Computational phylogenetics in the context of "Evolutionary tree"

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⭐ Core Definition: Computational phylogenetics

Computational phylogenetics, phylogeny inference, or phylogenetic inference focuses on computational and optimization algorithms, heuristics, and approaches involved in phylogenetic analyses. The goal is to find a phylogenetic tree representing optimal evolutionary ancestry between a set of genes, species, or taxa. Maximum likelihood, parsimony, Bayesian, and minimum evolution are typical optimality criteria used to assess how well a phylogenetic tree topology describes the sequence data. Nearest Neighbour Interchange (NNI), Subtree Prune and Regraft (SPR), and Tree Bisection and Reconnection (TBR), known as tree rearrangements, are deterministic algorithms to search for optimal or the best phylogenetic tree. The space and the landscape of searching for the optimal phylogenetic tree is known as phylogeny search space.

Maximum Likelihood (also likelihood) optimality criterion is the process of finding the tree topology along with its branch lengths that provides the highest probability observing the sequence data, while parsimony optimality criterion is the fewest number of state-evolutionary changes required for a phylogenetic tree to explain the sequence data.

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Computational phylogenetics in the context of Phylogenetic tree

A phylogenetic tree or phylogeny is a graphical representation which shows the evolutionary history between a set of species or taxa during a specific time. In other words, it is a branching diagram or a tree showing the evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics. In evolutionary biology, all life on Earth is theoretically part of a single phylogenetic tree, indicating common ancestry. Phylogenetics is the study of phylogenetic trees. The main challenge is to find a phylogenetic tree representing optimal evolutionary ancestry between a set of species or taxa. Computational phylogenetics (also phylogeny inference) focuses on the algorithms involved in finding optimal phylogenetic tree in the phylogenetic landscape.

Phylogenetic trees may be rooted or unrooted. In a rooted phylogenetic tree, each node with descendants represents the inferred most recent common ancestor of those descendants, and the edge lengths in some trees may be interpreted as time estimates. Each node is called a taxonomic unit. Internal nodes are generally called hypothetical taxonomic units, as they cannot be directly observed. Trees are useful in fields of biology such as bioinformatics, systematics, and phylogenetics. Unrooted trees illustrate only the relatedness of the leaf nodes and do not require the ancestral root to be known or inferred.

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Computational phylogenetics in the context of Phylogenetics

In biology, phylogenetics (/ˌfləˈnɛtɪks, -lə-/) is the study of the evolutionary history of life using observable characteristics of organisms (or genes), which is known as phylogenetic inference. It infers the relationship among organisms based on empirical data and observed heritable traits of DNA sequences, protein amino acid sequences, and morphology. The results are a phylogenetic tree—a diagram depicting the hypothetical relationships among the organisms, reflecting their inferred evolutionary history.

The tips of a phylogenetic tree represent the observed entities, which can be living taxa or fossils. A phylogenetic diagram can be rooted or unrooted. A rooted tree diagram indicates the hypothetical common ancestor of the taxa represented on the tree. An unrooted tree diagram (a network) makes no assumption about directionality of character state transformation, and does not show the origin or "root" of the taxa in question.

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Computational phylogenetics in the context of Cladograms

A cladogram (from Greek κλάδος klados "branch" and γραμμα gramma "character") is a diagram used in cladistics to show evolutionary relations (common descent) between groups of organisms. Cladograms are a type (subset) of phylogenetic trees that do not normally show evolutionary time but are required to meet specific criteria defined by cladistics. Like other evolutionary trees, cladograms can be used show actual, hypothesized, or even hypothetical descent. Modern cladograms are most often generated algorithmically through computational phylogenetics using genetic data, typically from DNA sequencing, as part of a molecular systematics approach.

A cladogram uses lines that branch off in different directions ending at a clade, a group of organisms with a last common ancestor. There are many shapes of cladograms but they all have lines that branch off from other lines. The lines can be traced back to where they branch off. These branching off points represent a hypothetical ancestor (not an actual entity) which can be inferred to exhibit the traits shared among the terminal taxa above it. This hypothetical ancestor might then provide clues about the order of evolution of various features, adaptation, and other evolutionary narratives about ancestors.

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