A Markovian analysis of bacterial genome sequence constraints

The arrangement of nucleotides within a bacterial chromosome is influenced by numerous factors. The degeneracy of the third codon within each reading frame allows some flexibility of nucleotide selection; however, the third nucleotide in the triplet of each codon is at least partly determined by the...

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Main Authors: Aaron D. Skewes, Roy D. Welch
Format: Article
Language:English
Published: PeerJ Inc. 2013-08-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/127.pdf
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spelling doaj-19cdce595cbd40c3afc351f61dcb685c2020-11-24T23:47:21ZengPeerJ Inc.PeerJ2167-83592013-08-011e12710.7717/peerj.127127A Markovian analysis of bacterial genome sequence constraintsAaron D. Skewes0Roy D. Welch1Department of Biology, Syracuse University, Syracuse, NY, United StatesDepartment of Biology, Syracuse University, Syracuse, NY, United StatesThe arrangement of nucleotides within a bacterial chromosome is influenced by numerous factors. The degeneracy of the third codon within each reading frame allows some flexibility of nucleotide selection; however, the third nucleotide in the triplet of each codon is at least partly determined by the preceding two. This is most evident in organisms with a strong G + C bias, as the degenerate codon must contribute disproportionately to maintaining that bias. Therefore, a correlation exists between the first two nucleotides and the third in all open reading frames. If the arrangement of nucleotides in a bacterial chromosome is represented as a Markov process, we would expect that the correlation would be completely captured by a second-order Markov model and an increase in the order of the model (e.g., third-, fourth-…order) would not capture any additional uncertainty in the process. In this manuscript, we present the results of a comprehensive study of the Markov property that exists in the DNA sequences of 906 bacterial chromosomes. All of the 906 bacterial chromosomes studied exhibit a statistically significant Markov property that extends beyond second-order, and therefore cannot be fully explained by codon usage. An unrooted tree containing all 906 bacterial chromosomes based on their transition probability matrices of third-order shares ∼25% similarity to a tree based on sequence homologies of 16S rRNA sequences. This congruence to the 16S rRNA tree is greater than for trees based on lower-order models (e.g., second-order), and higher-order models result in diminishing improvements in congruence. A nucleotide correlation most likely exists within every bacterial chromosome that extends past three nucleotides. This correlation places significant limits on the number of nucleotide sequences that can represent probable bacterial chromosomes. Transition matrix usage is largely conserved by taxa, indicating that this property is likely inherited, however some important exceptions exist that may indicate the convergent evolution of some bacteria.https://peerj.com/articles/127.pdfSequencingMarkov modelrRNABacteriaTopology
collection DOAJ
language English
format Article
sources DOAJ
author Aaron D. Skewes
Roy D. Welch
spellingShingle Aaron D. Skewes
Roy D. Welch
A Markovian analysis of bacterial genome sequence constraints
PeerJ
Sequencing
Markov model
rRNA
Bacteria
Topology
author_facet Aaron D. Skewes
Roy D. Welch
author_sort Aaron D. Skewes
title A Markovian analysis of bacterial genome sequence constraints
title_short A Markovian analysis of bacterial genome sequence constraints
title_full A Markovian analysis of bacterial genome sequence constraints
title_fullStr A Markovian analysis of bacterial genome sequence constraints
title_full_unstemmed A Markovian analysis of bacterial genome sequence constraints
title_sort markovian analysis of bacterial genome sequence constraints
publisher PeerJ Inc.
series PeerJ
issn 2167-8359
publishDate 2013-08-01
description The arrangement of nucleotides within a bacterial chromosome is influenced by numerous factors. The degeneracy of the third codon within each reading frame allows some flexibility of nucleotide selection; however, the third nucleotide in the triplet of each codon is at least partly determined by the preceding two. This is most evident in organisms with a strong G + C bias, as the degenerate codon must contribute disproportionately to maintaining that bias. Therefore, a correlation exists between the first two nucleotides and the third in all open reading frames. If the arrangement of nucleotides in a bacterial chromosome is represented as a Markov process, we would expect that the correlation would be completely captured by a second-order Markov model and an increase in the order of the model (e.g., third-, fourth-…order) would not capture any additional uncertainty in the process. In this manuscript, we present the results of a comprehensive study of the Markov property that exists in the DNA sequences of 906 bacterial chromosomes. All of the 906 bacterial chromosomes studied exhibit a statistically significant Markov property that extends beyond second-order, and therefore cannot be fully explained by codon usage. An unrooted tree containing all 906 bacterial chromosomes based on their transition probability matrices of third-order shares ∼25% similarity to a tree based on sequence homologies of 16S rRNA sequences. This congruence to the 16S rRNA tree is greater than for trees based on lower-order models (e.g., second-order), and higher-order models result in diminishing improvements in congruence. A nucleotide correlation most likely exists within every bacterial chromosome that extends past three nucleotides. This correlation places significant limits on the number of nucleotide sequences that can represent probable bacterial chromosomes. Transition matrix usage is largely conserved by taxa, indicating that this property is likely inherited, however some important exceptions exist that may indicate the convergent evolution of some bacteria.
topic Sequencing
Markov model
rRNA
Bacteria
Topology
url https://peerj.com/articles/127.pdf
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