Dataset Information

A Stochastic Phylogenetic Algorithm for Mitochondrial DNA Analysis.

ABSTRACT: This paper presents an exploratory analysis of the mitochondrial DNA (mtDNA) of 32 species in the subphylum Vertebrata, divided in 7 taxonomic classes. Multiple stochastic parameters, such as the Hurst and detrended fluctuation analysis (DFA) exponents, Shannon entropy, and Chargaff ratio are computed for each DNA sequence. The biological interpretation of these parameters leads to defining a triplet of novel indices. These new functions incorporate the long-range correlations, the probability of occurrence of nucleic bases, and the ratio of pyrimidines-to-purines. Results suggest that relevant regions in mtDNA can be located using the proposed indices. Furthermore, early results from clustering algorithms indicate that the indices introduced might be useful in phylogenetic studies.

SUBMITTER: Corona-Ruiz M

PROVIDER: S-EPMC6418022 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Stochastic Phylogenetic Algorithm for Mitochondrial DNA Analysis.

Corona-Ruiz M M Hernandez-Cabrera Francisco F Cantú-González José Roberto JR González-Amezcua O O Javier Almaguer Francisco F

Frontiers in genetics 20190308

This paper presents an exploratory analysis of the mitochondrial DNA (mtDNA) of 32 species in the subphylum Vertebrata, divided in 7 taxonomic classes. Multiple stochastic parameters, such as the Hurst and detrended fluctuation analysis (DFA) exponents, Shannon entropy, and Chargaff ratio are computed for each DNA sequence. The biological interpretation of these parameters leads to defining a triplet of novel indices. These new functions incorporate the long-range correlations, the probability o ...[more]

PMID: 30906309

Similar Datasets

Project description:Anemonefishes (Pomacentridae Amphiprioninae) are a group of 30 valid coral reef fish species with their phylogenetic relationships still under debate. The eight available mitogenomes of anemonefishes were used to reconstruct the molecular phylogenetic tree; six were obtained from this study (Amphiprion clarkii, A. frenatus, A. percula, A. perideraion, A. polymnus and Premnas biaculeatus) and two from GenBank (A. bicinctus and A. ocellaris). The seven Amphiprion species represent all four subgenera and P. biaculeatus is the only species from Premnas. The eight mitogenomes of anemonefishes encoded 13 protein-coding genes, two rRNA genes, 22 tRNA genes and two main non-coding regions, with the gene arrangement and translation direction basically identical to other typical vertebrate mitogenomes. Among the 13 protein-coding genes, A. ocellaris (AP006017) and A. percula (KJ174497) had the same length in ND5 with 1,866 bp, which were three nucleotides less than the other six anemonefishes. Both structures of ND5, however, could translate to amino acid successfully. Only four mitogenomes had the tandem repeats in D-loop; the tandem repeats were located in downstream after Conserved Sequence Block rather than the upstream and repeated in a simply way. The phylogenetic utility was tested with Bayesian and Maximum Likelihood methods using all 13 protein-coding genes. The results strongly supported that the subfamily Amphiprioninae was monophyletic and P. biaculeatus should be assigned to the genus Amphiprion. Premnas biaculeatus with the percula complex were revealed to be the ancient anemonefish species. The tree forms of ND1, COIII, ND4, Cytb, Cytb+12S rRNA, Cytb+COI and Cytb+COI+12S rRNA were similar to that 13 protein-coding genes, therefore, we suggested that the suitable single mitochondrial gene for phylogenetic analysis of anemonefishes maybe Cytb. Additional mitogenomes of anemonefishes with a combination of nuclear markers will be useful to substantiate these conclusions in future studies.

Dataset Information

A Stochastic Phylogenetic Algorithm for Mitochondrial DNA Analysis.

Publications

A Stochastic Phylogenetic Algorithm for Mitochondrial DNA Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets