Improving tRNAscan-SE Annotation Results via Ensemble Classifiers.tRNAScan-SE is a tRNA detection program that is widely used for tRNA annotation; however, the false positive rate of tRNAScan-SE is unacceptable for large sequences.  Here, we used a machine learning method to try to improve the tRNAScan-SE results.  We obtained real and pseudo-tRNA sequences as training data sets using tRNAScan-SE and constructed three different tRNA feature sets.  The positive data set of 623 tRNA sequences was obtained from tRNAdb 2009 and the negative data set was the false positive tRNAs predicted by tRNAscan-SE.  However, tRNA-Predict can work with the output of tRNAscan-SE, which is a genome-wide scanning method, to improve the tRNAscan-SE annotation results. 
tRNAScan-SE
We then set up an ensemble classifier, LibMutil, to predict tRNAs from the training data. 
LibMutil
The positive data set of 623 tRNA sequences was obtained from tRNAdb 2009 and the negative data set was the false positive tRNAs predicted by tRNAscan-SE. 
tRNAdb 2009