Skip to main content
. 2016 Nov 9;6:36531. doi: 10.1038/srep36531

Table 2. Computational prediction of miR-107 targets.

Genes TargetScan5.1 Microcosm Target V5 PicTara miRandab
NF1 3 54 442 735
UPF2 24 460 655 358
CACNA2D1 72 506 742 289
ZBTB10 141 64 365 98
BAZ2A 148 21 432 21
HTR4 149 373 111 130
KIAA1033 158 559 508 24
KIF23 173 995 732 959
TLK1 186 884 193 38
TGFBR3 219 18 603 165
LRP1B 250 110 589 553
C20orf39 253 137 600 984
SH3GL2 254 8 475 134
RNF125 261 71 748 271
WNT3A 269 337 319 935
DLL1 285 4 445 394
RGS4 290 414 252 216
SYT6 291 474 524 693
OGT 294 1026 2 915
MTMR4 302 920 200 227
VAMP8 357 49 516 839
CCNE1 420 2 324 1441

The figure in the table indicated the rank order of each gene in the respectivemiRNA targets prediction software.

aLast updated March 2007.

bLast updated June 2005.