Format

Send to

Choose Destination
Biochem Biophys Res Commun. 2004 Aug 13;321(1):247-53.

A comparison of siRNA efficacy predictors.

Author information

1
Interagon AS, Medisinsk teknisk senter, NO-7489 Trondheim, Norway. paal.saetrom@interagon.com

Abstract

Short interfering RNA (siRNA) efficacy prediction algorithms aim to increase the probability of selecting target sites that are applicable for gene silencing by RNA interference. Many algorithms have been published recently, and they base their predictions on such different features as duplex stability, sequence characteristics, mRNA secondary structure, and target site uniqueness. We compare the performance of the algorithms on a collection of publicly available siRNAs. First, we show that our regularized genetic programming algorithm GPboost appears to have a higher and more stable performance than other algorithms on the collected datasets. Second, several algorithms gave close to random classification on unseen data, and only GPboost and three other algorithms have a reasonably high and stable performance on all parts of the dataset. Third, the results indicate that the siRNAs' sequence is sufficient input to siRNA efficacy algorithms, and that other features that have been suggested to be important may be indirectly captured by the sequence.

PMID:
15358242
DOI:
10.1016/j.bbrc.2004.06.116
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center