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.
Highlights
MicroRNAs are short RNA sequences that form a hairpin structure which harbors one or more mature miRNAs of about 21 nucleotides in length [1]
The PlantMiRNAPred data was divided into two parts, PlantMiRNAPred-p1 data consisting of 450 pre-miRNAs and 450 pseudo pre-miRNAs and PlantMiRNAPred-p2 data composed of 530 pre-miRNAs and 530 pseudo pre-miRNAs
We showed that for plant miRNA detection, motif based features are useful and they by themselves lead to a good recognition of pre-miRNAs at an accuracy of 90% - 95%, depending on the plant species (Table 4)
Summary
MicroRNAs (miRNAs) are short RNA sequences that form a hairpin structure which harbors one or more mature miRNAs of about 21 nucleotides in length [1]. Mature miRNAs, when incorporated into RISC, provide a template sequence for the recognition of their target mRNAs which are either degraded or whose translation is reduced [2]. Since their discovery by Lee and colleagues [3], they have received increasing attention and it is clear that in case of animals they are involved in many diseases [4] and in case of plants play essential roles in regulation, development, response to cold stress and nutrient deprivation [5]. The so called ab initio miRNA detection methodology is well established in animal models for which abundant learning data are available for example in miRBase [12]
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More From: Journal of Intelligent Learning Systems and Applications
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