The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR160
  5. lfo-miR160a

Accession: B40518944

Entry

ID: lfo-miR160a
Alias: F243.L000530.A29*
Sequence: UGCCUGGCUCCCUGAAUGCCA
Description: MIR160 putative
Species: 枫香树  Species latin name: Liquidambar formosana
TCM Name: Fengxiangzhi(枫香脂)   Lulutong(路路通)  
TCM Resource (Sample number): 路路通(1)
Predicted by Mirnovo: -
Evidence support: Mirbase
Experimental status: Not yet

Homologs

miRBase Homologs

ahy-miR160-5p; aof-miR160a; bdi-miR160e-5p; cme-miR160d; cpa-miR160d; mes-miR160f; mtr-miR160c; osa-miR160f-5p; ptc-miR160e-5p; ptc-miR160f; rco-miR160c; sbi-miR160f; tcc-miR160a; vca-miR160-5p;

Bencao Homologs

cco-miR160a; hmu-miR160; lfo-miR160a; nnu-miR160b;

Rfam Alignments

>> mir-160  mir-160 microRNA precursor family
 rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc
 ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----
  (1) ?     0.032   16.4   0.0  cm        4       24 ~~           1          21 + ~~ 1.00 5'&3' 0.62
                              ??? ??????? ?????????       NC
                         ~~~~~<<<-<<<<<<<-<<<<<<<<<~~~~~~ CS
              mir-160  1 <[3]*UGCCUGGCUCCCuGuAUGCCA*[66]> 90
                              UGCCUGGCUCCCUG AUGCCA
          nnu-miR160b  1 <[0]*UGCCUGGCUCCCUGAAUGCCA*[ 0]> 21
                         .....*********************...... PP

Targets

Predicted by TargetFinder

NM_001003828 ;   NM_001014431 ;   NM_001014432 ;   NM_001029883 ;   NM_001037806 ;   NM_001040703 ;   NM_001141979 ;   NM_001141980 ;   NM_001164586 ;   NM_001199427 ;   NM_001243385 ;   NM_001243386 ;   NM_001300832 ;   NM_001300833 ;   NM_001308370 ;   NM_001349608 ;   NM_001349609 ;   NM_001349610 ;   NM_001349612 ;   NM_001349613 ;   NM_001349614 ;   NM_001349615 ;   NM_001349616 ;   NM_001349617 ;   NM_001349618 ;   NM_001349619 ;   NM_001349620 ;   NM_001349621 ;   NM_001349622 ;   NM_001349623 ;   NM_001349624 ;   NM_001349625 ;   NM_001349626 ;   NM_001355001 ;   NM_001368048 ;   NM_002530 ;   NM_003300 ;   NM_005163 ;   NM_005657 ;   NM_007215 ;   NM_013327 ;   NM_015080 ;   NM_015215 ;   NM_015235 ;   NM_020643 ;   NM_031904 ;   NM_032283 ;   NM_052924 ;   NM_138732 ;   NM_145725 ;   NM_145726 ;   NM_152250 ;   NM_194312 ;   NR_027927 ;   NR_102401 ;   NR_110101 ;   NR_110759 ;  

Predicted by TAPIR

NM_001014431 ;   NM_001014432 ;   NM_001029883 ;   NM_001037806 ;   NM_001164586 ;   NM_001178003 ;   NM_001199427 ;   NM_001300833 ;   NM_001308370 ;   NM_001350979 ;   NM_001350980 ;   NM_001350981 ;   NM_001350982 ;   NM_001350983 ;   NM_001350984 ;   NM_001350985 ;   NM_001350986 ;   NM_001368048 ;   NM_003300 ;   NM_005163 ;   NM_007215 ;   NM_015080 ;   NM_015235 ;   NM_020643 ;   NM_031904 ;   NM_032283 ;   NM_052924 ;   NM_138732 ;   NM_145115 ;   NM_145725 ;   NM_145726 ;   NM_194312 ;   NR_027927 ;   NR_110101 ;   NR_110759 ;