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Bacteria
Published in Julius P. Kreier, Infection, Resistance, and Immunity, 2022
The need for intelligent communication about the large number of vastly diverse organisms has prompted biologists to develop systems of classification. Taxonomy is the classification (or grouping) of organisms according to their natural relationships. Hence, a taxon (plural taxa) is a category. The study of natural relationships or evolutionary history of organisms is called phylogeny; i.e., a taxonomic grouping based on heritable or evolutionary relationships. This system of taxonomy leads to construction of a phylogenetic tree, which emphasizes branching away from common ancestors due to heritable differences. It does not usually consider adaptations as differences between descendants of a common ancestor.
Methods for Outbreaks Using Genomic Data
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
Don Klinkenberg, Caroline Colijn, Xavier Didelot
A NJ tree is a binary tree built with the NJ algorithm, which iteratively connects clusters (which are single samples or groups of samples connected in previous steps) by selecting the pair of least distant clusters, until all samples are connected. If possible, this algorithm returns the tree where all distances between pairs of leaves are equal to the distances in the distance matrix, which is the case with the FMD outbreak data (Figures 13.2b and c). NJ trees are unrooted, but they can be used as the basis to make (rooted) phylogenies, where the direction of evolution is known for each branch, and the internal nodes represent ancestors. The root can be defined as the midpoint of the tree, but more often it is defined by including samples known to be well separated from the sequences under study, called outgroups. Figure 13.2c shows such a tree for the FMD outbreak, where four reference genomes that are not part of the outbreak were used to root the tree. NJ trees are examples of phylogenetic trees which describe the inferred evolutionary relationships between the samples (or taxa). There are many more approaches to infer phylogenetic trees, for example UPGMA, maximum parsimony, maximum likelihood, and various Bayesian approaches [1, 3].
The Role of the Computer in Estimates of DNA Nucleotide Sequence Divergence
Published in S. K. Dutta, DNA Systematics, 2019
Relatedness between species has long been analyzed through the construction of phylogenetic trees, based on morphological or other biological parameters. The possibility of more in-depth analysis through the use of protein sequence data quickly became apparent when these data became available. The basic principles behind such molecular analysis apply to the use of nucleotide sequences in the development of phylogenetic trees, although the details of the algorithms employed must be modified to make use of such data.
Testing for genetic mutation of seasonal influenza virus
Published in Journal of Applied Statistics, 2023
Phylogeny is concerned with the evolution of groups and specifically about the lines of descent and relationships among groups. It is one of the best tools for understanding the evolution of pathogens. A phylogenetic tree is a diagram depicting a phylogeny through lines of evolutionary descent from a common ancestor. Throughout this article, the phrase ‘phylogenetic tree’ and ‘evolutionary tree’ are used interchangeably. Influenza viruses are permanently changing, undergoing genetic changes over time and monitoring these changes in the genome is fundamental to the production of vaccines on a seasonal basis. The RNA genes of influenza are made up of nucleotides. It is the composition of these nucleotides and the differences which account for the different viruses. The differences and ancestry of viruses are demonstrated through the use of a phylogenetic tree. The tree shows how different viruses are related to each other and are grouped together based on how close their corresponding nucleotides are. Specifically the phylogenetic trees of influenza viruses will usually display how similar the viruses hemagglutinin (HA) or neuraminidase (NA) genes are to one another. The tree consists of branches and branch lengths. Groups on the same branch share the same nucleotides. At a split of a branch, the length of the branch indicates how different (i.e. the number of nucleotide differences) from each other the groups are.
Identification, characterization, and molecular phylogeny of scorpion enolase (Androctonus crassicauda and Hemiscorpius lepturus)
Published in Toxin Reviews, 2023
Elham Pondehnezhadan, Atefeh Chamani, Fatemeh Salabi, Reihaneh Soleimani
The evolutionary origin and relevance of scorpions have been the subject of many studies in recent decades, especially recent pan-genome studies that support the Arachnopulmonata hypothesis: a sister-group relationship between scorpions and tetrapulmonates (i.e. spiders and allied orders) (Regier et al. 2010, Sharma et al. 2014, 2015). Previous morphological-based phylogenetic approaches are restricted to those scorpions exemplifying morphological stasis (Prendini and Wheeler 2005, Prendini et al. 2006). To address this challenge, however, phylogenetic relationships can be inferred more precisely using both molecular and morphological methods (Chippaux and Goyffon 2008). Molecular phylogeny compares homologous DNA or protein sequences to decide the relationships among organisms or genes. It constructs a hierarchical phylogenetic tree based on the genetic divergences of the similarity or dissimilarity of homologous molecules from different organisms, resulting from molecular evolution over time (Patwardhan et al. 2014).
Variational Bayesian inference for association over phylogenetic trees for microorganisms
Published in Journal of Applied Statistics, 2022
Xiaojuan Hao, Kent M. Eskridge, Dong Wang
The data were generated in conjunction with phylogenetic trees, so we first simulated a collection of phylogenetic trees resembling those from the two actual data sets. Branch lengths t were resampled from the phylogenetic trees from the actual data sets based on DNA or RNA sequences. We assumed two state levels, 0 or 1, for each node of the tree. The state of the root was determined by a Bernoulli distribution with the probability i and node j with branch length t is Z for each node from a Bernoulli distribution, then observations for the variable Y were generated at the tip nodes of the tree using a Bernoulli distribution with probability