The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR30
  5. sor-miR30ad

Accession: B63029936

Entry

ID: sor-miR30ad
Alias: F3769.I000002.A11
Sequence: UGUAAACAUCCUCGACUG
Description: mir-30 microRNA precursor predicted,partially
Species: 豨莶  Species latin name: Sigesbeckia orientalis
TCM Name: Xixiancao(豨莶草)  
TCM Resource (Sample number): 豨莶草(1)
Predicted by Mirnovo: -
Evidence support: Infernal
Experimental status: Not yet

Homologs

miRBase Homologs

None

Bencao Homologs

Bjl-miR30aa; cmd-miR30aa; sor-miR30ad;

Rfam Alignments

>> mir-30  mir-30 microRNA precursor
 rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc
 ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----
  (1) !    0.0039   20.8   0.0  cm        1       18 [~           1          18 + [~ 1.00    3' 0.44
              ??????????  ?????       NC
             :<<<<<<<<<<--<<<<<~~~~~~ CS
   mir-30  1 UGuAAACAUCcUcGACuG*[42]> 60
             UGUAAACAUCCUCGACUG
sor-miR30ad  1 UGUAAACAUCCUCGACUG*[ 0]> 18
             ******************...... PP

Targets

Predicted by TargetFinder

NM_000433 ;   NM_001004309 ;   NM_001004346 ;   NM_001014809 ;   NM_001099294 ;   NM_001127651 ;   NM_001144978 ;   NM_001190789 ;   NM_001190794 ;   NM_001271815 ;   NM_001282426 ;   NM_001282427 ;   NM_001288661 ;   NM_001288662 ;   NM_001288973 ;   NM_001312673 ;   NM_001313 ;   NM_001317770 ;   NM_001322253 ;   NM_001322256 ;   NM_001322257 ;   NM_001330004 ;   NM_001330731 ;   NM_001351314 ;   NM_001351329 ;   NM_001351610 ;   NM_001351611 ;   NM_001351612 ;   NM_001351613 ;   NM_001351614 ;   NM_001351615 ;   NM_001351616 ;   NM_001351617 ;   NM_001351618 ;   NM_001351619 ;   NM_001351620 ;   NM_001351632 ;   NM_001352680 ;   NM_001352681 ;   NM_001353433 ;   NM_001369201 ;   NM_002649 ;   NM_003474 ;   NM_005017 ;   NM_006805 ;   NM_007052 ;   NM_007195 ;   NM_013955 ;   NM_015106 ;   NM_015425 ;   NM_016248 ;   NM_017592 ;   NM_018704 ;   NM_024764 ;   NM_032048 ;   NM_032346 ;   NM_144775 ;   NM_152834 ;   NM_178505 ;   NM_178821 ;   NR_002819 ;   NR_003024 ;   NR_003254 ;   NR_003291 ;   NR_034130 ;   NR_038854 ;   NR_103553 ;   NR_103554 ;   NR_110241 ;   NR_125825 ;   NR_133915 ;   NR_134507 ;   NR_138459 ;   NR_144567 ;   NR_144568 ;   NR_147097 ;  

Predicted by TAPIR

NM_001080496 ;   NM_001145901 ;   NM_001286247 ;   NM_001317770 ;   NM_001318374 ;   NM_001322253 ;   NM_001322256 ;   NM_001322257 ;   NM_001330731 ;   NM_001352158 ;   NM_006805 ;   NM_015106 ;   NM_015425 ;   NM_017592 ;   NM_019067 ;   NM_024764 ;   NM_032832 ;   NM_144775 ;   NR_003024 ;   NR_029846 ;   NR_125825 ;   NR_133915 ;   NR_134507 ;   NR_135557 ;