Markovian language model of the DNA and its information content

Shambhavi Srivastava, Murilo Da Silva Baptista

Research output: Contribution to journalArticle

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number150527
JournalRoyal Society Open Science
Volume3
Early online date6 Jan 2016
DOIs
Publication statusPublished - Jan 2016

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DNA
Nucleotides
Bacteria
Genes

Keywords

  • DNA linguistic model
  • symbolic dynamics
  • Markov partitions
  • chains
  • models
  • information and ergodic theory
  • network theory
  • correlation decay
  • mutual information rate and entropy rate

Cite this

Markovian language model of the DNA and its information content. / Srivastava, Shambhavi; Baptista, Murilo Da Silva.

In: Royal Society Open Science, Vol. 3, 150527, 01.2016.

Research output: Contribution to journalArticle

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author = "Shambhavi Srivastava and Baptista, {Murilo Da Silva}",
note = "S.S. and M.S.B. acknowledge the Engineering and Physical Sciences Research Council (EPSRC), grant ref. EP/I032608/1.",
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N2 - 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.

AB - 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.

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KW - models

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KW - network theory

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