miRNA Target Prediction - Usage Guide

Overview

Predict and prioritize miRNA target genes using seed-based tools (miRanda, TargetScan, miRDB) and experimentally validated databases (miRTarBase, multiMiR). The governing reality is that a predicted target is a hypothesis, not a finding: seed prediction is roughly 50% false-positive even for conserved sites, a 6mer seed occurs by chance about once per 4 kb, and a single miRNA represses most real targets only modestly. Confidence comes from climbing an evidence ladder - conservation, validated CLIP/reporter data, and above all intersecting predictions with inversely-correlated mRNA differential expression from the same samples - not from stacking more seed-based predictors, which is pseudo-replication.

Prerequisites

conda install -c bioconda miranda
pip install pandas biopython gseapy
# multiMiR (unifies predicted + validated) is an R/Bioconductor package

Quick Start

Tell your AI agent: - "Predict targets for my DE miRNAs, then keep only the functional ones" - "Rank TargetScan targets by weighted context++ score" - "Get experimentally validated targets from miRTarBase, weighted by evidence type" - "Intersect predicted targets with my anti-correlated mRNA-seq" - "Why shouldn't I just run GO enrichment on the predicted target list?"

Example Prompts

Prediction and Ranking

"Run miRanda with strict seed pairing for hsa-miR-21-5p against my 3' UTRs"

"Rank this miRNA's TargetScan targets by weighted context++ score"

"Get miRDB targets with score >= 80"

Evidence and Validation

"Pull validated targets from miRTarBase and separate strong from less-strong evidence"

"Use multiMiR to combine predicted and validated targets in one query"

"Which of my predicted targets are supported by AGO-CLIP?"

The Confidence Move

"Intersect predicted targets of my UP miRNAs with DOWN mRNAs from matched RNA-seq"

"My target enrichment says 'cancer pathways' for every miRNA - how do I fix the circularity?"

What the Agent Will Do

Generate candidate targets by seed complementarity and thermodynamics (miRanda) or database lookup (TargetScan/miRDB)
Rank by the appropriate score (weighted context++, mirSVR, miRDB score)
Anchor in experimentally validated interactions (miRTarBase strong, CLIP/CLASH)
Intersect with inversely-correlated mRNA/protein DE from matched samples - the key confidence filter
Only then run functional enrichment, on an evidence-filtered list, to avoid circular inflation

Tips

A predicted target is a hypothesis for the wet lab, not a result - seed prediction is ~50% false-positive even for conserved sites
Five seed-based tools agreeing is pseudo-replication; require an orthogonal evidence tier (CLIP, validated, expression) instead
The single most useful move is intersecting predictions with anti-correlated mRNA DE from the same samples
Rank TargetScan by weighted context++ score (more negative = stronger); it is relative within a miRNA, not an absolute cutoff
The miRNA column in the TargetScan context-scores file is Mirbase ID, not miRNA family
miRTarBase separates strong (reporter/western/qPCR) from less-strong (CLIP/NGS) evidence - weight accordingly
Enrichment on an unfiltered predicted target list is circular and inflates big pathways; filter by evidence first
miRNAs also repress translationally, so some real targets do not drop at the mRNA level (ribosome profiling/proteomics catches those)
Seed-only prediction also misses the bulged/seedless/3'-compensatory sites (a large fraction of real CLASH interactions are noncanonical, Helwak 2013), so a clean seed list is incomplete, not just imprecise
AGO-CLIP evidence is cell-type/state-specific; a peak from another tissue is weak support for yours
Treat ceRNA/sponge mechanisms skeptically - stoichiometry is usually too low to derepress targets

differential-mirna - Source of DE miRNAs
pathway-analysis/go-enrichment - Enrich an evidence-filtered target list
database-access/entrez-fetch - Fetch UTR/gene sequences
clip-seq/ago-clip-mirna-targets - Direct AGO-CLIP/CLASH target evidence