Is there a comprehensive life science techniques/methods database?

Is there a comprehensive life science techniques/methods database?

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There are so many techniques/methodologies in the life sciences that we can use to interrogate interesting questions. The thing is, most of us are completely unaware of the available methods we can employ. Rather, we go with the techniques we are familiar with or that are popular in our subdomains at the time. But that's pretty limiting.

So I'm wondering… we have databases for everything else… is there one for life sciences techniques/methods? Something like this could be immensely helpful in experimental planning. In particular, I think a comprehensive database would help scientists break outside of their spheres of familiarity and to employ less known (but potentially illuminating) methods to their questions.

I know there are journals that publish protocols and methods, but they are fragmented and don't encompass everything.

Does what I'm looking for exist? If not, how might one go about creating such a tool?

There's Benchfly, which is a video-based protocol library:

There's also JOVE, which is a peer-reviewed video journal that sometimes covers protocols:

There is also protocol online, but without tags, only sorted by category. It could nevertheless be interesting.

My personal favourite is OpenWetWare. Think wikipedia for scientific protocols and an open access lab notebook.

There's a problem with this things. Despite the common stereotype of scientist being open and good at sharing, my experience is the opposite. Many laboratories are not good at all in sharing their techniques/secrets. They will share the basic stuff that you can find online, no problem, but that's about it.

Seems they're afraid that everyone's else after them, trying to scoop them. I have suggested OpenWetWare to some other labs and they refused for that same reason. Even though some will use it to find protocols, they don't see a reason to share back.

you can use something like where you can store and share your protocols with collaborators. If you make your project open, then anyone in the world can view your protocols.

The nice thing about this is you can reference actual experiments that made use of the protocol (and literature) and this gives better context and understanding about the protocols…

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.

Table of Contents

  • Chapter 1Introduction to the Electronic Biological Literature
  • Chapter 3General Sources
  • Chapter 4Abstracts and Indexes
  • Chapter 5Biochemistry and Biophysics
  • Chapter 6Molecular and Cellular Biology
  • Chapter 7Genetics, Biotechnology, and Developmental Biology
  • Chapter 8Microbiology and Immunology
  • Chapter 9Ecology, Evolution, and Animal Behavior
  • Chapter 10Plant Biology
  • Chapter 11Anatomy and Physiology
  • Chapter 12Entomology
  • Chapter 13Zoology

How does the one-humped Arabian camel survive without drinking?

Research led by scientists at the University of Bristol has shed new light on how the kidneys of the one-humped Arabian camel play an important role in helping it to cope with extremes.

In a new paper published today in the journal Communications Biology, they have studied the response of the camel's kidneys to dehydration and rapid rehydration stresses.

Camelus dromedarius is the most important livestock animal in the arid and semi-arid regions of North and East Africa, the Arabian Peninsula and Iran, and continues to provide basic needs to millions of people.

Thought to have been domesticated 3,000 to 6,000 years ago in the Arabian Peninsula, the camel has been used as a beast of burden, for riding and sport, and to produce milk, meat and shelter, and they are still used today for the same purposes.

This animal is so incredibly well adapted to the desert environment that can endure weeks without access to water. A very well-developed kidney is the key to produce highly concentrated urine and assure water is never wasted.

In the current context of advancing desertification and climate change, there is renewed interest in the adaptations of camels. Further, advanced laboratory techniques allow to study the underlying genetic mechanisms of these adaptations.

However, there was not to date, a freely available and comprehensive study of the genes implicated in coping with dehydration in the kidney of the camel.

This project was born in 2015 with the onset of a fruitful collaboration between Professor David Murphy's Lab at University of Bristol and Professor Abdu Adem's Lab at United Arab Emirates University.

The team analysed how thousands of genes changed in the camel kidney as a consequence of dehydration and rehydration and suggested that the amount of cholesterol in the kidney has a role in the water conservation process. They used different techniques to further validate these results.

Lead authors Fernando AlviraI Iraizoz and Benjamin T. Gillard from the University of Bristol's Medical School, said: "A decrease in the amount of cholesterol in the membrane of kidney cells would facilitate the movement of solutes and water across different sections of the kidney -- a process that is required to efficiently reabsorb water and produce a highly concentrated urine, thus avoiding water loss.

"This is, to the best of our knowledge, the first time that the level of cholesterol has been directly associated with water conservation in the kidney. Thus, we describe a novel role for this lipid that may be of interest when studying other species."

The team also presents an immense source of information that, as mentioned by one of the reviewers, is very valuable in the context of climate change and thus will help scientists to understand the mechanisms of water control in dehydration.

Following the publication of this research, the team are now looking at how the camel brain responds to the same stimuli and how other species, such as jerboas and Olive mice, adapt to life in the deserts.

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13. Word walls

Science word walls in classrooms can stimulate the interest of students in the subject and an opportunity for them to illustrate different concepts. In an advanced technique, a more in-depth understanding of different scientific terms can be made possible with pictures that accompany the words.

This is also an option to help them better understand words with multiple meanings. Teachers can design creative word walls or ask students to contribute to the idea weekly or monthly.


Origin and preprocessing of PWM libraries

Base probability matrices from JASPAR Core Vertebrate (2018) and HOCOMOCO Human (v11 FULL) were downloaded in MEME database format from the MEME suite web server. CIS-BP matrices were directly downloaded from the original web site. The CIS-BP collection used in this work contains all matrices directly inferred from experiments with human TFs except those matrices imported from JASPAR and HOCOMOCO. Matrices were manually mapped to gene symbols and TF families from TFclass [29] and CIS-BP. The annotations of matrices (including basic features such as length and GC content) and respective TFs are provided in Additional file 6. The original matrices were slightly modified by adding a correction term of 0.0001 to each matrix element, followed by renormalization of the position-specific probability distributions. This preprocessing step serves to prevent numerical exceptions due to logarithms of 0. Incidentally, we also found that it increases benchmark performance for almost all matrices.

Benchmarking with ChIP-seq peak lists

A special version of ReMap peak lists [25], which included signal enrichment scores, was used in this work and can be downloaded from the MGA data repository [38]. Only peak lists with at least 5000 peaks were used. The following parameters were used for benchmarking: region width w = 250, number of top-ranked peaks: N = 2000, location of negative control sequence relative to peak centers d = + 500.

Benchmarking with HT-SELEX data

Library sequences described in [26] and [27] were downloaded from the European Nucleotide Archive ENA [39] in FASTQ format. The source files contain the DNA sequences of the random inserts without barcodes or primers. FASTQ files were converted into FASTA format. Only sequences exclusively composed of A, C, G, and T and having the length indicated by the library name were retained.

Note that the sequences from the two studies were derived from the same series of experiments. Libraries representing different cycles from the same experiment were pooled. Duplicates were then eliminated from the pooled libraries (assuming that they were PCR copies from the same founder molecules). A random subset of one million sequences was extracted from libraries containing more than a million sequences, in order to reduce benchmarking computing time. As an alternative to input (zero-cycle) sequences, we also generated negative control sequences by mononucleotide shuffling of the inserts but leaving the primer and barcode sequences unchanged (see below).

We extended the random insert sequences provided in the source files with sequences that were physically present during the binding experiments. According to [26], the 5′ and 3′ flanking primer sequences were as follows:

The barcodes vary from experiment to experiment. After flanking with barcode and primers on both sides, sequences were truncated to include only the 20 bp adjacent to the random insert on each flank. For instance, random insert sequences from experiment ELK3_TCGGGG20NGGT_AG (barcodes TCCGGGG and GGT, as indicated by the name) were extended in the following way:

As the source files containing the ReMap peak lists and SELEX libraries have informative and intuitively understandable names, they were thus used as experiment identifiers in this article. The TF gene symbols assigned to each experiment were also extracted from the corresponding filenames.

Benchmarking with PBM data

Protein-binding microarray (PBM) data for human and mouse were downloaded from UniPROBE (Hume et al. [12]) as “Normalized Probe Data.” The files for each experiment contain one column with normalized intensities per probe and the actual probe sequence including a fixed linker sequence. To obtain correlation values for an input PWM (obtained from JASPAR, HOCOMOCO, or CIS-BP and processed as described previously) with respect to measured log intensities, the following procedure was adhered to: For each probe sequence, the first 41 base pairs were extracted, excluding part of the fixed linker sequence. Within this sequence, the PWM was applied in a sliding window approach to obtain per-position probabilities, which were aggregated into a log sum occupancy score, i.e., the log of the sum of these probabilities per probe sequence. Finally, the Pearson correlation coefficient between the log sum occupancy scores and log intensities was reported as correlation value for a pair of PWM and PBM experiment.

Benchmarking protocol availability

The ChIP-seq and HT-SELEX-based benchmarking protocols are publicly available via a web interface at The ChIP-seq, HT-SELEX-based, and PBM-based protocols are available as docker images from [40].

Aggregate rank score and identification of the best performing matrix for a gene

All rows corresponding to a given gene were extracted from a complete table containing performance measures (ROC AUC or correlation coefficients) for combinations of experiments (rows) and TF motif matrices (columns). The numbers in a given row were first converted into ranks. The overall performance of a matrix for multiple experiments for the same gene was then computed as the geometric mean over the ranks. This score is referred to as the “aggregate rank score” elsewhere in the text.

Motif clustering

For motif clustering and selection of representative motifs, we first constructed a distance matrix between all pairs of human TFs in HOCOMOCO and JASPAR, using MacroAPE [41] to calculate Jaccard distances between sets of words recognized by motifs. This distance matrix was used to make a hierarchical tree using UPGMA (unweighted pair group method with arithmetic mean). Motif aggregation halted when the distance between merging clusters reached 0.95 or more. As a result, we came up with 225 clusters. From each cluster, one representative motif was taken by minimizing its average distance to other motifs in the cluster.

T-SNE analysis

The analysis was performed using the sklearn t-SNE implementation [42] with PCA initialization. Cosine similarity was used as a distance parameter. The perplexity parameter was set as 25 for ChIP-seq and HT-SELEX benchmarks and 55 for the PBM benchmark.

Analysis of the association between the basic motif features and the performance values

Each point underlying the density plot corresponds to a single PWM of a particular TF. To normalize for different numbers of data sets per TF, the AUC ROC (for ChIP-seq and SELEX) and correlation (for PBM) values were calculated by averaging the corresponding values over all data sets for the TF corresponding to the PWM under consideration. The Python seaborn package was used for visualization.

Strengths and Weaknesses of the Sequential Designs

In both of the sequential models described above (exploratory and explanatory), the data collection and analysis proceeded in two distinct phases. As illustrated by the examples from the BER literature, the main strengths of the sequential designs include the ability to 1) contextualize and generalize qualitative findings to larger samples (in the case of sequential exploratory) 2) enable one to gain a deeper understanding of findings revealed by quantitative studies (in the case of sequential explanatory) and 3) collect and analyze the different methods separately. Additionally, the two-phase approach makes sequential designs easy to implement, describe, and report.

One weakness of sequential designs is the length of time required to complete both data-collection phases, especially given that the second phase is often in response to the results of the first phase. That is, by collecting the data at two different time points, one essentially doubles the length of time required to complete a single-method study. Moreover, because data collection is sequential, it may be difficult to decide when to proceed to the next phase. It may also be difficult to integrate or connect the findings of the two phases. For those projects with shorter time lengths, concurrent designs in which both data sets are collected in a single phase may be more appropriate. The next section of the paper provides details of concurrent designs of MMR.

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The exact definition of taxonomy varies from source to source, but the core of the discipline remains: the conception, naming, and classification of groups of organisms. [1] As points of reference, recent definitions of taxonomy are presented below:

  1. Theory and practice of grouping individuals into species, arranging species into larger groups, and giving those groups names, thus producing a classification. [2]
  2. A field of science (and major component of systematics) that encompasses description, identification, nomenclature, and classification [3]
  3. The science of classification, in biology the arrangement of organisms into a classification [4]
  4. "The science of classification as applied to living organisms, including study of means of formation of species, etc." [5]
  5. "The analysis of an organism's characteristics for the purpose of classification" [6]
  6. "Systematics studies phylogeny to provide a pattern that can be translated into the classification and names of the more inclusive field of taxonomy" (listed as a desirable but unusual definition) [7]

The varied definitions either place taxonomy as a sub-area of systematics (definition 2), invert that relationship (definition 6), or appear to consider the two terms synonymous. There is some disagreement as to whether biological nomenclature is considered a part of taxonomy (definitions 1 and 2), or a part of systematics outside taxonomy. [8] For example, definition 6 is paired with the following definition of systematics that places nomenclature outside taxonomy: [6]

  • Systematics: "The study of the identification, taxonomy, and nomenclature of organisms, including the classification of living things with regard to their natural relationships and the study of variation and the evolution of taxa".

A whole set of terms including taxonomy, systematic biology, systematics, biosystematics, scientific classification, biological classification, and phylogenetics have at times had overlapping meanings – sometimes the same, sometimes slightly different, but always related and intersecting. [1] [9] The broadest meaning of "taxonomy" is used here. The term itself was introduced in 1813 by de Candolle, in his Théorie élémentaire de la botanique. [10]

Monograph and taxonomic revision Edit

A taxonomic revision or taxonomic review is a novel analysis of the variation patterns in a particular taxon. This analysis may be executed on the basis of any combination of the various available kinds of characters, such as morphological, anatomical, palynological, biochemical and genetic. A monograph or complete revision is a revision that is comprehensive for a taxon for the information given at a particular time, and for the entire world. Other (partial) revisions may be restricted in the sense that they may only use some of the available character sets or have a limited spatial scope. A revision results in a conformation of or new insights in the relationships between the subtaxa within the taxon under study, which may result in a change in the classification of these subtaxa, the identification of new subtaxa, or the merger of previous subtaxa. [11]

Alpha and beta taxonomy Edit

The term "alpha taxonomy" is primarily used today to refer to the discipline of finding, describing, and naming taxa, particularly species. [12] In earlier literature, the term had a different meaning, referring to morphological taxonomy, and the products of research through the end of the 19th century. [13]

William Bertram Turrill introduced the term "alpha taxonomy" in a series of papers published in 1935 and 1937 in which he discussed the philosophy and possible future directions of the discipline of taxonomy. [14]

. there is an increasing desire amongst taxonomists to consider their problems from wider viewpoints, to investigate the possibilities of closer co-operation with their cytological, ecological and genetics colleagues and to acknowledge that some revision or expansion, perhaps of a drastic nature, of their aims and methods, may be desirable . Turrill (1935) has suggested that while accepting the older invaluable taxonomy, based on structure, and conveniently designated "alpha", it is possible to glimpse a far-distant taxonomy built upon as wide a basis of morphological and physiological facts as possible, and one in which "place is found for all observational and experimental data relating, even if indirectly, to the constitution, subdivision, origin, and behaviour of species and other taxonomic groups". Ideals can, it may be said, never be completely realized. They have, however, a great value of acting as permanent stimulants, and if we have some, even vague, ideal of an "omega" taxonomy we may progress a little way down the Greek alphabet. Some of us please ourselves by thinking we are now groping in a "beta" taxonomy. [14]

Turrill thus explicitly excludes from alpha taxonomy various areas of study that he includes within taxonomy as a whole, such as ecology, physiology, genetics, and cytology. He further excludes phylogenetic reconstruction from alpha taxonomy (pp. 365–366).

Later authors have used the term in a different sense, to mean the delimitation of species (not subspecies or taxa of other ranks), using whatever investigative techniques are available, and including sophisticated computational or laboratory techniques. [15] [12] Thus, Ernst Mayr in 1968 defined "beta taxonomy" as the classification of ranks higher than species. [16]

An understanding of the biological meaning of variation and of the evolutionary origin of groups of related species is even more important for the second stage of taxonomic activity, the sorting of species into groups of relatives ("taxa") and their arrangement in a hierarchy of higher categories. This activity is what the term classification denotes it is also referred to as "beta taxonomy".

Microtaxonomy and macrotaxonomy Edit

How species should be defined in a particular group of organisms gives rise to practical and theoretical problems that are referred to as the species problem. The scientific work of deciding how to define species has been called microtaxonomy. [17] [18] [12] By extension, macrotaxonomy is the study of groups at the higher taxonomic ranks subgenus and above. [12]

While some descriptions of taxonomic history attempt to date taxonomy to ancient civilizations, a truly scientific attempt to classify organisms did not occur until the 18th century. Earlier works were primarily descriptive and focused on plants that were useful in agriculture or medicine. There are a number of stages in this scientific thinking. Early taxonomy was based on arbitrary criteria, the so-called "artificial systems", including Linnaeus's system of sexual classification for plants (Of course, Linnaeus's classification of animals was entitled "Systema Naturae" ("the System of Nature"), implying that he, at least, believed that it was more than an "artificial system"). Later came systems based on a more complete consideration of the characteristics of taxa, referred to as "natural systems", such as those of de Jussieu (1789), de Candolle (1813) and Bentham and Hooker (1862–1863). These classifications described empirical patterns and were pre-evolutionary in thinking. The publication of Charles Darwin's On the Origin of Species (1859) led to a new explanation for classifications, based on evolutionary relationships. This was the concept of phyletic systems, from 1883 onwards. This approach was typified by those of Eichler (1883) and Engler (1886–1892). The advent of cladistic methodology in the 1970s led to classifications based on the sole criterion of monophyly, supported by the presence of synapomorphies. Since then, the evidentiary basis has been expanded with data from molecular genetics that for the most part complements traditional morphology. [19] [ page needed ] [20] [ page needed ] [21] [ page needed ]

Pre-Linnaean Edit

Early taxonomists Edit

Naming and classifying our surroundings has probably been taking place as long as mankind has been able to communicate. It would always have been important to know the names of poisonous and edible plants and animals in order to communicate this information to other members of the family or group. Medicinal plant illustrations show up in Egyptian wall paintings from c. 1500 BC, indicating that the uses of different species were understood and that a basic taxonomy was in place. [22]

Ancient times Edit

Organisms were first classified by Aristotle (Greece, 384–322 BC) during his stay on the Island of Lesbos. [23] [24] [25] He classified beings by their parts, or in modern terms attributes, such as having live birth, having four legs, laying eggs, having blood, or being warm-bodied. [26] He divided all living things into two groups: plants and animals. [24] Some of his groups of animals, such as Anhaima (animals without blood, translated as invertebrates) and Enhaima (animals with blood, roughly the vertebrates), as well as groups like the sharks and cetaceans, are still commonly used today. [27] His student Theophrastus (Greece, 370–285 BC) carried on this tradition, mentioning some 500 plants and their uses in his Historia Plantarum. Again, several plant groups currently still recognized can be traced back to Theophrastus, such as Cornus, Crocus, and Narcissus. [24]

Medieval Edit

Taxonomy in the Middle Ages was largely based on the Aristotelian system, [26] with additions concerning the philosophical and existential order of creatures. This included concepts such as the Great chain of being in the Western scholastic tradition, [26] again deriving ultimately from Aristotle. The aristotelian system did not classify plants or fungi, due to the lack of microscopes at the time, [25] as his ideas were based on arranging the complete world in a single continuum, as per the scala naturae (the Natural Ladder). [24] This, as well, was taken into consideration in the Great chain of being. [24] Advances were made by scholars such as Procopius, Timotheos of Gaza, Demetrios Pepagomenos, and Thomas Aquinas. Medieval thinkers used abstract philosophical and logical categorizations more suited to abstract philosophy than to pragmatic taxonomy. [24]

Renaissance and Early Modern Edit

During the Renaissance and the Age of Enlightenment, categorizing organisms became more prevalent, [24] and taxonomic works became ambitious enough to replace the ancient texts. This is sometimes credited to the development of sophisticated optical lenses, which allowed the morphology of organisms to be studied in much greater detail. One of the earliest authors to take advantage of this leap in technology was the Italian physician Andrea Cesalpino (1519–1603), who has been called "the first taxonomist". [28] His magnum opus De Plantis came out in 1583, and described more than 1500 plant species. [29] [30] Two large plant families that he first recognized are still in use today: the Asteraceae and Brassicaceae. [31] Then in the 17th century John Ray (England, 1627–1705) wrote many important taxonomic works. [25] Arguably his greatest accomplishment was Methodus Plantarum Nova (1682), [32] in which he published details of over 18,000 plant species. At the time, his classifications were perhaps the most complex yet produced by any taxonomist, as he based his taxa on many combined characters. The next major taxonomic works were produced by Joseph Pitton de Tournefort (France, 1656–1708). [33] His work from 1700, Institutiones Rei Herbariae, included more than 9000 species in 698 genera, which directly influenced Linnaeus, as it was the text he used as a young student. [22]

The Linnaean era Edit

The Swedish botanist Carl Linnaeus (1707–1778) [26] ushered in a new era of taxonomy. With his major works Systema Naturae 1st Edition in 1735, [34] Species Plantarum in 1753, [35] and Systema Naturae 10th Edition, [36] he revolutionized modern taxonomy. His works implemented a standardized binomial naming system for animal and plant species, [37] which proved to be an elegant solution to a chaotic and disorganized taxonomic literature. He not only introduced the standard of class, order, genus, and species, but also made it possible to identify plants and animals from his book, by using the smaller parts of the flower. [37] Thus the Linnaean system was born, and is still used in essentially the same way today as it was in the 18th century. [37] Currently, plant and animal taxonomists regard Linnaeus' work as the "starting point" for valid names (at 1753 and 1758 respectively). [38] Names published before these dates are referred to as "pre-Linnaean", and not considered valid (with the exception of spiders published in Svenska Spindlar [39] ). Even taxonomic names published by Linnaeus himself before these dates are considered pre-Linnaean. [22]

A pattern of groups nested within groups was specified by Linnaeus' classifications of plants and animals, and these patterns began to be represented as dendrograms of the animal and plant kingdoms toward the end of the 18th century, well before On the Origin of Species was published. [25] The pattern of the "Natural System" did not entail a generating process, such as evolution, but may have implied it, inspiring early transmutationist thinkers . Among early works exploring the idea of a transmutation of species were Erasmus Darwin's 1796 Zoönomia and Jean-Baptiste Lamarck's Philosophie Zoologique of 1809. [12] The idea was popularized in the Anglophone world by the speculative but widely read Vestiges of the Natural History of Creation, published anonymously by Robert Chambers in 1844. [40]

With Darwin's theory, a general acceptance quickly appeared that a classification should reflect the Darwinian principle of common descent. [41] Tree of life representations became popular in scientific works, with known fossil groups incorporated. One of the first modern groups tied to fossil ancestors was birds. [42] Using the then newly discovered fossils of Archaeopteryx and Hesperornis, Thomas Henry Huxley pronounced that they had evolved from dinosaurs, a group formally named by Richard Owen in 1842. [43] [44] The resulting description, that of dinosaurs "giving rise to" or being "the ancestors of" birds, is the essential hallmark of evolutionary taxonomic thinking. As more and more fossil groups were found and recognized in the late 19th and early 20th centuries, palaeontologists worked to understand the history of animals through the ages by linking together known groups. [45] With the modern evolutionary synthesis of the early 1940s, an essentially modern understanding of the evolution of the major groups was in place. As evolutionary taxonomy is based on Linnaean taxonomic ranks, the two terms are largely interchangeable in modern use. [46]

The cladistic method has emerged since the 1960s. [41] In 1958, Julian Huxley used the term clade. [12] Later, in 1960, Cain and Harrison introduced the term cladistic. [12] The salient feature is arranging taxa in a hierarchical evolutionary tree, with the desideratum that all named taxa are monophyletic. [41] A taxon is called monophyletic if it includes all the descendants of an ancestral form. [47] [48] Groups that have descendant groups removed from them are termed paraphyletic, [47] while groups representing more than one branch from the tree of life are called polyphyletic. [47] [48] Monophyletic groups are recognized and diagnosed on the basis of synapomorphies, shared derived character states. [49]

Cladistic classifications are compatible with traditional Linnean taxonomy and the Codes of Zoological and Botanical Nomenclature. [50] An alternative system of nomenclature, the International Code of Phylogenetic Nomenclature or PhyloCode has been proposed, whose intent is to regulate the formal naming of clades. [51] [52] Linnaean ranks will be optional under the PhyloCode, which is intended to coexist with the current, rank-based codes. [52] It remains to be seen whether the systematic community will adopt the PhyloCode or reject it in favor of the current systems of nomenclature that have been employed (and modified as needed) for over 250 years.

Kingdoms and domains Edit

Well before Linnaeus, plants and animals were considered separate Kingdoms. [53] Linnaeus used this as the top rank, dividing the physical world into the vegetable, animal and mineral kingdoms. As advances in microscopy made classification of microorganisms possible, the number of kingdoms increased, five- and six-kingdom systems being the most common.

Domains are a relatively new grouping. First proposed in 1977, Carl Woese's three-domain system was not generally accepted until later. [54] One main characteristic of the three-domain method is the separation of Archaea and Bacteria, previously grouped into the single kingdom Bacteria (a kingdom also sometimes called Monera), [53] with the Eukaryota for all organisms whose cells contain a nucleus. [55] A small number of scientists include a sixth kingdom, Archaea, but do not accept the domain method. [53]

Thomas Cavalier-Smith, who published extensively on the classification of protists, recently [ when? ] proposed that the Neomura, the clade that groups together the Archaea and Eucarya, would have evolved from Bacteria, more precisely from Actinobacteria. His 2004 classification treated the archaeobacteria as part of a subkingdom of the kingdom Bacteria, i.e., he rejected the three-domain system entirely. [56] Stefan Luketa in 2012 proposed a five "dominion" system, adding Prionobiota (acellular and without nucleic acid) and Virusobiota (acellular but with nucleic acid) to the traditional three domains. [57]

1735 [58]
1866 [59]
1925 [60]
1938 [61]
1969 [62]
Woese et al.
1990 [63]
1998 [56]
2015 [64]
2 kingdoms 3 kingdoms 2 empires 4 kingdoms 5 kingdoms 3 domains 2 empires, 6 kingdoms 2 empires, 7 kingdoms
(not treated) Protista Prokaryota Monera Monera Bacteria Bacteria Bacteria
Archaea Archaea
Eukaryota Protoctista Protista Eucarya Protozoa Protozoa
Chromista Chromista
Vegetabilia Plantae Plantae Plantae Plantae Plantae
Fungi Fungi Fungi
Animalia Animalia Animalia Animalia Animalia Animalia

Recent comprehensive classifications Edit

Partial classifications exist for many individual groups of organisms and are revised and replaced as new information becomes available however, comprehensive, published treatments of most or all life are rarer recent examples are that of Adl et al., 2012 and 2019, [65] [66] which covers eukaryotes only with an emphasis on protists, and Ruggiero et al., 2015, [67] covering both eukaryotes and prokaryotes to the rank of Order, although both exclude fossil representatives. [67] A separate compilation (Ruggiero, 2014) [68] covers extant taxa to the rank of family. Other, database-driven treatments include the Encyclopedia of Life, the Global Biodiversity Information Facility, the NCBI taxonomy database, the Interim Register of Marine and Nonmarine Genera, the Open Tree of Life, and the Catalogue of Life. The Paleobiology Database is a resource for fossils.

Biological taxonomy is a sub-discipline of biology, and is generally practiced by biologists known as "taxonomists", though enthusiastic naturalists are also frequently involved in the publication of new taxa. [69] Because taxonomy aims to describe and organize life, the work conducted by taxonomists is essential for the study of biodiversity and the resulting field of conservation biology. [70] [71]

Classifying organisms Edit

Biological classification is a critical component of the taxonomic process. As a result, it informs the user as to what the relatives of the taxon are hypothesized to be. Biological classification uses taxonomic ranks, including among others (in order from most inclusive to least inclusive): Domain, Kingdom, Phylum, Class, Order, Family, Genus, Species, and Strain. [72] [note 1]

Taxonomic descriptions Edit

The "definition" of a taxon is encapsulated by its description or its diagnosis or by both combined. There are no set rules governing the definition of taxa, but the naming and publication of new taxa is governed by sets of rules. [8] In zoology, the nomenclature for the more commonly used ranks (superfamily to subspecies), is regulated by the International Code of Zoological Nomenclature (ICZN Code). [73] In the fields of phycology, mycology, and botany, the naming of taxa is governed by the International Code of Nomenclature for algae, fungi, and plants (ICN). [74]

The initial description of a taxon involves five main requirements: [75]

  1. The taxon must be given a name based on the 26 letters of the Latin alphabet (a binomial for new species, or uninomial for other ranks).
  2. The name must be unique (i.e. not a homonym).
  3. The description must be based on at least one name-bearing type specimen.
  4. It should include statements about appropriate attributes either to describe (define) the taxon or to differentiate it from other taxa (the diagnosis, ICZN Code, Article 13.1.1, ICN, Article 38). Both codes deliberately separate defining the content of a taxon (its circumscription) from defining its name.
  5. These first four requirements must be published in a work that is obtainable in numerous identical copies, as a permanent scientific record.

However, often much more information is included, like the geographic range of the taxon, ecological notes, chemistry, behavior, etc. How researchers arrive at their taxa varies: depending on the available data, and resources, methods vary from simple quantitative or qualitative comparisons of striking features, to elaborate computer analyses of large amounts of DNA sequence data. [76]

Author citation Edit

An "authority" may be placed after a scientific name. [77] The authority is the name of the scientist or scientists who first validly published the name. [77] For example, in 1758 Linnaeus gave the Asian elephant the scientific name Elephas maximus, so the name is sometimes written as "Elephas maximus Linnaeus, 1758". [78] The names of authors are frequently abbreviated: the abbreviation L., for Linnaeus, is commonly used. In botany, there is, in fact, a regulated list of standard abbreviations (see list of botanists by author abbreviation). [79] The system for assigning authorities differs slightly between botany and zoology. [8] However, it is standard that if the genus of a species has been changed since the original description, the original authority's name is placed in parentheses. [80]

In phenetics, also known as taximetrics, or numerical taxonomy, organisms are classified based on overall similarity, regardless of their phylogeny or evolutionary relationships. [12] It results in a measure of evolutionary "distance" between taxa. Phenetic methods have become relatively rare in modern times, largely superseded by cladistic analyses, as phenetic methods do not distinguish common ancestral (or plesiomorphic) traits from new common (or apomorphic) traits. [81] However, certain phenetic methods, such as neighbor joining, have found their way into cladistics, as a reasonable approximation of phylogeny when more advanced methods (such as Bayesian inference) are too computationally expensive. [82]

Modern taxonomy uses database technologies to search and catalogue classifications and their documentation. [83] While there is no commonly used database, there are comprehensive databases such as the Catalogue of Life, which attempts to list every documented species. [84] The catalogue listed 1.64 million species for all kingdoms as of April 2016, claiming coverage of more than three quarters of the estimated species known to modern science. [85]

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