The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR396
  5. gli-miR396a

Accession: B33793798

Entry

ID: gli-miR396a
Alias: F5.L000065.A3D
Sequence: UUCCACGGCUUUCUUGAACUG
Description: MIR396 putative
Species: 珊瑚菜  Species latin name: Glehnia littoralis
TCM Name: Beishashen(北沙参)  
TCM Resource (Sample number): 北沙参(1)
Predicted by Mirnovo: -
Evidence support: Mirbase|infernal
Experimental status: Not yet

Homologs

miRBase Homologs

aof-miR396a; cme-miR396e; eun-miR396a-5p; mdm-miR396f; mdm-miR396g; ptc-miR396f;

Bencao Homologs

Bjl-miR396b; aan-miR396d; aar-miR396a; abs-miR396a; ala-miR396b; asc-miR396c; asi-miR396a; att-miR396a; bch-miR396a; bpi-miR396c; car-miR396c; cid-miR396b; cja-miR396c; cmi-miR396d; cmn-miR396a; cmo-miR396d; csp-miR396b; dst-miR396c; epr-miR396c; esc-miR396b; fmu-miR396a; fsu-miR396d; gbi-miR396c; gli-miR396a; gmc-miR396b; gpe-miR396d; hcr-miR396d; ija-miR396b; ili-miR396d; lln-miR396b; mca-miR396c; nnu-miR396d; pgi-miR396a; ppr-miR396b; pvl-miR396d; rcr-miR396d; sba-miR396d; sgl-miR396b; tfa-miR396a;

Rfam Alignments

>> MIR396  microRNA MIR396
 rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc
 ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----
  (1) !    0.0038   20.6   0.0  cm       11       31 ~~           1          21 + ~~ 1.00 5'&3' 0.48
                                                                   ??? ?????? ???? ???       NC
                                                           ~~~~~~--<<<-<<<<<<-<<<<-<<<~~~~~~ CS
                                                 MIR396  1 <[10]*UUCCACgGCUUUCUUGAACUg*[87]> 118
                                                                 UUCCACGGCUUUCUUGAACUG
                                            tfa-miR396a  1 <[ 0]*UUCCACGGCUUUCUUGAACUG*[ 0]> 21
                                                           ......*********************...... PP

Targets

Predicted by TargetFinder

NM_001282874 ;   NM_001282875 ;   NM_001286229 ;   NM_001286230 ;   NM_001372065 ;   NM_001372068 ;   NM_001372069 ;   NM_001372070 ;   NM_003069 ;   NM_004860 ;   NM_013302 ;   NM_015261 ;   NM_015964 ;   NM_016140 ;   NM_016448 ;  

Predicted by TAPIR

NM_001252039 ;   NM_001256750 ;   NM_001256751 ;   NM_001256752 ;   NM_001256753 ;   NM_001256754 ;   NM_001256755 ;   NM_001256756 ;   NM_001282874 ;   NM_001282875 ;   NM_001349386 ;   NM_001349387 ;   NM_001349388 ;   NM_001349389 ;   NM_001349390 ;   NM_001349391 ;   NM_001349392 ;   NM_001349393 ;   NM_001349394 ;   NM_001349395 ;   NM_001349396 ;   NM_001349397 ;   NM_001349398 ;   NM_001349399 ;   NM_001349401 ;   NM_001349404 ;   NM_001349406 ;   NM_001349408 ;   NM_001349409 ;   NM_001349410 ;   NM_003069 ;   NM_004583 ;   NM_004860 ;   NM_013302 ;   NM_015964 ;   NM_016140 ;   NM_016559 ;   NM_022739 ;   NM_201434 ;   NR_104025 ;   NR_146167 ;