title: A comparison of siRNA efficacy predictors.
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.
authors.affiliation: Interagon AS, Medisinsk teknisk senter, NO-7489 Trondheim, Norway. paal.saetrom@interagon.com
authors.forename: Pål
authors.initials: P
authors.lastname: Saetrom
authors.forename: Ola
authors.initials: O
authors.lastname: Snøve
mesh_terms: D000465:Algorithms; D005189:False Positive Reactions; D020868:Gene Silencing / Q000502:physiology*; D005821:Genetic Techniques; D008957:Models, Genetic; D034741:RNA, Small Interfering; D015203:Reproducibility of Results
chemical_list: D034741:RNA, Small Interfering