| 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. |
| origin | openai4s |
| category | bioskills/small-rna-seq |
| metadata | {"tool_type":"cli","primary_tool":"miRDeep2","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}} |
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?
| Goal | Use | Why |
|---|
| Discover NOVEL miRNAs in an animal genome | miRDeep2 (full discovery) | The dedicated probabilistic biogenesis model; genome-anchored |
| Quantify KNOWN miRNAs + isomiRs + tRFs on a supported species | mirge3-analysis | Faster, isomiR-aware; discovery machinery is expensive and high-FP |
| Quantify KNOWN miRNAs only, no discovery | quantifier.pl (miRDeep2) or mirge3 | Skip the discovery engine when discovery is not needed |
| Profile tRFs / piRNAs (not miRNAs) | trf-pirna-profiling | tRF/rRF stacks are miRDeep2 false positives, not the target |
| Plant small RNAs | ShortStack (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)
bowtie-build genome.fa genome_index
Step 2: Map reads with mapper.pl
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
Step 3: Prepare miRBase references
wget https://www.mirbase.org/download/mature.fa
wget https://www.mirbase.org/download/hairpin.fa
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
Step 4: Run discovery with miRDeep2.pl
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
Step 5: Known-miRNA quantification only (skip discovery)
quantifier.pl \
-p hairpin_hsa.fa \
-m mature_hsa.fa \
-r reads_collapsed.fa \
-t hsa
Output files
| File | Description |
|---|
| result_*.csv | Ranked candidates: miRDeep2 score, randfold p, mature/star, miRBase match, estimated probability TP |
| result_*.html | Interactive report with read-stack and structure plots |
| miRNAs_expressed_all_samples_*.csv | Known-miRNA expression matrix |
| mirdeep_runs/, expression_analyses/, pdfs_*/ | Intermediate read-stack alignments (.mrd) and structures |
Reading and filtering results
import pandas as pd
def parse_mirdeep2_results(csv_path, score_cutoff):
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):
return candidates
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
| Symptom | Cause | Fix |
|---|
| "novel miRNAs" cluster at tRNA/rRNA loci | Structured-RNA fragments fold into scoring hairpins | Intersect candidates against GtRNAdb/rRNA annotations and discard overlaps |
| mapper.pl fails or maps almost nothing | bowtie2 index supplied, or genome not indexed with bowtie 1 | Rebuild with bowtie-build (bowtie 1); confirm reads were adapter-trimmed |
| miRDeep2.pl errors on the reference FASTA | miRBase U-containing or whitespace-laden sequences | Convert U->T and strip header whitespace, or use the bundled extraction script |
| Treating score > 10 as truth | No universal cutoff exists | Use survey.pl signal-to-noise/FDR to set and report a cutoff |
| Very few known miRNAs detected | Wrong species -t, or reads not collapsed (_xN) | Set the correct species code; collapse reads in mapper.pl (-m) |
| Novel call has no star-arm reads | Real miRNAs usually show some passenger reads | Down-weight single-arm candidates; require duplex evidence |
Related Skills
- 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
