The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR167
  5. nnu-miR167g

Accession: B48231316

Entry

ID: nnu-miR167g
Alias: F123.K001499.A25
Sequence: UGAAGCUGCCAGCAUGAUCG
Description: microRNA MIR167_1 predicted
Species: 莲  Species latin name: Nelumbo nucifera
TCM Name: Lianzi(莲子)   Lianzixin(莲子心)   Lianfang(莲房)   Liaxu(莲须)   Heye(荷叶)   Oujie(藕节)  
TCM Resource (Sample number): 莲须(1) 荷叶(1) 莲(1)
Predicted by Mirnovo: -
Evidence support: Infernal
Experimental status: Not yet

Homologs

miRBase Homologs

None

Bencao Homologs

apl-miR167g; dst-miR167i; est-miR167e; gbi-miR167e; gma-miR167d; lpu-miR167f; mca-miR167g; nnu-miR167g; pfr-miR167e; por-miR167g; vum-miR167g;

Rfam Alignments

>> MIR167_1  microRNA MIR167_1
 rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc
 ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----
  (1) !    0.0086   17.6   0.0  cm       14       33 ~~           1          20 + ~~ 0.99 5'&3' 0.55
                                                                 ??? ?????  ?????????       NC
                                                           ~~~~~~<<<-<<<<<--<<<<<<<<<~~~~~~ CS
                                               MIR167_1  1 <[13]*UGAAGCUGCCAGCAUGAuCU*[97]> 130
                                                                 UGAAGCUGCCAGCAUGAUC
                                            vum-miR167g  1 <[ 0]*UGAAGCUGCCAGCAUGAUCG*[ 0]> 20
                                                           ......******************99...... PP

Targets

Predicted by TargetFinder

NM_000071 ;   NM_001005751 ;   NM_001024843 ;   NM_001159770 ;   NM_001162501 ;   NM_001178008 ;   NM_001178009 ;   NM_001261834 ;   NM_001278595 ;   NM_001286779 ;   NM_001291398 ;   NM_001297549 ;   NM_001307960 ;   NM_001308026 ;   NM_001318040 ;   NM_001318041 ;   NM_001318042 ;   NM_001320298 ;   NM_001321072 ;   NM_001321073 ;   NM_001321981 ;   NM_001330102 ;   NM_001330504 ;   NM_001352691 ;   NM_001352692 ;   NM_001352693 ;   NM_001354006 ;   NM_001354007 ;   NM_001354008 ;   NM_001354009 ;   NM_001354010 ;   NM_001354012 ;   NM_001354014 ;   NM_001354015 ;   NM_001370528 ;   NM_002911 ;   NM_004749 ;   NM_005916 ;   NM_006392 ;   NM_014801 ;   NM_015088 ;   NM_019109 ;   NM_020880 ;   NM_025141 ;   NM_032293 ;   NM_078474 ;   NM_080622 ;   NM_133374 ;   NM_139177 ;   NM_172364 ;   NM_182776 ;   NM_198859 ;   NR_024248 ;   NR_026552 ;   NR_027700 ;   NR_104591 ;   NR_120673 ;   NR_121624 ;   NR_134278 ;   NR_145428 ;   NR_148682 ;  

Predicted by TAPIR

NM_001024843 ;   NM_001159770 ;   NM_001162501 ;   NM_001197244 ;   NM_001242412 ;   NM_001261834 ;   NM_001278595 ;   NM_001297549 ;   NM_001301061 ;   NM_001307960 ;   NM_001308026 ;   NM_001321072 ;   NM_001321073 ;   NM_001330332 ;   NM_001330504 ;   NM_001346743 ;   NM_001346744 ;   NM_001352691 ;   NM_001352692 ;   NM_001352693 ;   NM_001354006 ;   NM_001354007 ;   NM_001354008 ;   NM_001354009 ;   NM_001354010 ;   NM_001354012 ;   NM_001354014 ;   NM_001354015 ;   NM_001364170 ;   NM_001370528 ;   NM_001707 ;   NM_002911 ;   NM_004749 ;   NM_005916 ;   NM_015088 ;   NM_018897 ;   NM_019109 ;   NM_020731 ;   NM_025141 ;   NM_078474 ;   NM_080622 ;   NM_139177 ;   NM_172364 ;   NM_175709 ;   NM_182776 ;   NM_198859 ;   NR_024248 ;   NR_026964 ;   NR_036682 ;   NR_120673 ;   NR_121624 ;   NR_148682 ;