Bio small rna seq mirdeep2 analysis skills mirdeep2 analysis

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)

# miRDeep2 uses bowtie 1, NOT bowtie2
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

# -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

# 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

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)

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

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):
    # 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

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