Nucleotide genomic signal methodology

The nucleotide genomic signal (NuGS) methodology is based on the conversion of symbolic nucleotide sequences into digital genomic signals. There have been several attempts to represent nucleotide sequences as digital signals, most using some specific property of the nitrogeneous bases, e.g., the electronion interaction (EII) potential. Unfortunately, the resulting representation tends to be biased, being adequate primarily for the study of those phenomena in which the chosen property plays a key role, but less for others. The representation presented and discussed in this tutorial is unbiased, i.e., it is adequate for a large range of problems related to nucleotide sequence analysis, having been used for both the analysis of global features of genomic sequences, maintained over distances of 10
⁶
–10
⁸
base pairs, as well as for the local study of nucleotide sequences. The large scale approach reveals hidden symmetries and regularities of current and ancestral nucleotide sequences, while the local approach can be used in the analysis of pathogen variability, important in the context of the development of pathogen resistance to treatment. This aspect is especially important for the fast diagnosis and early assessment of drug efficiency, allowing a simple and systematic use of the recent advances in molecular medicine to help clinical decisions. The striking regularities of the digital genomic signals reveal surprising restrictions in the distribution of nucleotides and pairs of nucleotides along DNA sequences, in both prokaryotes and eukaryotes. In what concerns its statistical structural symmetry, a chromosome appears to be more than a plain text, as it also satisfies restrictions evoking the rhythm and rhyme in poems. These regularities can be used to "predict" individual nucleotides in nucleotide sequences, by using a methodology similar to time series prediction and allow to analyze the potential for molecular scale self-repair in processes such as replication, transcription or crossover.