bio-small-rna-seq-target-prediction

name	bio-small-rna-seq-target-prediction
description	Predicts and prioritizes miRNA target genes with seed-based tools (miRanda, TargetScan, miRDB) and experimentally validated databases (miRTarBase, multiMiR). Use when deciding that a predicted target is a hypothesis not a finding; ranking by the right score (weighted context++, mirSVR, miRDB); raising confidence by intersecting predictions with inversely-correlated mRNA DE; weighing validated (CLIP/reporter) over predicted evidence; or avoiding the circular enrichment of unfiltered target lists.
tool_type	mixed
primary_tool	miRanda

Version Compatibility

Reference examples tested with: miRanda 3.3a+, BioPython 1.83+, pandas 2.2+, gseapy 1.1+

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.

miRNA Target Prediction

"Predict target genes for my miRNAs" -> Generate candidate mRNA targets by seed complementarity and thermodynamics, then raise confidence with conservation, validated databases, and matched expression.

• CLI: miranda miRNA.fa UTR.fa -sc 140 -en -20 -strict for de novo prediction
• Python: query TargetScan/miRDB downloads and miRTarBase for validated interactions

The governing principle: a predicted target is a hypothesis, not a finding

Seed-based prediction has a false-positive rate near 50% even for conserved sites (Pinzon 2017), because a 6mer seed match occurs by chance roughly once per 4 kb of sequence, so a multi-kb 3' UTR carries many spurious matches. It also has a RECALL problem in the opposite direction: AGO-CLIP and CLASH show that roughly 60% of real interactions are noncanonical, and the bulged, seedless, and 3'-compensatory sites among them are missed entirely by seed-only tools (3'-supplementary sites keep a canonical seed and are still found), so a clean seed list is incomplete, not just imprecise (Helwak 2013). Worse, a single miRNA represses most of its real targets only modestly - typically less than two-fold at the protein level (Baek 2008; Selbach 2008) - so miRNAs are rheostats, not switches, and large single-target claims should be distrusted. The decisive move is therefore not running more predictors (five seed-based tools agreeing is pseudo-replication, not independent evidence) but climbing an evidence ladder and, above all, intersecting predictions with INVERSELY-correlated differentially expressed mRNA or protein from the SAME samples. Prediction proposes; expression disposes.

Evidence tier (low to high)	What it means
seed match alone	weakest; common by chance
+ conservation (TargetScan PCT)	precision up, recall down (misses species-specific targets)
+ multiple independent tools / ML (miRDB)	modestly higher precision
+ AGO-CLIP footprint	the miRNA's complex bound there (but CLIP is cell-type/state-specific - a peak from another tissue is weak evidence)
+ CLASH/CLEAR-CLIP chimera	direct miRNA-target duplex
+ anti-correlated matched miRNA/mRNA(protein) DE	functional in YOUR system
+ reporter / seed-mutation rescue (miRTarBase "strong")	causal, gold standard

Decision: which predictor, and how it scores

Tool	Scoring philosophy	Use for	Caveat
TargetScan (context++)	conservation + 14-feature regression of repression	conserved-site prioritization	v7 = context++; v8 = a different Kd/biochemical model
miRanda + mirSVR	thermodynamic alignment + expression-trained regression	non-conserved / non-canonical sites	permissive; tune thresholds
miRDB / MirTarget	ML (SVM) on CLIP + overexpression data	data-driven ranking (score 0-100)	score >= 80 is the conventional high-confidence cut
RNAhybrid	pure MFE hybridization, no seed constraint	exploratory, no-seed sites	most false positives without filters
miRTarBase / TarBase (ENCORI)	experimentally validated interactions	the gold tier; anchor claims here	"less strong" CLIP/NGS entries are not individually validated
multiMiR	unifies predicted + validated sources	one-call aggregation	inherits each source DB's errors

De novo prediction with miRanda

Goal: Predict miRNA-mRNA target sites by complementarity and duplex energy.

Approach: Align miRNA sequences against 3' UTRs with a minimum score and a maximum (negative) energy, requiring strict seed pairing.

miranda miRNA.fa UTRs.fa -sc 140 -en -20 -strict -out predictions.txt

# -sc 140: minimum alignment score (keep alignments with score >= 140; default 140)
# -en -20: maximum free energy in kcal/mol (keep energies <= -20; value is negative)
# -strict: require canonical seed pairing at positions 2-8 (no gaps/wobble in seed)
# Tunable: many use -sc 150 / -en -7 (looser) up to -sc 155 / -en -20 (stringent)

Parse miRanda output

Goal: Extract interaction records into a DataFrame.

Approach: Read the lines miRanda prefixes with '>' (per-hit summary) and pull miRNA, target, score, and energy.

import pandas as pd

def parse_miranda(output_file):
    rows = []
    with open(output_file) as f:
        for line in f:
            if line.startswith('>') and not line.startswith('>>'):
                p = line.strip().split('\t')
                if len(p) >= 5:
                    rows.append({'mirna': p[0].lstrip('>'), 'target': p[1],
                                 'score': float(p[2]), 'energy': float(p[3])})
    return pd.DataFrame(rows)

TargetScan context-scores lookup

Goal: Retrieve conserved-site predictions and rank a miRNA's targets.

Approach: Read the downloadable per-site context-scores file and rank by the weighted context++ score (more negative = stronger predicted repression across the whole UTR).

import pandas as pd

def query_targetscan(mirbase_id, ts_file='Predicted_Targets_Context_Scores.default_predictions.txt'):
    # Verified column names: the miRNA column is 'Mirbase ID' (NOT 'miRNA family'),
    # the gene column is 'Gene ID', and 'weighted context++ score' aggregates a UTR's sites.
    df = pd.read_csv(ts_file, sep='\t')
    hits = df[df['Mirbase ID'] == mirbase_id]
    return hits.sort_values('weighted context++ score')   # ascending: most negative first

miRDB (machine-learning) lookup

Goal: Retrieve ML-based target predictions above the conventional confidence cut.

Approach: Read the miRDB prediction download and keep targets with score >= 80.

def query_mirdb(mirna_id, mirdb_file='miRDB_v6.0_prediction_result.txt'):
    df = pd.read_csv(mirdb_file, sep='\t', header=None, names=['mirna', 'refseq', 'score'])
    hits = df[df['mirna'] == mirna_id]
    return hits[hits['score'] >= 80].sort_values('score', ascending=False)

Validated targets and unified lookup

Goal: Anchor target claims in experimental evidence rather than prediction.

Approach: Query miRTarBase for validated interactions and weight by evidence type; use multiMiR (R) to unify predicted and validated sources in one call.

def get_validated_targets(mirna, mirtarbase_file='miRTarBase_MTI.xlsx'):
    df = pd.read_excel(mirtarbase_file)
    hits = df[df['miRNA'] == mirna]
    # 'Support Type' separates strong (reporter/western/qPCR) from less-strong (CLIP/NGS)
    return hits[['Target Gene', 'Experiments', 'Support Type']]

The confidence move: intersect with anti-correlated mRNA DE

Goal: Keep only targets that behave functionally in the actual experiment.

Approach: Intersect predicted (or CLIP-supported) targets of UP miRNAs with DOWN mRNAs from matched samples; note the blind spot that translation-only targets may not move at the mRNA level.

def functional_targets(predicted_targets, mrna_de, mirna_direction):
    # mrna_de: DataFrame with index = gene, column 'log2FC' from matched mRNA-seq.
    # Anti-correlation: an UP miRNA should repress -> targets DOWN (and vice versa).
    # Blind spot: miRNAs also act translationally, so some real targets stay flat at
    # the mRNA level (need ribosome profiling / proteomics to see those).
    want_down = mirna_direction == 'up'
    moved = mrna_de[(mrna_de['log2FC'] < 0) == want_down].index
    return [g for g in predicted_targets if g in set(moved)]

Seed match analysis and site types

Goal: Locate seed matches in a UTR and classify site strength.

Approach: The seed is miRNA positions 2-7; a site is the reverse complement of the seed in the 3' UTR. Canonical sites by decreasing efficacy: 8mer > 7mer-m8 > 7mer-A1 > 6mer.

from Bio.Seq import Seq

def find_seed_matches(mirna_seq, utr_seq):
    # 7mer-m8 site = reverse complement of miRNA positions 2-8 found in the UTR
    seed = str(Seq(mirna_seq)[1:8])
    site = str(Seq(seed).reverse_complement())
    matches, start = [], 0
    while True:
        pos = utr_seq.find(site, start)
        if pos == -1:
            break
        matches.append(pos)
        start = pos + 1
    return matches

Common Errors

Symptom	Cause	Fix
KeyError: 'miRNA family' on TargetScan file	Wrong column name for the context-scores file	The miRNA column is Mirbase ID; rank by weighted context++ score
Hundreds of "targets", almost none real	Treating seed prediction as truth	Intersect with anti-correlated mRNA DE; anchor in miRTarBase strong evidence
Every miRNA "regulates cancer pathways"	Enrichment on an unfiltered predicted target list (circular)	Build the list from validated/CLIP or expression-filtered targets before enrichment
Five tools "agree" so a target is trusted	All five use the seed (pseudo-replication)	Require an orthogonal evidence tier (CLIP/validated/expression), not more seed tools
A strong single-target claim	miRNAs repress most targets < 2-fold	Treat large single-target effects skeptically; demand validation
ceRNA/sponge mechanism asserted	Stoichiometry usually too low to matter (Denzler)	Require absolute abundance (miRNA copies vs added sites) before accepting it

Related Skills

• differential-mirna - Source of the DE miRNAs to predict targets for
• pathway-analysis/go-enrichment - Enrich a target list (only after evidence-filtering)
• database-access/entrez-fetch - Fetch UTR/gene sequences and identifiers
• clip-seq/ago-clip-mirna-targets - AGO-CLIP / CLASH direct target evidence

References

• Agarwal V, Bell GW, Nam JW, Bartel DP. 2015. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4:e05005. doi:10.7554/eLife.05005
• Betel D, Koppal A, Agius P, Sander C, Leslie C. 2010. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 11:R90. doi:10.1186/gb-2010-11-8-r90
• Chen Y, Wang X. 2020. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res 48:D127-D131. doi:10.1093/nar/gkz757
• Huang HY, Lin YC, Cui S, et al. 2022. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 50:D222-D230. doi:10.1093/nar/gkab1079
• Ru Y, Kechris KJ, Tabakoff B, et al. 2014. The multiMiR R package and database: integration of microRNA-target interactions. Nucleic Acids Res 42:e133. doi:10.1093/nar/gku631
• Pinzón N, Li B, Martinez L, et al. 2017. microRNA target prediction programs predict many false positives. Genome Res 27:234-245. doi:10.1101/gr.205146.116
• Baek D, Villén J, Shin C, et al. 2008. The impact of microRNAs on protein output. Nature 455:64-71. doi:10.1038/nature07242
• Selbach M, Schwanhäusser B, Thierfelder N, et al. 2008. Widespread changes in protein synthesis induced by microRNAs. Nature 455:58-63. doi:10.1038/nature07228
• Helwak A, Kudla G, Dudnakova T, Tollervey D. 2013. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153:654-665. doi:10.1016/j.cell.2013.03.043
• Denzler R, Agarwal V, Stefano J, Bartel DP, Stoffel M. 2014. Assessing the ceRNA hypothesis with quantitative measurements of miRNA and target abundance. Mol Cell 54:766-776. doi:10.1016/j.molcel.2014.03.045