Triplet periodicity (TP) is a distinctive feature of the protein coding sequences of both prokaryotic and eukaryotic genomes. In this work, we explored the TP difference inside and between 45 prokaryotic genomes. We constructed two hypotheses of TP distribution on a set of coding sequences and generated artificial datasets that correspond to the hypotheses. We found that TP is more similar inside a genome than between genomes and that TP distribution inside a real genome dataset corresponds to the hypothesis which implies that a common TP pattern exists for the majority of sequences inside a genome. Additionally, we performed gene classification based on TP matrixes. This classification showed that TP allows identification of the genome to which a given gene belongs with more than 85% accuracy.

We have developed a de novo method for the identification of dispersed repeats based on the use of random position-weight matrices (PWMs) and an iterative procedure (IP). The created algorithm (IP method) allows detection of dispersed repeats for which the average number of substitutions between any two repeats per nucleotide (x) is less than or equal to 1.5. We have shown that all previously developed methods and algorithms (RED, RECON, and some others) can only find dispersed repeats for x ≤ 1.0. We applied the IP method to find dispersed repeats in the genomes of E. coli and nine other bacterial species. We identify three families of approximately 1.09 × 10⁶, 0.64 × 10⁶, and 0.58 × 10⁶ DNA bases, respectively, constituting almost 50% of the complete E. coli genome. The length of the repeats is in the range of 400 to 600 bp. Other analyzed bacterial genomes contain one to three families of dispersed repeats with a total number of 10³ to 6 × 10³ copies. The existence of such highly divergent repeats could be associated with the presence of a single-type triplet periodicity in various genes or with the packing of bacterial DNA into a nucleoid.

Background: Transposable elements (TEs) constitute a significant part of eukaryotic genomes. Short interspersed nuclear elements (SINEs) are non-autonomous TEs, which are widely represented in mammalian genomes and also found in plants. After insertion in a new position in the genome, TEs quickly accumulate mutations, which complicate their identification and annotation by modern bioinformatics methods. In this study, we searched for highly divergent SINE copies in the genome of rice (Oryza sativasubsp.japonica) using the Highly Divergent Repeat Search Method (HDRSM).Results: The HDRSM considers correlations of neighboring symbols to construct position weight matrix (PWM) for a SINE family, which is then used to perform a search for new copies. In order to evaluate the accuracy of the method and compare it with the RepeatMasker program, we generated a set of SINE copies containing nucleotide substitutions and indels and inserted them into an artificial chromosome for analysis. The HDRSM showed better results both in terms of the number of identified inserted repeats and the accuracy of determining their boundaries. A search for the copies of 39 SINE families in the rice genome produced 14,030 hits; among them, 5704 were not detected by RepeatMasker.Conclusions: The HDRSM could find divergent SINE copies, correctly determine their boundaries, and offer a high level of statistical significance. We also found that RepeatMasker is able to find relatively short copies of the SINE families with a higher level of similarity, while HDRSM is able to find more diverged copies. To obtain a comprehensive profile of SINE distribution in the genome, combined application of the HDRSM and RepeatMasker is recommended.

A new mathematical method for potential reading frameshift detection in protein-coding sequences (cds) was developed. The algorithm is adjusted to the triplet periodicity of each analysed sequence using dynamic programming and a genetic algorithm. This does not require any preliminary training. Using the developed method, cds from the Arabidopsis thaliana genome were analysed. In total, the algorithm found 9,930 sequences containing one or more potential reading frameshift(s). This is ∼21% of all analysed sequences of the genome. The Type I and Type II error rates were estimated as 11% and 30%, respectively. Similar results were obtained for the genomes of Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Rattus norvegicus and Xenopus tropicalis. Also, the developed algorithm was tested on 17 bacterial genomes. We compared our results with the previously obtained data on the search for potential reading frameshifts in these genomes. This study discussed the possibility that the reading frameshift seems like a relatively frequently encountered mutation; and this mutation could participate in the creation of new genes and proteins.