdSave it as .claude/skills/bio-small-rna-seq-mirdeep2-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-mirdeep2-analysis
description
Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature. Use when deciding whether a study needs de novo discovery at all versus known-miRNA quantification; choosing the species and related-species miRBase references; reading the miRDeep2 score as a signal-to-noise hypothesis rather than a fixed cutoff; or filtering novel candidates against tRNA/rRNA loci to reject the classic false positives.
tool_type
cli
primary_tool
miRDeep2

Version Compatibility

Reference examples tested with: miRDeep2 2.0.1.3+, bowtie 1.3+ (NOT bowtie2), ViennaRNA 2.5+, pandas 2.2+

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

  • CLI: <tool> --version then <tool> --help to confirm flags
  • 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.

miRDeep2 Analysis

"Discover novel miRNAs from my small RNA-seq data" -> Map collapsed reads to the genome, excise candidate hairpins, fold them, and score how well the observed read stacks match the Dicer/Drosha processing signature.

  • CLI: mapper.pl (map to genome, emit ARF) -> miRDeep2.pl (discover + quantify) -> quantifier.pl (known-only quantification)

The governing principle: a miRDeep2 score is a biogenesis hypothesis, not a validated miRNA

miRDeep2 does not detect miRNAs by sequence; it asks whether the reads piled on a genomic hairpin look like the product of Dicer/Drosha processing: a sharp, abundant MATURE arm, a lower-abundance STAR (passenger) arm with the correct ~2-nt 3' overhang geometry, a depleted loop, and a thermodynamically stable fold whose minimum free energy is lower than shuffled controls (the randfold p-value). A log-odds model converts that fit into a score (Friedländer 2012). The decisive consequence is that any locus producing a stacked, hairpin-foldable read pile can mimic the signature, so novel discovery is intrinsically high false-positive. The textbook failure is contaminating tRNA and rRNA fragments: tRNAs fold into stable cloverleaf arms and throw sharp, abundant read stacks that score as "novel miRNAs." A high score is a structural and expression hypothesis that demands orthogonal validation, never a finding.

There is no universal score cutoff. survey.pl sweeps cutoffs and reports, at each, the estimated true positives, false positives, signal-to-noise ratio, and an estimated FDR derived from permuted controls; Friedländer 2012 chose, per analysis, the lowest cutoff giving signal-to-noise >= 5. Asserting "score > 10 = high confidence" as a fixed rule is folklore: read the survey output, pick a cutoff for an acceptable estimated FDR, and report it.

Decision: is miRDeep2 the right tool?

GoalUseWhy
Discover NOVEL miRNAs in an animal genomemiRDeep2 (full discovery)The dedicated probabilistic biogenesis model; genome-anchored
Quantify KNOWN miRNAs + isomiRs + tRFs on a supported speciesmirge3-analysisFaster, isomiR-aware; discovery machinery is expensive and high-FP
Quantify KNOWN miRNAs only, no discoveryquantifier.pl (miRDeep2) or mirge3Skip the discovery engine when discovery is not needed
Profile tRFs / piRNAs (not miRNAs)trf-pirna-profilingtRF/rRF stacks are miRDeep2 false positives, not the target
Plant small RNAsShortStack (see trf-pirna-profiling)Plant hairpins and 24-nt siRNA biology break the animal model
Animal with NO genome assembly (non-model, single-cell)Mirnovo (genome-free ML)miRDeep2 is genome-anchored and cannot run without an assembly

miRDeep2 requires a reference GENOME and bowtie 1 (not bowtie2). The species and related-species miRBase references are load-bearing: the same-species mature/hairpin define "known," and the other-species mature provides conservation evidence that raises confidence in novel calls.

Workflow overview

collapsed reads (FASTA, _xN counts)
    |
    v   mapper.pl  --> bowtie align to genome, emit ARF
    v
miRDeep2.pl  --> excise hairpins, fold (RNAfold), randfold, score read stacks
    |
    v   quantifier.pl  --> known-miRNA counts (run alone if no discovery needed)

Step 1: Build the genome index (bowtie 1)

bash
# miRDeep2 uses bowtie 1, NOT bowtie2
bowtie-build genome.fa genome_index

Step 2: Map reads with mapper.pl

bash
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

# -e: input is FASTQ   -h: parse to FASTA   -i: convert RNA to DNA
# -j: remove reads with non-ACGTN   -k: clip 3' adapter   -l 18: discard < 18 nt
# -m: collapse identical reads   -p: bowtie index   -s/-t: collapsed FASTA + ARF

Step 3: Prepare miRBase references

bash
# miRBase distributes RNA (U) sequences; miRDeep2 needs DNA and no whitespace.
# Pin the miRBase version - accessions and sequences change between releases.
wget https://www.mirbase.org/download/mature.fa
wget https://www.mirbase.org/download/hairpin.fa

# Same-species mature + hairpin (here human, hsa) and a related species for conservation
grep -A1 '>hsa-' mature.fa | grep -v '^--$' > mature_hsa.fa
grep -A1 '>hsa-' hairpin.fa | grep -v '^--$' > hairpin_hsa.fa
grep -A1 '>mmu-' mature.fa | grep -v '^--$' > mature_mmu.fa
# Convert U->T and strip spaces if the tool's extract_miRNAs.pl is not used:
# sed '/^>/!s/U/T/g; /^>/!s/u/t/g' in.fa

Step 4: Run discovery with miRDeep2.pl

bash
miRDeep2.pl \
    reads_collapsed.fa \
    genome.fa \
    reads_vs_genome.arf \
    mature_hsa.fa \
    mature_mmu.fa \
    hairpin_hsa.fa \
    -t Human \
    2> report.log

# Positional args (ORDER is fixed): collapsed reads, genome, ARF,
#   same-species mature, other-species mature (or 'none'), same-species hairpin
# -t: species for miRBase labelling

Step 5: Known-miRNA quantification only (skip discovery)

bash
quantifier.pl \
    -p hairpin_hsa.fa \
    -m mature_hsa.fa \
    -r reads_collapsed.fa \
    -t hsa
# Output: miRNAs_expressed_all_samples_*.csv
# Note: quantifier.pl and miRDeep2.pl counts can differ (different mapping logic)

Output files

FileDescription
result_*.csvRanked candidates: miRDeep2 score, randfold p, mature/star, miRBase match, estimated probability TP
result_*.htmlInteractive report with read-stack and structure plots
miRNAs_expressed_all_samples_*.csvKnown-miRNA expression matrix
mirdeep_runs/, expression_analyses/, pdfs_*/Intermediate read-stack alignments (.mrd) and structures

Reading and filtering results

python
import pandas as pd

def parse_mirdeep2_results(csv_path, score_cutoff):
    # score_cutoff is NOT universal: choose it from survey.pl signal-to-noise / FDR,
    # then report the value. There is no fixed 'score > 10' rule.
    df = pd.read_csv(csv_path, sep='\t', skiprows=1)
    return df[df['miRDeep2 score'] >= score_cutoff]

def reject_structured_rna_false_positives(candidates, trna_rrna_bed):
    # The classic miRDeep2 false positive is a tRNA/rRNA fragment hairpin.
    # Require: (a) no overlap with tRNA/rRNA/snoRNA loci, (b) some star-arm read
    # support, (c) reproducibility across replicates, before trusting a novel call.
    return candidates  # intersect coordinates against trna_rrna_bed with bedtools upstream
Show full SKILL.md (441 more words)Show less

Calling a novel miRNA real: the community criteria

A miRDeep2 score is a prefilter, not a verdict. A genuine novel miRNA must satisfy the community annotation criteria (Ambros 2003; MirGeneDB), and the deliverable should be a per-candidate criteria table, not a score-ranked list:

  • CONSISTENT 5' processing of BOTH the mature and star arms across reads - this 5'-end homogeneity is the single most discriminating signal (a precise 5' end is what defines the seed; degradation gives smeared ends).
  • A mature/star duplex with the ~2-nt 3' overhang geometry of Dicer cleavage.
  • Star-arm read support (real miRNAs usually show some passenger reads).
  • A ~22-nt mature length and a hairpin without large internal loops/bulges.
  • Conservation or Dicer/Drosha-dependence (loss of signal on knockdown), and reproducibility across replicates.

Common Errors

SymptomCauseFix
"novel miRNAs" cluster at tRNA/rRNA lociStructured-RNA fragments fold into scoring hairpinsIntersect candidates against GtRNAdb/rRNA annotations and discard overlaps
mapper.pl fails or maps almost nothingbowtie2 index supplied, or genome not indexed with bowtie 1Rebuild with bowtie-build (bowtie 1); confirm reads were adapter-trimmed
miRDeep2.pl errors on the reference FASTAmiRBase U-containing or whitespace-laden sequencesConvert U->T and strip header whitespace, or use the bundled extraction script
Treating score > 10 as truthNo universal cutoff existsUse survey.pl signal-to-noise/FDR to set and report a cutoff
Very few known miRNAs detectedWrong species -t, or reads not collapsed (_xN)Set the correct species code; collapse reads in mapper.pl (-m)
Novel call has no star-arm readsReal miRNAs usually show some passenger readsDown-weight single-arm candidates; require duplex evidence
  • smrna-preprocessing - Adapter trimming and read collapsing before mapping
  • mirge3-analysis - Faster known-miRNA + isomiR quantification when discovery is not needed
  • differential-mirna - Differential expression of the resulting count matrix
  • trf-pirna-profiling - For tRF/piRNA biology, which would otherwise appear as miRDeep2 false positives
  • genome-annotation/ncrna-annotation - Annotating tRNA/rRNA/snoRNA loci to filter false positives

References

  • Friedländer MR, Mackowiak SD, Li N, Chen W, Rajewsky N. 2012. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40:37-52. doi:10.1093/nar/gkr688
  • Friedländer MR, Chen W, Adamidi C, et al. 2008. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26:407-415. doi:10.1038/nbt1394
  • Bonnet E, Wuyts J, Rouzé P, Van de Peer Y. 2004. Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 20:2911-2917. doi:10.1093/bioinformatics/bth374
  • 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
  • Ambros V, Bartel B, Bartel DP, et al. 2003. A uniform system for microRNA annotation. RNA 9:277-279. doi:10.1261/rna.2183803

© 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/mirdeep2-analysis of GPTomics/bioSkills.

  • SKILL.md
  • examples/mirdeep2_workflow.sh
  • 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 Mirdeep2 Analysis

What does Bio Small Rna Seq Mirdeep2 Analysis do?

Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature. Bio Small Rna Seq Mirdeep2 Analysis is an agent skill from GPTomics/bioSkills. Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature.

When should I use Bio Small Rna Seq Mirdeep2 Analysis?

Bio Small Rna Seq Mirdeep2 Analysis fits situations like: deciding whether a study needs de novo discovery at all versus known-miRNA quantification; choosing the species and related-species miRBase references; reading the miRDeep2 score as a signal-to-noise hypothesis rather than a fixed cutoff; filtering novel candidates against tRNA/rRNA loci to reject the classic false positives.

How do I install Bio Small Rna Seq Mirdeep2 Analysis in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirdeep2-analysis -a claude-code`. Or copy the skill folder (small-rna-seq/mirdeep2-analysis in GPTomics/bioSkills) into .claude/skills/bio-small-rna-seq-mirdeep2-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Bio Small Rna Seq Mirdeep2 Analysis in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-small-rna-seq-mirdeep2-analysis -a codex`. Or copy the skill folder (small-rna-seq/mirdeep2-analysis in GPTomics/bioSkills) into .agents/skills/bio-small-rna-seq-mirdeep2-analysis in your project. Codex loads it when a task matches its description.

Can I use Bio Small Rna Seq Mirdeep2 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-mirdeep2-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-mirdeep2-analysis, .gemini/skills/bio-small-rna-seq-mirdeep2-analysis, .github/skills/bio-small-rna-seq-mirdeep2-analysis and .opencode/skills/bio-small-rna-seq-mirdeep2-analysis in your project.

What does Bio Small Rna Seq Mirdeep2 Analysis need to run?

Going by SKILL.md and its folder, Bio Small Rna Seq Mirdeep2 Analysis needs a shell for the scripts in its folder and the command-line tools its instructions call (wget and pip). Our summary lists: Python 3; A Bash shell.

Does Bio Small Rna Seq Mirdeep2 Analysis access the network?

SKILL.md names 1 domain. In commands or code: mirbase.org; 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 Mirdeep2 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 Mirdeep2 Analysis use?

Bio Small Rna Seq Mirdeep2 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 Mirdeep2 Analysis use?

About 2.8k 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 Mirdeep2 Analysis?

Skills that share tags, products or a category with Bio Small Rna Seq Mirdeep2 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 Mirdeep2 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.