  In several recent papers new gene-detection algorithms were proposed for
detecting protein-coding regions without requiring learning dataset of already
known genes. The fact that unsupervised gene-detection is possible closely
connected to existence of a cluster structure in oligomer frequency
distributions. In this paper we study cluster structure of several genomes in
the space of their triplet frequencies, using pure data exploration strategy.
Several complete genomic sequences were analyzed, using visualization of tables
of triplet frequencies in a sliding window. The distribution of 64-dimensional
vectors of triplet frequencies displays a well-detectable cluster structure.
The structure was found to consist of seven clusters, corresponding to
protein-coding information in three possible phases in one of the two
complementary strands and in the non-coding regions with high accuracy (higher
than 90% on the nucleotide level). Visualizing and understanding the structure
allows to analyze effectively performance of different gene-prediction tools.
Since the method does not require extraction of ORFs, it can be applied even
for unassembled genomes. The information content of the triplet distributions
and the validity of the mean-field models are analysed.
