Reliable prediction of Drosha processing sites improves microRNA gene prediction.
BACKGROUND
Mature microRNAs (miRNAs) are processed from long hairpin transcripts. Even though it is only the first of several steps, the initial Drosha processing defines the mature product and is characteristic for all miRNA genes. Methods that can separate between true and false processing sites are therefore essential to miRNA gene discovery.


RESULTS
We present a classifier that predicts 5' Drosha processing sites in hairpins that are candidate miRNAs. The classifier, called Microprocessor SVM, correctly predicts the processing site for 50% of known human 5' miRNAs, and 90% of its predictions are within two nucleotides of the true site. Another classifier that is trained on the output from the Microprocessor SVM outperforms existing methods for prediction of unconserved miRNAs. Reanalysis of characteristics and supporting evidence for a set of newly annotated miRNAs shows that some miRNAs may be misannotated. This suggests that expressed hairpins should not be annotated as miRNAs until they are verified to be Drosha and Dicer substrates.


BACKGROUND
The classifiers are publicly available at https://demo1.interagon.com/miRNA/
Helvik
Snorre A
SA
Department of Computer and Information Science, Norwegian University of Science and Technology, NO-7052 Trondheim, Norway.
Snøve
Ola
O
Saetrom
Pål
P
D001185:Artificial Intelligence; D001483:Base Sequence; D001665:Binding Sites; D035683:MicroRNAs / Q000235:genetics*; D008969:Molecular Sequence Data; D011485:Protein Binding; D012323:RNA Processing, Post-Transcriptional / Q000235:genetics*; D043244:Ribonuclease III / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
D035683:MicroRNAs; C517918:DROSHA protein, human; D043244:Ribonuclease III