Markovian language model of the DNA and its information content

TL;DR

This paper introduces a Markovian model for DNA sequences that simplifies their complexity, enabling accurate prediction of nucleotide group transitions and analysis of informational content using a network-based approach.

Contribution

The work presents a novel Markovian language model for DNA that captures its grammatical structure and allows for efficient analysis of genetic information and gene similarity detection.

Findings

High accuracy in predicting DNA group transitions

Reduced DNA to a network of tens of nodes for analysis

Identification of sequences responsible for most information content

Abstract

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.