Using triplet periodicity of nucleotide sequences for finding potential reading frame shifts in genes.

We introduce a novel approach for the detection of possible mutations leading to a reading frame (RF) shift in a gene. Deletions and insertions of DNA coding regions are considerable events for genes because an RF shift results in modifications of the extensive region of amino acid sequence coded by a gene. The suggested method is based on the phenomenon of triplet periodicity (TP) in coding regions of genes and its relative resistance to substitutions in DNA sequence. We attempted to extend 326 933 regions of continuous TP found in genes from the KEGG databank by considering possible insertions and deletions. We revealed totally 824 genes where such extension was possible and statistically significant. Then we generated amino acid sequences according to active (KEGG's) and hypothetically ancient RFs in order to find confirmation of a shift at a protein level. Consequently, 64 sequences have protein similarities only for ancient RF, 176 only for active RF, 3 for both and 581 have no protein similarity at all. We aimed to have revealed lower bound for the number of genes in which a shift between RF and TP is possible. Further ways to increase the number of revealed RF shifts are discussed.

[Classification of triplet periodicity in DNA sequences of genes taken from KEGG databank].

We conducted classification for 472,288 regions of triplet periodicity found in 578,868 genes from release 29 of KEGG databank. A new concept of triplet periodicity class and a measure of similarity between them are introduced. Totally 2520 classes were created that contain 94% of found triplet periodicity. For 92% of triplet periodicity regions contained in classes an identical linkage of triplet periodicity to reading frame is observed. For the rest triplet periodicity cases a shift between reading frame of a gene and reading frame common for majority of genes contained in a class of triplet periodicity was observed. These periodicity regions were encoded into hypothetical amino acid sequences in accordance with reading frame built by triplet periodicity class. By BLAST program it was shown that 2660 hypothetical amino acid sequences have statistically significant similarity with proteins from UniProt databank. We suppose that 8% of triplet periodicity regions that joined classes mutated by means of reading frame shift. Created classes of triplet periodicity can be used for identification of coding regions of genes as well as for searching for mutations arisen from reading frame shift.

Classification analysis of triplet periodicity in protein-coding regions of genes.

We introduce a new concept of triplet periodicity class (TPC) and a measure of similarity between such classes. We performed classification of 472288 triplet periodicity (TP) regions found in 578868 genes from 29th release of KEGG databank. Totally 2520 classes were obtained. They contain 94% of 472288 found cases of TP. For 92% of TP regions contained in classes the same linkage of TP to open reading frame (ORF) is observed. For 8% of TP cases we revealed a shift between ORF of a gene and ORF common for majority of genes contained in a TPC. For these 8% of periodic regions the hypothetical amino acid sequences corresponding to ORF built by TPC were made. BLAST program has shown that 2679 hypothetical amino acid sequences have statistically significant similarity with proteins from UniProt databank. We suppose that 8% of TP regions contained in classes possess a mutation originating from ORF shift. Obtained TPCs can be used for identification of genes' coding regions as well as for searching for mutations arisen arising from ORF shift.

[The informational concept of searching for periodicity in symbol sequences]. / Informatsionnaia kontseptsiia poiska periodichnosti v simvol'nykh posledovatel'nostiakh.

Method of informational decomposition has been developed, allowing one to reveal hidden periodicity in any symbol sequences. The informational decomposition is calculated without conversion of a symbol sequence into the numerical one, which facilitates finding periodicities in a symbol sequence. The method permits introducing an analog of the autocorrelation function of a symbol sequence. The method developed by us has been applied to reveal hidden periodicities in nucleotide and amino acid sequences, as well as in different poetical texts. Hidden periodicity has been detected in various genes, testifying to their quantum structure. The functional and structural role of hidden periodicity is discussed.