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FOMmiR

MicroRNA Prediction Using a Fixed-Order Markov Model Based on the Secondary Structure Pattern


Introduction:

     FOMmiR is a new generation of miRNA prediction algorithm, using a fixed-order Markov model based on the secondary structure pattern, which successfully realizes a full function recognition of the mature miRNAs directly from the hairpin RNA molecules.

     For a training dataset containing 809 human pre-miRNAs and 6441 human pseudo-miRNA hairpins, the model's parameters were defined and evaluated. The results showed that FOMmiR reached 91% accuracy on the human dataset through 5-fold crossvalidation.

     Moreover, for the independent test datasets, the FOMmiR presented an outstanding prediction in human and other species including vertebrates, Drosophila, worms and viruses, even plants, in contrast to the well-known algorithms and models. Especially, the FOMmiR was not only able to distinguish the miRNA precursors from the hairpins, but also locate the position and strand of the mature miRNA.



Citation:Shen W, Chen M, Wei G, Li Y (2012) MicroRNA Prediction Using a Fixed-Order Markov Model Based on the Secondary Structure Pattern. PLoS ONE 7(10): e48236. doi:10.1371/journal.pone.0048236

Yanli Lab, Medical Research Center, Southwest Hospital,
Third Military Medical University, Chongqing 400038, China.