# Are There Seven Symbols for the Nucleotide-Based Genetic Code?
###### Abstract  
The common assumption is that genetic information is built on a four-symbol alphabet, i.e., DNA nucleotides, the smallest meaningful blocks of genomes are codon triplets, and the record of genetic information does not contain any asserted symbols playing the role of the space. It is, however, well known that some nucleotides in some codons are redundant. Our study, therefore, tests the alternative scenario. As the same nucleotide may play various semiotic roles, the genomic alphabet actually contains seven semiotic symbols. Consequently, the meaningful fragments of genomes (words) can be of different sizes, and there are asserted symbols in the record of genomic information. If this is true, then, similarly to natural languages, the frequency-range of these genomic words should follow the power-law distribution. The presented hypothesis was tested, in comparison to competitive (codon-based and n-tuple) forms of tokenization, on a wide range of genomic texts. Zipf's law; genetic information; code biology; power law; genetic code; linguistic +
Footnote †: journal: Applied  
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power-law phenomenon and states that the frequency of any word is linearly proportional to its range in a text written in a natural language. In [PERSON]'s corpus analysis, the most common word has a range of r = 1, the second most common has a range of 2, and so on, up to the least used word. The distribution is represented on the double logarithmic scale, where the frequency decays linearly as the rank increases. In our approach, we followed the linguistic metaphors as it is easier to understand the method of genome analysis applied in our studies and achieved results. For us, the power-law distribution is more a detector of some kind of meaningful order hidden in analyzed texts [21]. Whether this structure meets all language requirements of or not, for our purpose, is a secondary matter that will not be addressed here. Having established that, we can ask in what respect does the genome resemble language? The most obvious answer is that as in the English language, there are 26 letters (used as building blocks for meaningful words and sentences), the genetic alphabet contains a four-nucleotide alphabet (A, G, C, T) which in proper rearrangement codes information. However, differently from most natural languages, genomic \"texts\" have no obvious way to distinguish words. Application of the sequence-rank analysis thus requires some method of word extraction, i.e., tokenization. In natural languages, words are separated by whitespaces and punctuation marks such as commas, semicolons, full stops, and others. The genomic text or randomly generated sequences of unknown punctuation marks need a different approach. For structural reasons, it cannot be a break in the nucleotide chain. The sliding window method [22] (more recognizable as the n-tuple method) was designed to resolve that problem. This technique, further called \"frame tokenization\" (TF), uses the frame of the size of n-characters to cut n-size words (Figures 1 and 2). It was shown that the corpus of natural language merged in continuous text and analyzed with the sliding window method preserves the Zipf distribution [22]. Frame tokenization (TF) is commonly used with text of unknown tokenization characters (deprived of whitespaces or other punctuation symbols), and according to the literature, it can be a proper tool for language detection [22; 23; 24]. Therefore, the frame method serves as a baseline for comparing other methods and an initial procedure for examining texts of unknown tokenization procedure like genomes. The second technique of tokenization is more specific to the genome as it utilizes the properties of the genetic code. Each codon consists of three nucleotides; therefore, it seems natural to assume that triplets are basic words in genomic texts. This approach we called triplet tokenization (TT).