I Can’t Sleep - Phylogenetics | Calm Educational Reading for Sleep

Episode Date: January 30, 2025

Unwind with this calm bedtime reading about phylogenetics, created to ease insomnia and bring restful focus. In this soothing episode, Benjamin explores the study of evolutionary relationships among species, explaining how scientists use genetic and morphological data to trace the tree of life. His steady, reassuring narration transforms complex biological concepts into peaceful storytelling, helping you release stress and quiet your thoughts. There is no whispering or hypnosis, only calm, fact-filled narration designed to support relaxation. Press play, close your eyes, and let the story of phylogenetics carry you into gentle slumber. Want More? Request a Topic: https://www.icantsleeppodcast.com/request-a-topic Ad-Free Episodes: https://icantsleep.supportingcast.fm/ Shop Sleep-Friendly Products: https://www.icantsleeppodcast.com/sponsors Join the Discussion on Discord: https://discord.gg/myhGhVUhn7 This content is derived from the Wikipedia article on Phylogenetics, available under the Creative Commons Attribution-ShareAlike (CC BY-SA) license. Read the full article: Wikipedia – Phylogenetics. Happy sleeping! Learn more about your ad choices. Visit megaphone.fm/adchoices

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I also speak to world-class experts like Dr. Nicole Kane, who shares how to permanently heal anxiety by addressing the root cause. With over 200 episodes in our catalog, this podcast is for you if you're ready to crush self-doubt, conquer challenges, and become stronger than ever with therapist-approved strategies that can change your life. Listen to Mentally Stronger with Therapist Amy Morin, wherever you get your podcasts. Welcome to the I Can't Sleep Podcast, where I read random articles from across the web to bore you to sleep with my soothing voice. I'm your host, Benjamin Boster, and today's episode is from a Wikipedia article titled Philogenetics. In bioengineer, in bio,

Phylogy, phylogenetics is the study of the evolutionary history of life using genetics, which is known as phylogenetic inference. It establishes the relationship between organisms with the empirical data and observed heritable traits of DNA sequences, protein amino acid sequences, and morphology. The results are a phylogenetic tree, a diagram setting the hypergeneatic, hypothetical relationships between organisms and their evolutionary history. The tips of a phylogenetic tree can be living taxa or fossils, which represent the present time or end of an evolutionary lineage, respectively. A phylogenetic diagram can be rooted or unrooted.

A rooted tree diagram indicates the hypothetical common ancestor of the tree. An unrooted tree diagram, a network makes no assumption about the ancestral line and does not show the origin or root of the taxa in question or the direction of inferred evolutionary transformations. In addition to their use for inferring phylogenetic patterns among taxa, phylogenetic analyses are often employed to represent relationships among genes or individual organisms. Such uses have become central to understanding biodiversity, evolution, ecology, and genomes. Philogenetics is a component of systematics that uses similarities and differences of the characteristics of species

to interpret their evolutionary relationships and origins. It focuses on whether the characteristics of a species reinforce a phylogenetic inference that it diverged from, the most recent common ancestor of a taxonomic group. In the field of cancer research, phylogenetics can be used to study the clonal evolution of tumors and molecular chronology, predicting and showing how cell populations varies throughout the progression of the disease and during treatment, using whole genome sequencing techniques. The evolutionary processes behind cancer progression are quite different from the

those in most species and are important to phylogenetic inference. These differences manifest in several areas, the types of aberrations that occur, the rates of mutation, the high heterogeneity, variability of tumor cells subclones, and the absence of genetic recombination. Philogenetics can also aid in drug design and discovery. It allows scientists to organize species, and can show which species are likely to have inherited particular traits that are medically useful, such as producing biologically active compounds, those that have effects on the human body. For example, in drug discovery, venom-producing animals are particularly useful. Venoms from these animals produce several important drugs, e.g. ACE inhibitors and pre-alt zaconotide.

To find new venom, scientists turn to phylogenetics to screen for closely related species that may have the same useful traits. The phylogenetic tree shows which species of fish have an origin of venom and related fish that may contain the trait. Using this approach in studying venomous fish, biologists are able to identify the fish species that may be venomous. Biologists have used this approach in many species, such as snakes and lizards. In forensic science, phylogenetic tools are useful to assess DNA evidence for court cases. The simple phylogenetic tree of viruses A through E shows the relationship between viruses, e.g. all viruses are descendants of virus A. Taxonomy is the identification, naming, and classification of organisms.

Compared to systemization, classification emphasizes whether a species has characteristics of a taxonomic group. The Linnaean classification system developed in the 1700s by Carolus Linnaeus is the foundation for modern classification methods. This classification relies on an organism's phenotype or physical characteristics to group and organize species. With the emergence of biochemistry, organism classifications are now usually based on phylogenetic data, and many systematists contend that only monophyletic taxis should be recognized as named groups. The degree to which classification depends on inferred evolutionary history differs depending on the school of taxonomy.

Phonetics ignores phylogenetic speculation altogether, trying to represent the similarity between organisms instead. Cladistics, phylogenetic, systematics, tries to reflect phylogeny in its classifications by only recognizing groups based on shared derived characteristics. Evolutionary taxonomy tries to take into account both the branching pattern and degree of difference to find a compromise between them. Usual methods of phylogenetic inference involve computational approaches,

implementing the optimality criteria and methods of parsimony, maximum likelihood, ML, and MCMC-based Bayesian inference. All these depend upon an implicit or explicit mathematical model, describing the evolution of characters observed. Phonetics, popular in the mid-20th century but now largely obsolete, used distance matrix-based methods to construct trees based on overall similarity in morphology or similar observable traits, i.e. in the phenotype or the overall similarity of DNA,

not the DNA sequence, which was often assumed to approximate phylogenetic relationships, Prior to 1950, phylogenetic inferences were generally presented as narrative scenarios. Such methods are often ambiguous and lack explicit criteria for evaluating alternative hypotheses. In phylogenetic analysis, taxon sampling selects a small group of taxa to represent the evolutionary history of its broader population. This process is also known as stratified semantics. sampling or clade-based sampling. The practice occurs given limited resources to compare and analyze every species within a target population.

Based on the representative group selected, the construction and accuracy of phylogenetic trees vary, which impacts derived phylogenetic inferences. Unavailable data sets, such as an organism's incomplete DNA and protein. protein amino acid sequences and genome databases directly restrict taxonomic sampling. Consequently, a significant source of error within phylogenetic analysis occurs due to inadequate taxon samples. Accuracy may be improved by increasing the number of genetic samples within its monophyletic group. Conversely, increasing sampling from outgroups extraneous to the target stratophiles, population may decrease accuracy. Long branch attraction is an attributed theory for this

occurrence, where non-related branches are incorrectly classified together, insinuating a shared evolutionary history. There are debates if increasing the number of taxa-sampled improves phylogenetic accuracy more than increasing the number of genes sampled per taxon. Differences in each method sampling impacts a number of nucleotide sites utilized in a sequence alignment, which may contribute to disagreements. For example, phylogenetic trees constructed utilizing a more significant number of total nucleotides are generally more accurate, as supported by phylogenetic trees bootstrapping replicability from random sampling.

The graphic presented in taxon sampling bioinformatics and phylogenomics compares the correctness of phylogenetic trees generated using fewer taxa and more sites per taxon on the x-axis to more taxa and fewer sites per taxon on the y-axis. With fewer taxa, more genes are sampled amongst the taxonomic group, in comparison with more taxa. taxa added to the taxonomic sampling group, fewer genes are sampled. Each method has the same total number of nucleotide sites sampled. Furthermore, the dotted line represents a one-to-one accuracy between the two sampling methods. As seen in the graphic, most of the plotted points are located below the dotted line, which indicates gravitation toward increased accuracy when sampling fewer taxa was more sites per taxon. The research performed utilizes four different phylogenetic tree construction models to verify the theory.

Neighbor joining, N.J., minimum evolution, M.E., unweighted maximum parsimony, MP, and maximum likelihood, ML. In the majority of models, sampling fewer taxon with more sites per taxon demonstrated higher accuracy. Generally, with the alignment of a relatively equal number of total nucleotide sites, sampling more genes per taxon has higher bootstrapping replicability than sampling more taxa. However, unbalanced datasets data sets within genomic databases make increasing the gene comparison per taxon in uncommonly sampled organisms increasingly difficult. The term phylogeny derives from the germ phylogenia, introduced by Heckel in 1866, and the Darwinian approach to classification became known as the philetic approach.

It can be traced back to Aristotle, who wrote in his posterior analytics, we may assume the superiority satires paribus, other things being equal, of the demonstration which derives from fewer postulates or hypotheses. The modern concept of phylogenetics evolved primarily as a disprove, of a previously widely accepted theory. During the late 19th century, Ernst Haeckel's recapitulation theory, or biogenic fundamental law, was widely popular. It was often expressed as hauntagony recapitulates philogyny,

i.e., the development of a single organism during its lifetime, from germ to adult, successively mirrors the adult stages of successive ancestors of the species to which it belongs. But this theory has long been rejected. Instead, ontogeny evolves. The phylogenetic history of a species cannot be read directly from its ontogeny, as Heckel thought would be possible. The characters from the ontogeny can be and have been used as data for phylogenetic analyses. The more closely related to species are, the more apomorphies their embryos share. Timeline of key points.

14th century, Lex Parsonomone, Parcimony principle, William of Ackham, English philosopher, theologian, and Franciscan friar, but the idea actually goes back to Aristotle as a precursor concept. He introduced the concept of Ackham's razor, which is the problem-solving principles that recommend searching for explanations constructed with the smallest possible set of elements. Though he did not use these exact words, the principle can be summarized as entities must not be multiplied beyond necessity. The principle advocates that when presented with competing hypotheses about the same prediction, one should prefer the one that requires fewest assumptions.

1763 Bayesian Probability, Reverend Thomas Bays, a precursor concept. Bayesian probability began a resurgence in the 1950s, allowing scientists in the computing field to pair traditional Bayesian statistics with other more modern techniques. It is now used as a blanket term for several related interpretations of probability as an amount of epistemic confidence. 18th century, Pierre Simone, Marquis de Laplace, perhaps first to use ML, maximum likelihood, precursor concept.

His work gave way to the Laplace distribution, which can be directly linked to least absolute deviations. 1809, evolutionary theory, philosophy zoologic, Jean-Baptiste de Le Mac. Precursor concept, foreshelieu. Inshadowed in the 17th century and 18th century by Voltaire, Descartes, and Leibniz. With Leipnis even proposing evolutionary changes to account for observed gaps, suggesting that many species had become extinct, others transformed, and different species that share common trades, may have at one time been a single race.

Also foreshadowed by some early Greek philosophers, such as Anna Zamander in the 6th century BC and the atomists of the 5th century BC, who proposed rudimentary theories of evolution. 1837, Darwin's notebooks show an evolutionary tree. 1840, American geologist Edward Hitchcock published what is considered to be the first paleontological tree of life. many critiques, modifications, and explanations would follow. 1843

Distinction between homology and analogy. The latter now referred to as homoplazy. Richard Owen, precursor concept. Homology is a term used to characterize the similarity of features that can be parsimoniously explained by common ancestry. Homoplasy is the term used to describe a feature that has been gained or lost independently in separate lineages over the course of evolution. 1858. Paleontologist Heinrich Georg Braun

1862, published a hypothetical tree to illustrating the paleontological arrival of new similar species following the extinction of an older species. Braun did not propose a mechanism responsible for such phenomena. Precursor concept. 1858 Elaboration of Evolutionary Theory Darwin and Wallace Also in Origin of Species by Darwin the following year Precursor Concept

1868 Ernst Heckel First publishes his philogynet based evolutionary tree Precursor Concept Heckel introduces the now-improved Recapitulation Theory He introduced the term Gladys as a taxonomic category just below subphylum.

1893. Dolo's Law of Character-State Irreversibility Precursor Concept Dolos law of irreversibility states that an organism never comes back exactly to its previous state due to the indestructible nature of the past. It always retains some trace

of the transitional stages through which it has passed. 1912, ML Maximum Likelihood Recommended, Analyzed, and Popularized by Ronald Fisher, Precursor Concept Fisher is one of the main contributors to the early 20th century revival of Darwinism and has been called the greatest of Darwin's successors for his contributions to the revision of the theory of evolution and his use of mathematics to combine Mendelian genetics and natural selection in the 20th century, modern synthesis. 1921. Tilliard uses term phylogenetic and distinguishes between archaic and specialized characters

in his classification system. 1940. Lucien Quino coined the term clade in 1940. He uses it for evolutionary branching. 1947. Bernhardt-Ranch introduced the term cladogenesis, in his German book, Evolution Above the Species Level. 1949.

Jack Knife Res. Canui. foreshadowed in 46 by Mahalanabas and extended in 58 by Tuke, precursor concept. 1950. Willie Hennegg's classic formalization. Henning is considered the founder of phylogenetic systematics and published his first works in German of this year. He also asserted a version of the parsimony principle, stating that the presence of amorphous characters in different species is always reason for suspecting kinship, and that their origin

by convergence should not be presumed a priori. This has been considered a foundation view of phylogenetic inference. 1952 William Wagner's ground-planned divergence method 1957 Julian Huxley adopted Wrench's terminology as cladogenesis with a full definition Cladogenesis I have taken over directly from wrench

to denote all splitting from subspeciation through adaptive radiation to the divergence of phyla and kingdoms With it, he introduced the word clades, defining it as cladogenesis results in the formation of delimitable monophyletic units, which may be called clades. 1960. Arthur Kane and Jeffrey Ainsworth Harrison coined cladistic to mean evolutionary relationship. 1963.

First attempt to use ML maximum likelihood for phylogenetics. Edwards and Cavallis Forza 1965 Camon Sokol parsimony First parsimony Optimization Criterion and First Computer Programme Algorithm for cladistic analysis

Both by Camin and Sokol Character compatibility method Also called click analysis Introduced independently by Camin and Sokol And E.O. Wilson 166. English translation of Henneg. Cladistics and cladogram coined. 1969.

Dynamic and successive waiting, James Ferris. Wagner Parsimony, Cluge and Ferris. C.I. Consistency Index. Cluge and Ferris. Introduction of Pairways Compatibility for Click Analysis, Luchesne. 1970, Wagner parsimony generalized by Ferris. 1971. First successful application of ML maximum likelihood to phylogenetics for protein sequences,

Nyman. Fitch Parsimony, Walter M. Fitch. These gave way to the most basics of maximum parsimony. Fitch is known for his work on reconstructing phylogenetic trees from protein and DNA. sequences. His definition of orsologous sequences has been referenced in many research publications. NNI Nearest Neighbor Interchange First Branch Swapping Search Strategy, developed independently by Robinson and Moore at all. M.E. Minimum Evolution

Kidd ends Garmela Zonta. It is unclear if this is the pairwise distance method. or related to ML as Edwards and Cavalys Ford so-called ML Minimum Evolution. 1972, Adams Consensus, Adams 1976, prefix system for ranks, Ferris 1977, Dolo Parcimony, Ferris 1979, Nelson Consensus, Nelson MAST, Maximum Agreement Subtree, Gas, Greatest Agreement Subtree. A Consensus Method, Gordon.

Bootstrap, Bradley Ephron, Precursor Concept 1980, Philip, first software package for phylogenetic analysis, Joseph Felsenstein, a free computational phylogenetics package of programs for inferring evolutionary trees, phylogenes. One such example tree created by Philip called A. Drawgram generates rooted trees. 1981. Majority Consensus.

Margish and McMorris. Strict consensus. Sokol and Rolf. First computational efficiency ML Maximum Likelihood Algorithm. Felsenstein created the the Felsenstein Maximum Likelihood Method used for the inference of philogyny which evaluates a hypothesis about evolutionary history

in terms of the probability that the proposed model and the hypothesized history would give rise to the observed data set. 1982 Fysis Mikovic and Ferris Branch and Bound Hendi and Penny 1985

First cladistic analysis of eukaryotes based Based on combined phenotypic and genotypic evidence, Diana Lipscomb. First issue of cladistics. First phylogenetic application of bootstrap, Felsenstein. First phylogenetic application of Jackknife, Scott Lanyon. 1986, McLeod, Madison, and Madison. 1987. Neighbor- Joining Joining Method

Say to and nay 1988 Henig 86 version 1.5 Ferris Bremer Support Decay Index Bremer

1989 RI retention index RCI Reescaled Consistency Index Ferris Her Homoplae Z excess ratio Archie

1990 Combinable components, semi-strict consensus, Bremer SPR, Subtree Pruning and Regrafting, TBR, Tree Bissection and Reconnection, Swilford and Olson. 1991, DDI, DETEDD Decisiveness Index, Goloboff First Gladistic Analysis of your caryotes based only on phenotypic evidence, Lipscomb. 1993 Implied Waiting Golobov

1994 reduced consensus RCC reduced cladistic consensus for rooted trees Wilkinson 1995 Reduced Consensus RPC Reduced Partition Consensus

for Unrooted Trees Wilkinson 1996 First Working Methods for or B.I. Bayesian inference, independently developed by Lee, Mao, and Ranala and Yang, and all using MCMC, Markov-Chain-Montecarlo. 1998, TNT. Tree analysis using new technology.

Golobov, Ferris, and Nixon. 1999. Winclada, Nixon. 2003, Symmetrical Resampling, Golobov. 2004-2005 similarity metric, using an approximation of Kolmogorov complexity, or NCD, normalized compression distance, Lee at all, Kilbrazi and Vitany.