Andrew G. Hart | Searcharxiv

arXiv2019

A Markovian genomic concatenation model guided by persymmetric matrices

The aim of this work is to provide a rigorous mathematical analysis of a stochastic concatenation model presented by Sobottka and Hart (2011) which allows approximation of the first-order stochastic structure in bacterial DNA by means of a stationary Markov chain. Two probabilistic constructions that rigorously formalize the model are presented. Necessary and sufficient conditions for a Markov chain to be generated by the model are given, as well as the theoretical background needed for designing new algorithms for statistical analyses of real bacterial genomes. It is shown that the model encompasses the Markov chains satisfying intra-strand parity, a property observed in most DNA sequences.

q-bio.GN↗

arXiv2014

A model capturing novel strand symmetries in bacterial DNA

Chargaff's second parity rule for short oligonucleotides states that the frequency of any short nucleotide sequence on a strand is approximately equal to the frequency of its reverse complement on the same strand. Recent studies have shown that, with the exception of organellar DNA, this parity rule generally holds for double stranded DNA genomes and fails to hold for single-stranded genomes. While Chargaff's first parity rule is fully explained by the Watson-Crick pairing in the DNA double helix, a definitive explanation for the second parity rule has not yet been determined. In this work, we propose a model based on a hidden Markov process for approximating the distributional structure of primitive DNA sequences. Then, we use the model to provide another possible theoretical explanation for Chargaff's second parity rule, and to predict novel distributional aspects of bacterial DNA sequences.

q-bio.GN↗