The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR396
  5. sba-miR396g

Accession: B59412477

Entry

ID: sba-miR396g
Alias: F826.L000039.A3D*
Sequence: UUCCACAGCUUUCUUGAACUA
Description: MIR396 putative
Species: 半枝莲  Species latin name: Scutellaria barbata
TCM Name: Banzhilian(半枝莲)  
TCM Resource (Sample number): 半枝莲(1)
Predicted by Mirnovo: -
Evidence support: Mirbase
Experimental status: Not yet

Homologs

miRBase Homologs

pab-miR396a-5p; vvi-miR396a;

Bencao Homologs

Bsr-miR396f; apl-miR396e; ave-miR396e; bmj-miR396f; car-miR396f; cco-miR396e; ccs-miR396d; cmd-miR396f; cmo-miR396h; cof-miR396g; dst-miR396g; eja-miR396d; epr-miR396h; est-miR396f; gbi-miR396i; gmc-miR396f; gpe-miR396h; gur-miR396c; hcr-miR396g; hmu-miR396d; lfo-miR396i; lji-miR396c; ljn-miR396e; llc-miR396d; mal-miR396e; mca-miR396f; nca-miR396c; nnu-miR396j; nte-miR396e; pav-miR396g; pcl-miR396d; pfr-miR396g; por-miR396j; pvl-miR396h; rcr-miR396f; rja-miR396e; sba-miR396g; sbc-miR396e; sgl-miR396e; smi-miR396g; ssu-miR396e; vof-miR396d;

Rfam Alignments

>> MIR396  microRNA MIR396
 rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc
 ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----
  (1) ?     0.011   19.0   0.0  cm       11       31 ~~           1          21 + ~~ 1.00 5'&3' 0.38
                                                                   ??? ?????? ???? ???       NC
                                                           ~~~~~~--<<<-<<<<<<-<<<<-<<<~~~~~~ CS
                                                 MIR396  1 <[10]*UUCCACgGCUUUCUUGAACUg*[87]> 118
                                                                 UUCCAC GCUUUCUUGAACU
                                            vof-miR396d  1 <[ 0]*UUCCACAGCUUUCUUGAACUA*[ 0]> 21
                                                           ......*********************...... PP

Targets

Predicted by TargetFinder

NM_000970 ;   NM_001024662 ;   NM_001135669 ;   NM_001270 ;   NM_001271562 ;   NM_001281717 ;   NM_001286229 ;   NM_001286230 ;   NM_001308022 ;   NM_001308023 ;   NM_001320137 ;   NM_001320138 ;   NM_001320139 ;   NM_001320140 ;   NM_001320141 ;   NM_001320142 ;   NM_001347701 ;   NM_001347702 ;   NM_001364113 ;   NM_001364567 ;   NM_001364568 ;   NM_001364569 ;   NM_001364570 ;   NM_001364571 ;   NM_001364572 ;   NM_001364573 ;   NM_001370465 ;   NM_001515 ;   NM_004274 ;   NM_004736 ;   NM_005502 ;   NM_016052 ;   NM_016448 ;   NM_022648 ;   NM_022739 ;   NM_033071 ;   NM_182961 ;   NM_198976 ;   NR_033417 ;   NR_040013 ;   NR_137330 ;   NR_157078 ;   NR_157079 ;  

Predicted by TAPIR

NM_001001556 ;   NM_001076 ;   NM_001142929 ;   NM_001142933 ;   NM_001142934 ;   NM_001271562 ;   NM_001281717 ;   NM_001286230 ;   NM_001289030 ;   NM_001301210 ;   NM_001308022 ;   NM_001308023 ;   NM_001347701 ;   NM_001347702 ;   NM_001370465 ;   NM_002044 ;   NM_005502 ;   NM_012444 ;   NM_015114 ;   NM_016052 ;   NM_016132 ;   NM_016448 ;   NM_020549 ;   NM_020984 ;   NM_020985 ;   NM_020986 ;   NM_022648 ;   NM_022739 ;   NM_033071 ;   NM_182961 ;   NM_197954 ;   NM_198265 ;   NM_198976 ;   NR_040013 ;   NR_147895 ;