Demonstration of two novel methods for predicting functional siRNA efficiency.
BACKGROUND
siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know 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.
Jia
Peilin
P
Bioinformatics Center, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. pljia@sibs.ac.cn
Shi
Tieliu
T
Cai
Yudong
Y
Li
Yixue
Y
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D030541:Databases, Genetic; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D034622:RNA Interference / Q000502:physiology*; D034741:RNA, Small Interfering / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
D034741:RNA, Small Interfering