Identification of motifs that significantly associate with antisense sequence activity

Antisense oligonucleotides are able to modulate gene expression levels by a posttranscriptional mechanism involving an RNA:DNA heteroduplex and the cleavage of the RNA by RNase H (1,2). This sequence-specific hybridization between an antisense molecule and its target can, in principle, allow the inhibition of any target mRNA without affecting closely related genes. One approach for identifying antisense sequences that knockdown a target gene to desired levels is to screen many candidate antisense molecules to find the most active antisense sequences. In addition to antisense screening by trial and error, several computational approaches have been developed to improve the hit rate of selecting active antisense sequences (3-14). These models all associate some factors of an antisense sequence with the antisense sequence’s activity (sequence ~ activity). Methods that identify factors to incorporate into a sequence ~ activity model provide the opportunity not only provide the opportunity to improve the predictive models and avoid factors that unnecessarily introduce complexity, but also are important in contributing to a more complete understanding of the antisense knockdown mechanisms of action.