>Mup13|NM_001134674|3'UTR
   1 GAAGTCCAATTCCAGTCTATCCACATGTTACCTAGGATACCTCATCAAGAATCAAAGACTTCTTTAAATTTCTCTTTGAT
  81 ATACCCATGACAATTTTTCATGAATTTCTTCCTCTTCCTGTTCAATAAATGATTACCCTTGCACTTAAAT

ID	Duplex structure	Position	Score	MFE
1	miRNA 3' aacuauccuccACGAGUUAUUu 5' ||:||||||| Target 5' cttcctcttccTGTTCAATAAa 3'	108 - 129	151.00	-9.00
2	miRNA 3' aacuAUCC--UCCACGAGUUAUUu 5' |||| | | |||| || Target 5' taccTAGGATACCTCATCAAGAAt 3'	29 - 52	116.00	-6.80

- BMC genomics, 2013

BACKGROUND: Validation of physiologic miRNA targets has been met with significant challenges. We employed HITS-CLIP to identify which miRNAs participate in liver regeneration, and to identify their target mRNAs. RESULTS: miRNA recruitment to the RISC is highly dynamic, changing more than five-fold for several miRNAs. miRNA recruitment to the RISC did not correlate with changes in overall miRNA expression for these dynamically recruited miRNAs, emphasizing the necessity to determine miRNA recruitment to the RISC in order to fully assess the impact of miRNA regulation. We incorporated RNA-seq quantification of total mRNA to identify expression-weighted Ago footprints, and developed a microRNA regulatory element (MRE) prediction algorithm that represents a greater than 20-fold refinement over computational methods alone. These high confidence MREs were used to generate candidate 'competing endogenous RNA' (ceRNA) networks. CONCLUSION: HITS-CLIP analysis provide novel insights into global miRNA:mRNA relationships in the regenerating liver.