  This work proposes a markovian memoryless model for the DNA that simplifies
enormously the complexity of it. We encode nucleotide sequences into symbolic
sequences, called words, from which we establish meaningful length of words and
group of words that share symbolic similarities. Interpreting a node to
represent a group of similar words and edges to represent their functional
connectivity allows us to construct a network of the grammatical rules
governing the appearance of group of words in the DNA. Our model allows to
predict the transition between group of words in the DNA with unprecedented
accuracy, and to easily calculate many informational quantities to better
characterize the DNA. In addition, we reduce the DNA of known bacteria to a
network of only tens of nodes, show how our model can be used to detect similar
(or dissimilar) genes in different organisms, and which sequences of symbols
are responsible for the most of the information content of the DNA. Therefore,
the DNA can indeed be treated as a language, a markovian language, where a
"word" is an element of a group, and its grammar represents the rules behind
the probability of transitions between any two groups.
