Artificial intelligence & self-consistent sonification method for converting DNA sequence to music

Abstract

One of the most favorable directions in data sonification is time-series-sonification data (TSSD), due to auditory imagination is very sensitive to changes in time. Biological sonification contains total steps of ways that convert the bio-macromolecules data derived from biology systems into acousmatic music. NMR data of the 13C are particularly well suited data sources for DNA &mRNA sonification. Although, their resonant frequencies are typically in the MHz range, the resonant frequencies span around kHz. The E. coli is consisting of several genes which one of them can be divided into eight series of codons for eight octaves of notes. During NMR calculation with AB-initio methods, these signals are routinely mixed down into the audible frequencies ranges, rendering the need for any additional frequencies transpositions unnecessary. By this study, E .coli’s gene sequences into musical notes for a revealing auditory algorithm has been converted. Estimations of their calculation and optimization of those codons have been done and the total frequencies of each nucleotide have been converted to several music notes and distinguishing those using variations of chemical shifts including pitch, time duration length of notes and even rhythm have been accomplished.