Finding new protein-coding genes is one of the most important goals of 
eukaryotic genome sequencing projects. However, genomic organization of novel 
eukaryotic genomes is diverse and ab initio gene finding tools tuned up for 
previously studied species are rarely suitable for efficacious gene hunting in 
DNA sequences of a new genome. Gene identification methods based on cDNA and 
expressed sequence tag (EST) mapping to genomic DNA or those using alignments to 
closely related genomes rely either on existence of abundant cDNA and EST data 
and/or availability on reference genomes. Conventional statistical ab initio 
methods require large training sets of validated genes for estimating gene model 
parameters. In practice, neither one of these types of data may be available in 
sufficient amount until rather late stages of the novel genome sequencing. 
Nevertheless, we have shown that gene finding in eukaryotic genomes could be 
carried out in parallel with statistical models estimation directly from yet 
anonymous genomic DNA. The suggested method of parallelization of gene 
prediction with the model parameters estimation follows the path of the 
iterative Viterbi training. Rounds of genomic sequence labeling into coding and 
non-coding regions are followed by the rounds of model parameters estimation. 
Several dynamically changing restrictions on the possible range of model 
parameters are added to filter out fluctuations in the initial steps of the 
algorithm that could redirect the iteration process away from the biologically 
relevant point in parameter space. Tests on well-studied eukaryotic genomes have 
shown that the new method performs comparably or better than conventional 
methods where the supervised model training precedes the gene prediction step. 
Several novel genomes have been analyzed and biologically interesting findings 
are discussed. Thus, a self-training algorithm that had been assumed feasible 
only for prokaryotic genomes has now been developed for ab initio eukaryotic 
gene identification.