The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR167
  5. Bjl-miR167e

Accession: B71796872

Entry

ID: Bjl-miR167e
Alias: F123.J000729.A25
Sequence: UGAAGCUGCCAGCAUGAUC
Description: microRNA MIR167_1 predicted
Species: 焦榔  Species latin name: -
TCM Name:
TCM Resource (Sample number): 焦榔(1)
Predicted by Mirnovo: -
Evidence support: Infernal
Experimental status: Not yet

Homologs

miRBase Homologs

None

Bencao Homologs

Bjl-miR167e; aha-miR167c; ala-miR167; apl-miR167e; aqi-miR167; ath-miR167a; bca-miR167b; cas-miR167b; cco-miR167f; cit-miR167c; cmd-miR167d; csu-miR167a; dst-miR167g; efo-miR167b; ehu-miR167e; eja-miR167c; epr-miR167c; est-miR167c; gbi-miR167c; gja-miR167c; gma-miR167b; gmc-miR167; gsi-miR167c; hmu-miR167c; iba-miR167b; ini-miR167a; ljp-miR167a; llc-miR167b; llu-miR167; lpu-miR167d; mca-miR167e; mch-miR167b; nnu-miR167e; nte-miR167b; pas-miR167a; pav-miR167b; pcl-miR167b; pcn-miR167d; pfr-miR167c; pmu-miR167b; poe-miR167e; por-miR167e; pvl-miR167e; rch-miR167c; sch-miR167d; sdi-miR167c; smi-miR167e; sto-miR167e; tci-miR167d; vof-miR167b; vum-miR167e; zmu-miR167b;

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) !    0.0006   21.4   0.0  cm       14       32 ~~           1          19 + ~~ 1.00 5'&3' 0.53
                                                                ??? ?????  ????????       NC
                                                          ~~~~~~<<<-<<<<<--<<<<<<<<~~~~~~ CS
                                              MIR167_1  1 <[13]*UGAAGCUGCCAGCAUGAuC*[98]> 130
                                                                UGAAGCUGCCAGCAUGAUC
                                           zmu-miR167b  1 <[ 0]*UGAAGCUGCCAGCAUGAUC*[ 0]> 19
                                                          ......*******************...... PP

Targets

Predicted by TargetFinder

NM_000071 ;   NM_000140 ;   NM_000236 ;   NM_000922 ;   NM_001005176 ;   NM_001005751 ;   NM_001012515 ;   NM_001018057 ;   NM_001024843 ;   NM_001080509 ;   NM_001114118 ;   NM_001122853 ;   NM_001145024 ;   NM_001159770 ;   NM_001162501 ;   NM_001166131 ;   NM_001178008 ;   NM_001178009 ;   NM_001197244 ;   NM_001198773 ;   NM_001198774 ;   NM_001198775 ;   NM_001261834 ;   NM_001278451 ;   NM_001278452 ;   NM_001278453 ;   NM_001278651 ;   NM_001282356 ;   NM_001282357 ;   NM_001286779 ;   NM_001291398 ;   NM_001297549 ;   NM_001301061 ;   NM_001307960 ;   NM_001308026 ;   NM_001308319 ;   NM_001317930 ;   NM_001318040 ;   NM_001318041 ;   NM_001318042 ;   NM_001320298 ;   NM_001321072 ;   NM_001321073 ;   NM_001321981 ;   NM_001330102 ;   NM_001330220 ;   NM_001330332 ;   NM_001330504 ;   NM_001330721 ;   NM_001346743 ;   NM_001346744 ;   NM_001352127 ;   NM_001352156 ;   NM_001352157 ;   NM_001352158 ;   NM_001352691 ;   NM_001352692 ;   NM_001352693 ;   NM_001354006 ;   NM_001354007 ;   NM_001354008 ;   NM_001354009 ;   NM_001354010 ;   NM_001354012 ;   NM_001354014 ;   NM_001354015 ;   NM_001363569 ;   NM_001363570 ;   NM_001369623 ;   NM_001369624 ;   NM_001369625 ;   NM_001369626 ;   NM_001369628 ;   NM_001369629 ;   NM_001370301 ;   NM_001370302 ;   NM_001370528 ;   NM_001371094 ;   NM_001371095 ;   NM_001707 ;   NM_002651 ;   NM_002883 ;   NM_002911 ;   NM_003728 ;   NM_004749 ;   NM_005632 ;   NM_006392 ;   NM_007237 ;   NM_012293 ;   NM_013253 ;   NM_014801 ;   NM_015078 ;   NM_015088 ;   NM_015881 ;   NM_019109 ;   NM_020880 ;   NM_025134 ;   NM_025141 ;   NM_031961 ;   NM_031963 ;   NM_032293 ;   NM_052832 ;   NM_078474 ;   NM_080622 ;   NM_133371 ;   NM_133374 ;   NM_133640 ;   NM_134266 ;   NM_139177 ;   NM_152463 ;   NM_172364 ;   NM_175709 ;   NM_182557 ;   NM_198859 ;   NR_024248 ;   NR_026552 ;   NR_027700 ;   NR_036682 ;   NR_038253 ;   NR_038254 ;   NR_038255 ;   NR_040066 ;   NR_040067 ;   NR_040068 ;   NR_040069 ;   NR_040070 ;   NR_046417 ;   NR_104591 ;   NR_105001 ;   NR_110089 ;   NR_120673 ;   NR_121624 ;   NR_121680 ;   NR_134278 ;   NR_135280 ;   NR_136885 ;   NR_145428 ;   NR_148682 ;  

Predicted by TAPIR

NM_000157 ;   NM_000236 ;   NM_000783 ;   NM_001005176 ;   NM_001005741 ;   NM_001005742 ;   NM_001024843 ;   NM_001024845 ;   NM_001080837 ;   NM_001109977 ;   NM_001122853 ;   NM_001159770 ;   NM_001162501 ;   NM_001165412 ;   NM_001166131 ;   NM_001171812 ;   NM_001178005 ;   NM_001242412 ;   NM_001247996 ;   NM_001261380 ;   NM_001261834 ;   NM_001278451 ;   NM_001278452 ;   NM_001278453 ;   NM_001278651 ;   NM_001286741 ;   NM_001286779 ;   NM_001297549 ;   NM_001307960 ;   NM_001308026 ;   NM_001317930 ;   NM_001318040 ;   NM_001318041 ;   NM_001318042 ;   NM_001318392 ;   NM_001319226 ;   NM_001321538 ;   NM_001321539 ;   NM_001321981 ;   NM_001328626 ;   NM_001328627 ;   NM_001328628 ;   NM_001328629 ;   NM_001328630 ;   NM_001330332 ;   NM_001330504 ;   NM_001348694 ;   NM_001352691 ;   NM_001352692 ;   NM_001352693 ;   NM_001362924 ;   NM_001362925 ;   NM_001362926 ;   NM_001364170 ;   NM_001367493 ;   NM_001370528 ;   NM_002883 ;   NM_002911 ;   NM_003998 ;   NM_004749 ;   NM_006576 ;   NM_006934 ;   NM_007237 ;   NM_012293 ;   NM_014793 ;   NM_015078 ;   NM_015088 ;   NM_015320 ;   NM_017614 ;   NM_018266 ;   NM_018482 ;   NM_018897 ;   NM_019109 ;   NM_020700 ;   NM_020731 ;   NM_020880 ;   NM_022492 ;   NM_025141 ;   NM_032293 ;   NM_032329 ;   NM_033315 ;   NM_057157 ;   NM_078474 ;   NM_080622 ;   NM_133371 ;   NM_133374 ;   NM_139177 ;   NM_152463 ;   NM_172364 ;   NM_198859 ;   NM_199280 ;   NM_201649 ;   NR_002188 ;   NR_015406 ;   NR_024248 ;   NR_026552 ;   NR_027749 ;   NR_038253 ;   NR_038254 ;   NR_038255 ;   NR_040018 ;   NR_040019 ;   NR_048548 ;   NR_073506 ;   NR_104591 ;   NR_105001 ;   NR_110089 ;   NR_120673 ;   NR_121624 ;   NR_121680 ;   NR_134278 ;   NR_135280 ;   NR_136885 ;