We present a highly accurate gene-prediction system for eukaryotic genomes, 
called mGene. It combines in an unprecedented manner the flexibility of 
generalized hidden Markov models (gHMMs) with the predictive power of modern 
machine learning methods, such as Support Vector Machines (SVMs). Its excellent 
performance was proved in an objective competition based on the genome of the 
nematode Caenorhabditis elegans. Considering the average of sensitivity and 
specificity, the developmental version of mGene exhibited the best prediction 
performance on nucleotide, exon, and transcript level for ab initio and 
multiple-genome gene-prediction tasks. The fully developed version shows 
superior performance in 10 out of 12 evaluation criteria compared with the other 
participating gene finders, including Fgenesh++ and Augustus. An in-depth 
analysis of mGene's genome-wide predictions revealed that approximately 2200 
predicted genes were not contained in the current genome annotation. Testing a 
subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression 
for 24 (42%) of them. mGene missed 300 annotated genes, out of which 205 were 
unconfirmed. RT-PCR testing of 24 of these genes resulted in a success rate of 
merely 8%. These findings suggest that even the gene catalog of a well-studied 
organism such as C. elegans can be substantially improved by mGene's 
predictions. We also provide gene predictions for the four nematodes C. 
briggsae, C. brenneri, C. japonica, and C. remanei. Comparing the resulting 
proteomes among these organisms and to the known protein universe, we identified 
many species-specific gene inventions. In a quality assessment of several 
available annotations for these genomes, we find that mGene's predictions are 
most accurate.