Demonstration of two novel methods for predicting functional siRNA efficiency - siRNA

Demonstration of two novel methods for predicting functional siRNA efficiency
29 May 2006
PeiLin Jia , TieLiu Shi , YuDong Cai , YiXue Li
BMC Bioinformatics
Abstract (provisional)
Background
siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well known that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs.
Results
In this paper, two systems have been established for the prediction of functional siRNAs: (1) a statistical model based on sequence information and (2) a machine learning model based on three features of siRNA sequences, namely binary description, thermodynamic profile and nucleotide composition. Both of the two methods show high performance on the two datasets we have constructed for training the model.
Conclusions
Both of the two methods studied in this paper emphasize the importance of sequence information for the prediction of functional siRNAs. The way of denoting a bio-sequence by binary system in mathematical language might be helpful in other analysis work associated with fixed-length bio-sequence.