Clustering mtDNA sequences for human evolution studies

A novel distance method for sequence classification and intraspecie phylogeny reconstruction is proposed. The method incorporates biologically motivated definitions of DNA sequence distance in the recently proposed Chaotic Map Clustering algorithm (CMC) which performs a hierarchical partition of data by exploiting the cooperative behavior of an inhomogeneous lattice of chaotic maps living in the space of data. Simulation results show that our method outperforms, on average, the simple and most widely used approach to intra specie phylogeny reconstruction based on the Neighbor Joining (NJ) algorithm. The method has been tested on real data too, by applying it to two distinct datasets of human mtDNA HVRI haplotypes from different geographical origins. A comparison with results from other well known methods such as Stochastic Stationary Markov method and Reduced Median Network has also been performed.

A novel distance method for sequence classification and intraspeie phylogeny reconstruction is proposed. The method incorporates biologically motivated definitions of DNA sequence distance in the recently proposed Chaotic Map Clustering algorithm (CMC) which performs a hierarchical partition of data by exploiting the cooperative behavior of an inhomogeneius lattice of chaotic maps living in the space of data. Simulation results show that our method outperforms, on average, the simply and most widely used approach to intra specie phylogeny reconstruction base on the Neighbor Joining (NJ) algorithm. The method has been tested on real data too, by applying it to two distinct data sets of human mtDNA HVRI haplotypes from different geographical origins. A comparison with results from other well known methods such as Stochastic Stationary Markov method and Reduced Median Network has also been performed.