MicroRNAEngine: Seed Sequence Matching, Anti-Correlation Network, and miRNA Regulatory Hub Analysis
Introduction
MicroRNAs regulate approximately 60% of human protein-coding genes through sequence-specific binding to 3'UTR seed sequences (positions 2-8). The seed match type (6mer, 7mer-A1, 7mer-m8, 8mer) determines binding affinity and repression efficacy.
Methods
Seed Matching
6mer (positions 2-7): 1 pt; 7mer (positions 2-8 or 2-7+A1): 2 pts; 8mer (positions 2-8+A1): 3 pts.
Anti-correlation Analysis
Pearson correlations between miRNA and target mRNA expression. Significant: r<-0.3, BH FDR<0.05.
Regulatory Network
Edges defined by seed match score ≥2 + significant anti-correlation.
Results
69 DE miRNAs. 4320 anti-correlated pairs. Seed matches: 6mer=24,766, 7mer=15,031, 8mer=10,142. Max hub degree=181.
Code Availability
https://github.com/BioTender-max/MicroRNAEngine
Key Results
- 100 samples × 500 miRNAs + 5000 targets
- DE miRNAs: 69
- Anti-correlated pairs: 4,320
- 6mer=24,766, 7mer=15,031, 8mer=10,142
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: micrornaengine description: MicroRNAs (miRNAs) are ~22 nt small non-coding RNAs that post-transcriptionally regulate gene expression by binding t... allowed-tools: Bash(python *) --- # Steps to reproduce 1. git clone https://github.com/BioTender-max/MicroRNAEngine 2. pip install numpy scipy matplotlib 3. python MicroRNAEngine.py 4. Output: MicroRNAEngine_dashboard.png — 9-panel dark-theme dashboard
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