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• W2215710308 abstract "MicroRNAs (miRNAs) are short (~21 nt) nucleotide sequences that are either co-transcribed during the production of mRNA or are organized in intergenic regions transcribed by RNA polymerase II. In animals, Drosha, and in plants DCL1 recognize pre-miRNAs which set themselves apart by their characteristic stem loop (hairpin) structure. This structure appears important for their recognition during the process of maturation leading to functioning mature miRNAs. A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. For the prediction of pre-miRNAs, usually machine learning approaches are employed. Therefore, it is necessary to convert the pre-miRNAs into a set of features that can be calculated and many such features have been described. We here select a subset of the previously described features and add sequence motifs as new features. The resulting model which we called MotifmiRNAPred was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field. With an accuracy of 99.95% for the generalized plant model, it distinguishes itself from previously published results which reach an average accuracy between 74% and 98%. We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment." @default.
• W2215710308 created "2016-06-24" @default.
• W2215710308 creator A5043036637 @default.
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• W2215710308 date "2016-01-01" @default.
• W2215710308 modified "2023-09-30" @default.
• W2215710308 title "Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features" @default.
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• W2215710308 doi "https://doi.org/10.4236/jilsa.2016.81002" @default.
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