# Are There Seven Symbols for the Nucleotide-Based Genetic Code?
## 4 Discussion  
This study aimed to examine the consequences of the assumption that the seven-symbol code is used in the recording of genomic information. The authors assume that if it is true, then the frequency-range dependency of words derived by contextual tokenization would follow the power law. To verify this hypothesis, we juxtapose the contextual tokenization method with the other most commonly used forms of tokenization, i.e., triplet  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Random Texts**} & \multicolumn{10}{c}{**Genomes**} \\ \hline PL & \% & \% & \% & \% & \multicolumn{1}{c}{**Visual**} & \multicolumn{1}{c}{\(D\)} & **No** & **Analysis** & **No** & \(\overset{-}{D}\) & **Visual** & \% & \% & \% & PL \\ Fit & PT & EXP & LN & & & & & & & & LN & EXP & PT & Fit \\ \hline - & -- & -- & -- & - & -- & 0/60 & **SOTTAN** & 0/60 & — & - & -- & — & -- & - \\ - & -- & -- & -- & - & -- & 0/60 & **SNTCAN** & 0/60 & — & - & -- & — & -- & - \\ - & -- & -- & -- & - & -- & 0/60 & **SNTTAC** & 0/60 & — & - & -- & — & - \\ - & -- & -- & -- & - & -- & 0/60 & **SNTTAC** & 0/60 & — & - & -- & — & - \\ - & -- & -- & -- & - & -- & 0/60 & **SCTTAC** & 0/60 & — & - & -- & — & - \\ \hline - & -- & -- & -- & + & -- & 0/60 & **SOTFAN** & 60/60 & 0.008 \(\pm\) 0.003 & ++ & 10\% & 0\% & 100\% & +++ \\ - & -- & -- & -- & + & -- & 0/60 & **SNTCAN** & 33/60 & 0.019 \(\pm\) 0.014 & ++ & 3\% & 0\% & 94\% & + \\ - & -- & -- & -- & ++ & -- & 0/60 & **SOTFAC** & 60/60 & 0.013 \(\pm\) 0.004 & ++ & 3\% & 0\% & 100\% & + \\ - & -- & -- & -- & ++ & -- & 0/60 & **SNTTAC** & 58/60 & 0.022 \(\pm\) 0.010 & ++ & 12\% & 0\% & 100\% & + \\ - & 100\% & 0\% & 100\% & +++ & 0.017 \(\pm\) 0.000 & 60/60 & **SCTFAC** & 60/60 & 0.008 \(\pm\) 0.004 & ++ & 28\% & 0\% & 100\% & +++ \\ \hline - & -- & -- & -- & + & -- & 0/60 & **SOTCAC** & 19/60 & 0.038 \(\pm\) 0.010 & + & 26\% & 0\% & 5\% & + \\ - & -- & -- & -- & +/ & -- & 0/60 & **SNTCAC** & 4/60 & 0.029 \(\pm\) 0.011 & +/- & 0\% & 0\% & 0\% & - \\ ++ & 0\% & 0\% & 2\% & +++ & 0.020 \(\pm\) 0.002 & 60/60 & **SCTCAC** & 60/60 & 0.012 \(\pm\) 0.003 & +++ & 0\% & 0\% & 88\% & +++ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of goodness-of-fit for power-law frequency-rank distributions in random and genomic texts with \(D\leq\) 0.05.  
Figure 9: The comparison of the K-S test for SCTCAC analysis of genomic and random texts. \(D\) marks the distance parameter of the K-S test.  
tokenization and frame tokenization. The hypothesis was tested on genomic texts of different species and random pseudo-genomic texts.  
Our study yields several significant findings:  
* The frequency-range dependency of tokens generated via triplet tokenization consistently fails to follow a power-law distribution, suggesting that the conventional nucleotide sequence tokenization method is inadequate for representing semiotic information in genomic sequences.
* The genomic corpora generated by frame tokenization in most cases followed the Zipf distribution, supporting the claim of hidden informational structures within genomic sequences.
* The excellent power-law performance of shuffled contextual genomic texts (SCTCAC) corroborates our hypothesis that genomic sequences should be analyzed using a seven-character alphabet system.
* The moderate performance of original contextual tokenization genomic corpora (SOTCAC) indicates notable differences between genomic texts and natural language structures.
* Observed differences in word frequency distributions between random texts and genomes provide evidence that our findings regarding genomic sequences are not attributable to random chance.
* These results collectively suggest that novel tokenization methods, particularly those employing a seven-character alphabet, may offer insightful approaches to analyzing the informational content of genomic sequences.