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Bootstrapping

In general, bootstrapping usually refers to a self-starting process that is supposed to continue or grow without external input.

Etymology
Tall boots may have a tab, loop or handle at the top known as a bootstrap, allowing one to use fingers or a boot hook tool to help pull the boots on. The saying "to " was already in use during the 19th century as an example of an impossible task. The idiom dates at least to 1834, when it appeared in the Workingman's Advocate: "It is conjectured that Mr. Murphee will now be enabled to hand himself over the Cumberland river or a barn yard fence by the straps of his boots." In 1860 it appeared in a comment on philosophy of mind: "The attempt of the mind to analyze itself [is] an effort analogous to one who would lift himself by his own bootstraps." Bootstrap as a metaphor, meaning to better oneself by one's own unaided efforts, was in use in 1922. This metaphor spawned additional metaphors for a series of self-sustaining processes that proceed without external help.

The term is sometimes attributed to a story in Rudolf Erich Raspe's The Surprising Adventures of Baron Munchausen, but in that story Baron Munchausen pulls himself (and his horse) out of a swamp by his hair (specifically, his pigtail), not by his bootstraps – and no explicit reference to bootstraps has been found elsewhere in the various versions of the Munchausen tales.

Originally meant to attempt something ludicrously far-fetched or even impossible, the phrase "Pull yourself up by your bootstraps!" has since been utilized as a narrative for economic mobility or a cure for depression. That idea is believed to have been popularized by American writer Horatio Alger in the 19th century. To request that someone "bootstrap" is to suggest that they might overcome great difficulty by sheer force of will.

Critics have observed that the phrase is used to portray unfair situations as far more meritocratic than they really are. A 2009 study found that 77% of Americans believe that wealth is often the result of hard work. Various studies have found that the main predictor of future wealth is not IQ or hard work, but initial wealth.

Computing
In computer technology, the term bootstrapping refers to language compilers that are able to be coded in the same language. (For example, a C compiler is now written in the C language. Once the basic compiler is written, improvements can be iteratively made, thus pulling the language up by its bootstraps). Also, booting usually refers to the process of loading the basic software into the memory of a computer after power-on or general reset, the kernel will load the operating system which will then take care of loading other device drivers and software as needed.

Software loading and execution
Booting is the process of starting a computer, specifically with regard to starting its software. The process involves a chain of stages, in which at each stage, a relatively small and simple program loads and then executes the larger, more complicated program of the next stage. It is in this sense that the computer "pulls itself up by its bootstraps"; i.e., it improves itself by its own efforts. Booting is a chain of events that starts with execution of hardware-based procedures and may then hand-off to firmware and software which is loaded into main memory. Booting often involves processes such as performing self-tests, loading configuration settings, loading a BIOS, resident monitors, a hypervisor, an operating system, or utility software.

The computer term bootstrap began as a metaphor in the 1950s. In computers, pressing a bootstrap button caused a hardwired program to read a bootstrap program from an input unit. The computer would then execute the bootstrap program, which caused it to read more program instructions. It became a self-sustaining process that proceeded without external help from manually entered instructions. As a computing term, bootstrap has been used since at least 1953.

Software development
Bootstrapping can also refer to the development of successively more complex, faster programming environments. The simplest environment will be, perhaps, a very basic text editor (e.g., ed) and an assembler program. Using these tools, one can write a more complex text editor, and a simple compiler for a higher-level language and so on, until one can have a graphical IDE and an extremely high-level programming language.

Historically, bootstrapping also refers to an early technique for computer program development on new hardware. The technique described in this paragraph has been replaced by the use of a cross compiler executed by a pre-existing computer. Bootstrapping in program development began during the 1950s when each program was constructed on paper in decimal code or in binary code, bit by bit (1s and 0s), because there was no high-level computer language, no compiler, no assembler, and no linker. A tiny assembler program was hand-coded for a new computer (for example the IBM 650) which converted a few instructions into binary or decimal code: A1. This simple assembler program was then rewritten in its just-defined assembly language but with extensions that would enable the use of some additional mnemonics for more complex operation codes. The enhanced assembler's source program was then assembled by its predecessor's executable (A1) into binary or decimal code to give A2, and the cycle repeated (now with those enhancements available), until the entire instruction set was coded, branch addresses were automatically calculated, and other conveniences (such as conditional assembly, macros, optimisations, etc.) established. This was how the early Symbolic Optimal Assembly Program (SOAP) was developed. Compilers, linkers, loaders, and utilities were then coded in assembly language, further continuing the bootstrapping process of developing complex software systems by using simpler software.

The term was also championed by Doug Engelbart to refer to his belief that organizations could better evolve by improving the process they use for improvement (thus obtaining a compounding effect over time). His SRI team that developed the NLS hypertext system applied this strategy by using the tool they had developed to improve the tool.

Compilers
The development of compilers for new programming languages first developed in an existing language but then rewritten in the new language and compiled by itself, is another example of the bootstrapping notion.

Installers
During the installation of computer programs, it is sometimes necessary to update the installer or package manager itself. The common pattern for this is to use a small executable bootstrapper file (e.g., setup.exe) which updates the installer and starts the real installation after the update. Sometimes the bootstrapper also installs other prerequisites for the software during the bootstrapping process.

Overlay networks
A bootstrapping node, also known as a rendezvous host, is a node in an overlay network that provides initial configuration information to newly joining nodes so that they may successfully join the overlay network.

Discrete-event simulation
A type of computer simulation called discrete-event simulation represents the operation of a system as a chronological sequence of events. A technique called bootstrapping the simulation model is used, which bootstraps initial data points using a pseudorandom number generator to schedule an initial set of pending events, which schedule additional events, and with time, the distribution of event times approaches its steady state—the bootstrapping behavior is overwhelmed by steady-state behavior.

Artificial intelligence and machine learning
Bootstrapping is a technique used to iteratively improve a classifier's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined.

Seed AI is a hypothesized type of artificial intelligence capable of recursive self-improvement. Having improved itself, it would become better at improving itself, potentially leading to an exponential increase in intelligence. No such AI is known to exist, but it remains an active field of research. Seed AI is a significant part of some theories about the technological singularity: proponents believe that the development of seed AI will rapidly yield ever-smarter intelligence (via bootstrapping) and thus a new era.

Statistics
Bootstrapping is a resampling technique used to obtain estimates of summary statistics.

Business
Bootstrapping in business means starting a business without external help or working capital. Entrepreneurs in the startup development phase of their company survive through internal cash flow and are very cautious with their expenses. Generally at the start of a venture, a small amount of money will be set aside for the bootstrap process. Bootstrapping can also be a supplement for econometric models. Bootstrapping was also expanded upon in the book Bootstrap Business by Richard Christiansen, the Harvard Business Review article The Art of Bootstrapping and the follow-up book The Origin and Evolution of New Businesses by Amar Bhide. There is also an entire bible written on how to properly bootstrap by Seth Godin.

Experts have noted that several common stages exist for bootstrapping a business venture:

 * 1) Birth-stage: This is the first stage to bootstrapping by which the entrepreneur utilizes any personal savings or borrowed and/or invested money from friends and family to launch the business. It is also possible for the business owner to be running or working for another organization at the time which may help to fuel their business and cover initial expenses.
 * 2) Funding from sales to consumers-stage: In this particular stage, money from customers is used to keep the business operating afloat. Once expenses caused by normal day-to-day business operations are met, the rate growth usually increases.
 * 3) Outsourcing-stage: At this point in the company's existence, the entrepreneur in question normally concentrates on the specific operating activities. This is the time in which entrepreneurs decide how to improve and upgrade equipment (subsequently increasing output) or even employing new staff members. At this point in time, the company may seek loans or even lean on other methods of additional funding such as venture capital to help with expansion and other improvements.

There are many types of companies that are eligible for bootstrapping. Early-stage companies that do not necessarily require large influxes of capital (particularly from outside sources) qualify. This would specifically allow for flexibility for the business and time to grow. Serial entrepreneur companies could also possibly reap the benefits of bootstrapping. These are organizations whereby the founder has money from the sale of a previous companies they can use to invest.

There are different methods of bootstrapping. Future business owners aspiring to use bootstrapping as way of launching their product or service often use the following methods:
 * Using accessible money from their own personal savings.
 * Managing their working capital in a way that minimizes their company's accounts receivable.
 * Cashing out 401k retirement funds and pay them off at later dates.
 * Gradually increasing the business' accounts payable through delaying payments or even renting equipment instead of buying them.

Bootstrapping is often considered successful. When taking into account statistics provided by Fundera, approximately 77% of small business rely on some sort of personal investment and or savings in order to fund their startup ventures. The average small business venture requires approximately $10,000 in startup capital with a third of small business launching with less than $5,000 bootstrapped.

Based on startup data presented by Entrepreneur.com, in comparison other methods of funding, bootstrapping is more commonly used than others. "0.91% of startups are funded by angel investors, while 0.05% are funded by VCs. In contrast, 57 percent of startups are funded by personal loans and credit, while 38 percent receive funding from family and friends."

Some examples of successful entrepreneurs that have used bootstrapping in order to finance their businesses include serial entrepreneur Mark Cuban. He has publicly endorsed bootstrapping claiming that "If you can start on your own … do it by [yourself] without having to go out and raise money." When asked why he believed this approach was most necessary, he replied, "I think the biggest mistake people make is once they have an idea and the goal of starting a business, they think they have to raise money. And once you raise money, that's not an accomplishment, that's an obligation" because "now, you're reporting to whoever you raised money from."

Bootstrapped companies such as Apple Inc. (APPL), eBay Inc. (EBAY) and Coca-Cola Co. have also claimed that they attribute some of their success to the fact that this method of funding enables them to remain highly focused on a specific array of profitable product.

Startups can grow by reinvesting profits in its own growth if bootstrapping costs are low and return on investment is high. This financing approach allows owners to maintain control of their business and forces them to spend with discipline. In addition, bootstrapping allows startups to focus on customers rather than investors, thereby increasing the likelihood of creating a profitable business. This leaves startups with a better exit strategy with greater returns.

Leveraged buyouts, or highly leveraged or "bootstrap" transactions, occur when an investor acquires a controlling interest in a company's equity and where a significant percentage of the purchase price is financed through leverage, i.e. borrowing by the acquired company.

Bootstrapping in finance refers to the method to create the spot rate curve. Operation Bootstrap (Operación Manos a la Obra) refers to the ambitious projects that industrialized Puerto Rico in the mid-20th century.

Biology
Richard Dawkins in his book River Out of Eden used the computer bootstrapping concept to explain how biological cells differentiate: "Different cells receive different combinations of chemicals, which switch on different combinations of genes, and some genes work to switch other genes on or off. And so the bootstrapping continues, until we have the full repertoire of different kinds of cells."

Phylogenetics
Bootstrapping analysis gives a way to judge the strength of support for clades on phylogenetic trees. A number is written by a node, which reflects the percentage of bootstrap trees which also resolve the clade at the endpoints of that branch.

Law
Bootstrapping is a rule preventing the admission of hearsay evidence in conspiracy cases.

Linguistics
Bootstrapping is a theory of language acquisition.

Quantum theory
Bootstrapping is using very general consistency criteria to determine the form of a quantum theory from some assumptions on the spectrum of particles or operators.

Magnetically confined fusion plasmas
In tokamak fusion devices, bootstrapping refers to the process in which a bootstrap current is self-generated by the plasma, which reduces or eliminates the need for an external current driver. Maximising the bootstrap current is a major goal of advanced tokamak designs.

Inertially confined fusion plasmas
Bootstrapping in inertial confinement fusion refers to the alpha particles produced in the fusion reaction providing further heating to the plasma. This heating leads to ignition and an overall energy gain.

Electronics
Bootstrapping is a form of positive feedback in analog circuit design.

Electric power grid
An electric power grid is almost never brought down intentionally. Generators and power stations are started and shut down as necessary. A typical power station requires power for start up prior to being able to generate power. This power is obtained from the grid, so if the entire grid is down these stations cannot be started.

Therefore, to get a grid started, there must be at least a small number of power stations that can start entirely on their own. A black start is the process of restoring a power station to operation without relying on external power. In the absence of grid power, one or more black starts are used to bootstrap the grid.

Nuclear power
A nuclear power plant always needs to have a way to remove decay heat, which is usually done with electrical cooling pumps. But in the rare case of a complete loss of electrical power, this can still be achieved by booting a turbine generator. As steam builds up in the steam generator, it can be used to power the turbine generator (initially with no oil pumps, circ water pumps, or condensation pumps). Once the turbine generator is producing electricity, the auxiliary pumps can be powered on, and the reactor cooling pumps can be run momentarily. Eventually the steam pressure will become insufficient to power the turbine generator, and the process can be shut down in reverse order. The process can be repeated until no longer needed. This can cause great damage to the turbine generator, but more importantly, it saves the nuclear reactor.

Cellular networks
A Bootstrapping Server Function (BSF) is an intermediary element in cellular networks which provides application independent functions for mutual authentication of user equipment and servers unknown to each other and for 'bootstrapping' the exchange of secret session keys afterwards. The term 'bootstrapping' is related to building a security relation with a previously unknown device first and to allow installing security elements (keys) in the device and the BSF afterwards.

title: Bootstrapping


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Baltic languages

The Baltic languages are a branch of the Indo-European language family spoken natively or as a second language by a population of about 6.5–7.0 million people mainly in areas extending east and southeast of the Baltic Sea in Europe. Together with the Slavic languages, they form the Balto-Slavic branch of the Indo-European family.

Scholars usually regard them as a single subgroup divided into two branches: West Baltic (containing only extinct languages) and East Baltic (containing at least two living languages, Lithuanian, Latvian, and by some counts including Latgalian and Samogitian as separate languages rather than dialects of those two). The range of the East Baltic linguistic influence once possibly reached as far as the Ural Mountains, but this hypothesis has been questioned.

Old Prussian, a Western Baltic language that became extinct in the 18th century, had possibly conserved the greatest number of properties from Proto-Baltic.

Although related, Lithuanian, Latvian, and particularly Old Prussian have lexicons that differ substantially from one another and so the languages are not mutually intelligible. Relatively low mutual interaction for neighbouring languages historically led to gradual erosion of mutual intelligibility, and development of their respective linguistic innovations that did not exist in shared Proto-Baltic. The substantial number of false friends and various uses and sources of loanwords from their surrounding languages are considered to be the major reasons for poor mutual intelligibility today.

Branches
Within Indo-European, the Baltic languages are generally classified as forming a single family with two branches: Eastern and Western Baltic. But these two branches are sometimes classified as independent branches of Balto-Slavic itself.

History
It is believed that the Baltic languages are among the most conservative of the currently remaining Indo-European languages, despite their late attestation.

Although the Baltic Aesti tribe was mentioned by ancient historians such as Tacitus as early as 98 CE, the first attestation of a Baltic language was c. 1369, in a Basel epigram of two lines written in Old Prussian. Lithuanian was first attested in a printed book, which is a Catechism by Martynas Mažvydas published in 1547. Latvian appeared in a printed Catechism in 1585.

One reason for the late attestation is that the Baltic peoples resisted Christianization longer than any other Europeans, which delayed the introduction of writing and isolated their languages from outside influence.

With the establishment of a German state in Prussia, and the mass influx of Germanic (and to a lesser degree Slavic-speaking) settlers, the Prussians began to be assimilated, and by the end of the 17th century, the Prussian language had become extinct.

After the Partitions of Poland, most of the Baltic lands were under the rule of the Russian Empire, where the native languages or alphabets were sometimes prohibited from being written down or used publicly in a Russification effort (see Lithuanian press ban for the ban in force from 1864 to 1904).

Geographic distribution
Speakers of modern Baltic languages are generally concentrated within the borders of Lithuania and Latvia, and in emigrant communities in the United States, Canada, Australia and the countries within the former borders of the Soviet Union.

Historically the languages were spoken over a larger area: west to the mouth of the Vistula river in present-day Poland, at least as far east as the Dniepr river in present-day Belarus, perhaps even to Moscow, and perhaps as far south as Kyiv. Key evidence of Baltic language presence in these regions is found in hydronyms (names of bodies of water) that are characteristically Baltic. The use of hydronyms is generally accepted to determine the extent of a culture's influence, but not the date of such influence.

The eventual expansion of the use of Slavic languages in the south and east, and Germanic languages in the west, reduced the geographic distribution of Baltic languages to a fraction of the area that they formerly covered. The Russian geneticist Oleg Balanovsky speculated that there is a predominance of the assimilated pre-Slavic substrate in the genetics of East and West Slavic populations, according to him the common genetic structure which contrasts East Slavs and Balts from other populations may suggest that the pre-Slavic substrate of the East Slavs consists most significantly of Baltic-speakers, which predated the Slavs in the cultures of the Eurasian steppe according to archaeological references he cites.

Contact with Uralic languages
Though Estonia is geopolitically included among the Baltic states due to its location, Estonian is a Finnic language of the Uralic language family and is not related to the Baltic languages, which are Indo-European.

The Mordvinic languages, spoken mainly along western tributaries of the Volga, show several dozen loanwords from one or more Baltic languages. These may have been mediated by contacts with the Eastern Balts along the river Oka. In regards to the same geographical location, Asko Parpola, in a 2013 article, suggested that the Baltic presence in this area, dated to c. 200–600 CE, is due to an "elite superstratum". However, linguist Petri Kallio argued that the Volga-Oka is a secondary Baltic-speaking area, expanding from East Baltic, due to a large number of Baltic loanwords in Finnic and Saami.

Finnish scholars also indicate that Latvian had extensive contacts with Livonian, and, to a lesser extent, to Estonian and South Estonian. Therefore, this contact accounts for the number of Finnic hydronyms in Lithuania and Latvia that increase in a northwards direction.

Parpola, in the same article, supposed the existence of a Baltic substratum for Finnic, in Estonia and coastal Finland. In the same vein, Kallio argues for the existence of a lost "North Baltic language" that would account for loanwords during the evolution of the Finnic branch.

Genetic relatedness

The Baltic languages are of particular interest to linguists because they retain many archaic features, which are thought to have been present in the early stages of the Proto-Indo-European language. However, linguists have had a hard time establishing the precise relationship of the Baltic languages to other languages in the Indo-European family. Several of the extinct Baltic languages have a limited or nonexistent written record, their existence being known only from the records of ancient historians and personal or place names. All of the languages in the Baltic group (including the living ones) were first written down relatively late in their probable existence as distinct languages. These two factors combined with others have obscured the history of the Baltic languages, leading to a number of theories regarding their position in the Indo-European family.

The Baltic languages show a close relationship with the Slavic languages, and are grouped with them in a Balto-Slavic family by most scholars. This family is considered to have developed from a common ancestor, Proto-Balto-Slavic. Later on, several lexical, phonological and morphological dialectisms developed, separating the various Balto-Slavic languages from each other. Although it is generally agreed that the Slavic languages developed from a single more-or-less unified dialect (Proto-Slavic) that split off from common Balto-Slavic, there is more disagreement about the relationship between the Baltic languages.

The traditional view is that the Balto-Slavic languages split into two branches, Baltic and Slavic, with each branch developing as a single common language (Proto-Baltic and Proto-Slavic) for some time afterwards. Proto-Baltic is then thought to have split into East Baltic and West Baltic branches. However, more recent scholarship has suggested that there was no unified Proto-Baltic stage, but that Proto-Balto-Slavic split directly into three groups: Slavic, East Baltic and West Baltic. Under this view, the Baltic family is paraphyletic, and consists of all Balto-Slavic languages that are not Slavic. In the 1960s Vladimir Toporov and Vyacheslav Ivanov made the following conclusions about the relationship between the Baltic and Slavic languages:
 * the Proto-Slavic language formed out of peripheral-type Baltic dialects;
 * the Slavic linguistic type formed later from the structural model of the Baltic languages;
 * the Slavic structural model is a result of the transformation from the Baltic languages structural model.

These scholars' theses do not contradict the close relationship between Baltic and Slavic languages and, from a historical perspective, specify the Baltic-Slavic languages' evolution – the terms 'Baltic' and 'Slavic' are relevant only from the point of view of the present time, meaning diachronic changes, and the oldest stage of the language development could be called both Baltic and Slavic; this concept does not contradict the traditional thesis that the Proto-Slavic and Proto-Baltic languages coexisted for a long time after their formation – between the 2nd millennium BC and circa the 5th century BC – the Proto-Slavic language was a continuum of the Proto-Baltic dialects, more rather, the Proto-Slavic language should have been localized in the peripheral circle of Proto-Baltic dialects.

Finally, a minority of scholars argue that Baltic descended directly from Proto-Indo-European, without an intermediate common Balto-Slavic stage. They argue that the many similarities and shared innovations between Baltic and Slavic are caused by several millennia of contact between the groups, rather than a shared heritage.

Thracian hypothesis
The Baltic-speaking peoples likely encompassed an area in eastern Europe much larger than their modern range. As in the case of the Celtic languages of Western Europe, they were reduced by invasion, extermination and assimilation. Studies in comparative linguistics point to genetic relationship between the languages of the Baltic family and the following extinct languages: The Baltic classification of Dacian and Thracian has been proposed by the Lithuanian scientist Jonas Basanavičius, who insisted this is the most important work of his life and listed 600 identical words of Balts and Thracians. His theory included Phrygian in the related group, but this did not find support and was disapproved among other authors, such as Ivan Duridanov, whose own analysis found Phrygian completely lacking parallels in either Thracian or Baltic languages.
 * Dacian
 * Thracian

The Bulgarian linguist Ivan Duridanov, who improved the most extensive list of toponyms, in his first publication claimed that Thracian is genetically linked to the Baltic languages and in the next one he made the following classification: "'The Thracian language formed a close group with the Baltic, the Dacian and the 'Pelasgian' languages. More distant were its relations with the other Indo-European languages, and especially with Greek, the Italic and Celtic languages, which exhibit only isolated phonetic similarities with Thracian; the Tokharian and the Hittite were also distant. '"Of about 200 reconstructed Thracian words by Duridanov most cognates (138) appear in the Baltic languages, mostly in Lithuanian, followed by Germanic (61), Indo-Aryan (41), Greek (36), Bulgarian (23), Latin (10) and Albanian (8). The cognates of the reconstructed Dacian words in his publication are found mostly in the Baltic languages, followed by Albanian. Parallels have enabled linguists, using the techniques of comparative linguistics, to decipher the meanings of several Dacian and Thracian placenames with, they claim, a high degree of probability. Of 74 Dacian placenames attested in primary sources and considered by Duridanov, a total of 62 have Baltic cognates, most of which were rated "certain" by Duridanov. For a big number of 300 Thracian geographic names most parallels were found between Thracian and Baltic geographic names in the study of Duridanov. According to him the most important impression make the geographic cognates of Baltic and Thracian "'the similarity of these parallels stretching frequently on the main element and the suffix simultaneously, which makes a strong impression'."

Romanian linguist Sorin Paliga, analysing and criticizing Harvey Mayer's study, did admit "great likeness" between Thracian, the substrate of Romanian, and "some Baltic forms".

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Baltic languages


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Bioinformatics

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

Computational, statistical, and computer programming techniques have been used for computer simulation analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of genomics, such as by the identification of genes and single nucleotide polymorphisms (SNPs). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. Bioinformatics also includes proteomics, which tries to understand the organizational principles within nucleic acid and protein sequences.

Image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations. Bioinformatics includes text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation. Bioinformatics tools aid in comparing, analyzing and interpreting genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology. At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology. In structural biology, it aids in the simulation and modeling of DNA, RNA, proteins as well as biomolecular interactions.

History
The first definition of the term bioinformatics was coined by Paulien Hogeweg and Ben Hesper in 1970, to refer to the study of information processes in biotic systems. This definition placed bioinformatics as a field parallel to biochemistry (the study of chemical processes in biological systems).

Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology.

Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics.

Sequences

There has been a tremendous advance in speed and cost reduction since the completion of the Human Genome Project, with some labs able to sequence over 100,000 billion bases each year, and a full genome can be sequenced for $1,000 or less.

Computers became essential in molecular biology when protein sequences became available after Frederick Sanger determined the sequence of insulin in the early 1950s. Comparing multiple sequences manually turned out to be impractical. Margaret Oakley Dayhoff, a pioneer in the field, compiled one of the first protein sequence databases, initially published as books as well as methods of sequence alignment and molecular evolution. Another early contributor to bioinformatics was Elvin A. Kabat, who pioneered biological sequence analysis in 1970 with his comprehensive volumes of antibody sequences released online with Tai Te Wu between 1980 and 1991.

In the 1970s, new techniques for sequencing DNA were applied to bacteriophage MS2 and øX174, and the extended nucleotide sequences were then parsed with informational and statistical algorithms. These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were the proof of the concept that bioinformatics would be insightful.

Goals
In order to study how normal cellular activities are altered in different disease states, raw biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This also includes nucleotide and amino acid sequences, protein domains, and protein structures.

Important sub-disciplines within bioinformatics and computational biology include:
 * Development and implementation of computer programs to efficiently access, manage, and use various types of information.
 * Development of new mathematical algorithms and statistical measures to assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences.

The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, the modeling of evolution and cell division/mitosis.

Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data.

Over the past few decades, rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes.

Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.

Sequence analysis
Since the bacteriophage Phage Φ-X174 was sequenced in 1977, the DNA sequences of thousands of organisms have been decoded and stored in databases. This sequence information is analyzed to determine genes that encode proteins, RNA genes, regulatory sequences, structural motifs, and repetitive sequences. A comparison of genes within a species or between different species can show similarities between protein functions, or relations between species (the use of molecular systematics to construct phylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. Computer programs such as BLAST are used routinely to search sequences—as of 2008, from more than 260,000 organisms, containing over 190 billion nucleotides.

DNA sequencing
Before sequences can be analyzed, they are obtained from a data storage bank, such as GenBank. DNA sequencing is still a non-trivial problem as the raw data may be noisy or affected by weak signals. Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing.

Sequence assembly
Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences. The shotgun sequencing technique (used by The Institute for Genomic Research (TIGR) to sequence the first bacterial genome, Haemophilus influenzae) generates the sequences of many thousands of small DNA fragments (ranging from 35 to 900 nucleotides long, depending on the sequencing technology). The ends of these fragments overlap and, when aligned properly by a genome assembly program, can be used to reconstruct the complete genome. Shotgun sequencing yields sequence data quickly, but the task of assembling the fragments can be quite complicated for larger genomes. For a genome as large as the human genome, it may take many days of CPU time on large-memory, multiprocessor computers to assemble the fragments, and the resulting assembly usually contains numerous gaps that must be filled in later. Shotgun sequencing is the method of choice for virtually all genomes sequenced (rather than chain-termination or chemical degradation methods), and genome assembly algorithms are a critical area of bioinformatics research.

Genome annotation
In genomics, annotation refers to the process of marking the stop and start regions of genes and other biological features in a sequenced DNA sequence. Many genomes are too large to be annotated by hand. As the rate of sequencing exceeds the rate of genome annotation, genome annotation has become the new bottleneck in bioinformatics.

Genome annotation can be classified into three levels: the nucleotide, protein, and process levels.

Gene finding is a chief aspect of nucleotide-level annotation. For complex genomes, a combination of ab initio gene prediction and sequence comparison with expressed sequence databases and other organisms can be successful. Nucleotide-level annotation also allows the integration of genome sequence with other genetic and physical maps of the genome.

The principal aim of protein-level annotation is to assign function to the protein products of the genome. Databases of protein sequences and functional domains and motifs are used for this type of annotation. About half of the predicted proteins in a new genome sequence tend to have no obvious function.

Understanding the function of genes and their products in the context of cellular and organismal physiology is the goal of process-level annotation. An obstacle of process-level annotation has been the inconsistency of terms used by different model systems. The Gene Ontology Consortium is helping to solve this problem.

The first description of a comprehensive annotation system was published in 1995 by The Institute for Genomic Research, which performed the first complete sequencing and analysis of the genome of a free-living (non-symbiotic) organism, the bacterium Haemophilus influenzae. The system identifies the genes encoding all proteins, transfer RNAs, ribosomal RNAs, in order to make initial functional assignments. The GeneMark program trained to find protein-coding genes in Haemophilus influenzae is constantly changing and improving.

Following the goals that the Human Genome Project left to achieve after its closure in 2003, the ENCODE project was developed by the National Human Genome Research Institute. This project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).

Gene function prediction
While genome annotation is primarily based on sequence similarity (and thus homology), other properties of sequences can be used to predict the function of genes. In fact, most gene function prediction methods focus on protein sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobic amino acids predicts transmembrane segments in proteins. However, protein function prediction can also use external information such as gene (or protein) expression data, protein structure, or protein-protein interactions.

Computational evolutionary biology
Evolutionary biology is the study of the origin and descent of species, as well as their change over time. Informatics has assisted evolutionary biologists by enabling researchers to: Future work endeavours to reconstruct the now more complex tree of life.
 * trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone,
 * compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in bacterial speciation,
 * build complex computational population genetics models to predict the outcome of the system over time
 * track and share information on an increasingly large number of species and organisms

Comparative genomics
The core of comparative genome analysis is the establishment of the correspondence between genes (orthology analysis) or other genomic features in different organisms. Intergenomic maps are made to trace the evolutionary processes responsible for the divergence of two genomes. A multitude of evolutionary events acting at various organizational levels shape genome evolution. At the lowest level, point mutations affect individual nucleotides. At a higher level, large chromosomal segments undergo duplication, lateral transfer, inversion, transposition, deletion and insertion. Entire genomes are involved in processes of hybridization, polyploidization and endosymbiosis that lead to rapid speciation. The complexity of genome evolution poses many exciting challenges to developers of mathematical models and algorithms, who have recourse to a spectrum of algorithmic, statistical and mathematical techniques, ranging from exact, heuristics, fixed parameter and approximation algorithms for problems based on parsimony models to Markov chain Monte Carlo algorithms for Bayesian analysis of problems based on probabilistic models.

Many of these studies are based on the detection of sequence homology to assign sequences to protein families.

Pan genomics
Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular monophyletic taxonomic group. Although initially applied to closely related strains of a species, it can be applied to a larger context like genus, phylum, etc. It is divided in two parts: the Core genome, a set of genes common to all the genomes under study (often housekeeping genes vital for survival), and the Dispensable/Flexible genome: a set of genes not present in all but one or some genomes under study. A bioinformatics tool BPGA can be used to characterize the Pan Genome of bacterial species.

Genetics of disease
As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. Simple Mendelian inheritance has been observed for over 3,000 disorders that have been identified at the Online Mendelian Inheritance in Man database, but complex diseases are more difficult. Association studies have found many individual genetic regions that individually are weakly associated with complex diseases (such as infertility, breast cancer and Alzheimer's disease ), rather than a single cause. There are currently many challenges to using genes for diagnosis and treatment, such as how we don't know which genes are important, or how stable the choices an algorithm provides.

Genome-wide association studies have successfully identified thousands of common genetic variants for complex diseases and traits; however, these common variants only explain a small fraction of heritability. Rare variants may account for some of the missing heritability. Large-scale whole genome sequencing studies have rapidly sequenced millions of whole genomes, and such studies have identified hundreds of millions of rare variants. Functional annotations predict the effect or function of a genetic variant and help to prioritize rare functional variants, and incorporating these annotations can effectively boost the power of genetic association of rare variants analysis of whole genome sequencing studies. Some tools have been developed to provide all-in-one rare variant association analysis for whole-genome sequencing data, including integration of genotype data and their functional annotations, association analysis, result summary and visualization. Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes.

Analysis of mutations in cancer
In cancer, the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to single-nucleotide polymorphism arrays identifying point mutations that cause cancer, oligonucleotide microarrays can be used to identify chromosomal gains and losses (called comparative genomic hybridization). These detection methods generate terabytes of data per experiment. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.

Two important principles can be used to identify cancer by mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.

Further improvements in bioinformatics could allow for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples. Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

Analysis of gene expression
The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization. All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

Analysis of protein expression
Protein microarrays and high throughput (HT) mass spectrometry (MS) can provide a snapshot of the proteins present in a biological sample. The former approach faces similar problems as with microarrays targeted at mRNA, the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples when multiple incomplete peptides from each protein are detected. Cellular protein localization in a tissue context can be achieved through affinity proteomics displayed as spatial data based on immunohistochemistry and tissue microarrays.

Analysis of regulation
Gene regulation is a complex process where a signal, such as an extracellular signal such as a hormone, eventually leads to an increase or decrease in the activity of one or more proteins. Bioinformatics techniques have been applied to explore various steps in this process.

For example, gene expression can be regulated by nearby elements in the genome. Promoter analysis involves the identification and study of sequence motifs in the DNA surrounding the protein-coding region of a gene. These motifs influence the extent to which that region is transcribed into mRNA. Enhancer elements far away from the promoter can also regulate gene expression, through three-dimensional looping interactions. These interactions can be determined by bioinformatic analysis of chromosome conformation capture experiments.

Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved in each state. In a single-cell organism, one might compare stages of the cell cycle, along with various stress conditions (heat shock, starvation, etc.). Clustering algorithms can be then applied to expression data to determine which genes are co-expressed. For example, the upstream regions (promoters) of co-expressed genes can be searched for over-represented regulatory elements. Examples of clustering algorithms applied in gene clustering are k-means clustering, self-organizing maps (SOMs), hierarchical clustering, and consensus clustering methods.

Analysis of cellular organization
Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. A gene ontology category, cellular component, has been devised to capture subcellular localization in many biological databases.

Microscopy and image analysis
Microscopic pictures allow for the location of organelles as well as molecules, which may be the source of abnormalities in diseases.

Protein localization
Finding the location of proteins allows us to predict what they do. This is called protein function prediction. For instance, if a protein is found in the nucleus it may be involved in gene regulation or splicing. By contrast, if a protein is found in mitochondria, it may be involved in respiration or other metabolic processes. There are well developed protein subcellular localization prediction resources available, including protein subcellular location databases, and prediction tools.

Nuclear organization of chromatin
Data from high-throughput chromosome conformation capture experiments, such as Hi-C (experiment) and ChIA-PET, can provide information on the three-dimensional structure and nuclear organization of chromatin. Bioinformatic challenges in this field include partitioning the genome into domains, such as Topologically Associating Domains (TADs), that are organised together in three-dimensional space.

Structural bioinformatics

Finding the structure of proteins is an important application of bioinformatics. The Critical Assessment of Protein Structure Prediction (CASP) is an open competition where worldwide research groups submit protein models for evaluating unknown protein models.

Amino acid sequence
The linear amino acid sequence of a protein is called the primary structure. The primary structure can be easily determined from the sequence of codons on the DNA gene that codes for it. In most proteins, the primary structure uniquely determines the 3-dimensional structure of a protein in its native environment. An exception is the misfolded protein involved in bovine spongiform encephalopathy. This structure is linked to the function of the protein. Additional structural information includes the secondary, tertiary and quaternary structure. A viable general solution to the prediction of the function of a protein remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.

Homology
In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. Homology modeling is used to predict the structure of an unknown protein from existing homologous proteins.

One example of this is hemoglobin in humans and the hemoglobin in legumes (leghemoglobin), which are distant relatives from the same protein superfamily. Both serve the same purpose of transporting oxygen in the organism. Although both of these proteins have completely different amino acid sequences, their protein structures are virtually identical, which reflects their near identical purposes and shared ancestor.

Other techniques for predicting protein structure include protein threading and de novo (from scratch) physics-based modeling.

Another aspect of structural bioinformatics include the use of protein structures for Virtual Screening models such as Quantitative Structure-Activity Relationship models and proteochemometric models (PCM). Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and in silico mutagenesis studies.

A 2021 deep-learning algorithms-based software called AlphaFold, developed by Google's DeepMind, greatly outperforms all other prediction software methods, and has released predicted structures for hundreds of millions of proteins in the AlphaFold protein structure database.

Network and systems biology
Network analysis seeks to understand the relationships within biological networks such as metabolic or protein–protein interaction networks. Although biological networks can be constructed from a single type of molecule or entity (such as genes), network biology often attempts to integrate many different data types, such as proteins, small molecules, gene expression data, and others, which are all connected physically, functionally, or both.

Systems biology involves the use of computer simulations of cellular subsystems (such as the networks of metabolites and enzymes that comprise metabolism, signal transduction pathways and gene regulatory networks) to both analyze and visualize the complex connections of these cellular processes. Artificial life or virtual evolution attempts to understand evolutionary processes via the computer simulation of simple (artificial) life forms.

Molecular interaction networks

Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography and protein nuclear magnetic resonance spectroscopy (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing protein–protein interaction experiments. A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field.

Other interactions encountered in the field include Protein–ligand (including drug) and protein–peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computational algorithms, termed docking algorithms, for studying molecular interactions.

Biodiversity informatics
Biodiversity informatics deals with the collection and analysis of biodiversity data, such as taxonomic databases, or microbiome data. Examples of such analyses include phylogenetics, niche modelling, species richness mapping, DNA barcoding, or species identification tools. A growing area is also macro-ecology, i.e. the study of how biodiversity is connected to ecology and human impact, such as climate change.

Literature analysis
The enormous number of published literature makes it virtually impossible for individuals to read every paper, resulting in disjointed sub-fields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. For example:
 * Abbreviation recognition – identify the long-form and abbreviation of biological terms
 * Named-entity recognition – recognizing biological terms such as gene names
 * Protein–protein interaction – identify which proteins interact with which proteins from text

The area of research draws from statistics and computational linguistics.

High-throughput image analysis
Computational technologies are used to automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems can improve an observer's accuracy, objectivity, or speed. Image analysis is important for both diagnostics and research. Some examples are:
 * high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics)
 * morphometrics
 * clinical image analysis and visualization
 * determining the real-time air-flow patterns in breathing lungs of living animals
 * quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury
 * making behavioral observations from extended video recordings of laboratory animals
 * infrared measurements for metabolic activity determination
 * inferring clone overlaps in DNA mapping, e.g. the Sulston score

High-throughput single cell data analysis
Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.

Ontologies and data integration
Biological ontologies are directed acyclic graphs of controlled vocabularies. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.

The OBO Foundry was an effort to standardise certain ontologies. One of the most widespread is the Gene ontology which describes gene function. There are also ontologies which describe phenotypes.

Databases
Databases are essential for bioinformatics research and applications. Databases exist for many different information types, including DNA and protein sequences, molecular structures, phenotypes and biodiversity. Databases can contain both empirical data (obtained directly from experiments) and predicted data (obtained from analysis of existing data). They may be specific to a particular organism, pathway or molecule of interest. Alternatively, they can incorporate data compiled from multiple other databases. Databases can have different formats, access mechanisms, and be public or private.

Some of the most commonly used databases are listed below:
 * Used in biological sequence analysis: Genbank, UniProt
 * Used in structure analysis: Protein Data Bank (PDB)
 * Used in finding Protein Families and Motif Finding: InterPro, Pfam
 * Used for Next Generation Sequencing: Sequence Read Archive
 * Used in Network Analysis: Metabolic Pathway Databases (KEGG, BioCyc), Interaction Analysis Databases, Functional Networks
 * Used in design of synthetic genetic circuits: GenoCAD

Software and tools
Software tools for bioinformatics include simple command-line tools, more complex graphical programs, and standalone web-services. They are made by bioinformatics companies or by public institutions.

Open-source bioinformatics software
Many free and open-source software tools have existed and continued to grow since the 1980s. The combination of a continued need for new algorithms for the analysis of emerging types of biological readouts, the potential for innovative in silico experiments, and freely available open code bases have created opportunities for research groups to contribute to both bioinformatics regardless of funding. The open source tools often act as incubators of ideas, or community-supported plug-ins in commercial applications. They may also provide de facto standards and shared object models for assisting with the challenge of bioinformation integration.

Open-source bioinformatics software includes Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS, .NET Bio, Orange with its bioinformatics add-on, Apache Taverna, UGENE and GenoCAD.

The non-profit Open Bioinformatics Foundation and the annual Bioinformatics Open Source Conference promote open-source bioinformatics software.

Web services in bioinformatics
SOAP- and REST-based interfaces have been developed to allow client computers to use algorithms, data and computing resources from servers in other parts of the world. The main advantage are that end users do not have to deal with software and database maintenance overheads.

Basic bioinformatics services are classified by the EBI into three categories: SSS (Sequence Search Services), MSA (Multiple Sequence Alignment), and BSA (Biological Sequence Analysis). The availability of these service-oriented bioinformatics resources demonstrate the applicability of web-based bioinformatics solutions, and range from a collection of standalone tools with a common data format under a single web-based interface, to integrative, distributed and extensible bioinformatics workflow management systems.

Bioinformatics workflow management systems
A bioinformatics workflow management system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or a workflow, in a Bioinformatics application. Such systems are designed to
 * provide an easy-to-use environment for individual application scientists themselves to create their own workflows,
 * provide interactive tools for the scientists enabling them to execute their workflows and view their results in real-time,
 * simplify the process of sharing and reusing workflows between the scientists, and
 * enable scientists to track the provenance of the workflow execution results and the workflow creation steps.

Some of the platforms giving this service: Galaxy, Kepler, Taverna, UGENE, Anduril, HIVE.

BioCompute and BioCompute Objects
In 2014, the US Food and Drug Administration sponsored a conference held at the National Institutes of Health Bethesda Campus to discuss reproducibility in bioinformatics. Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm. These stakeholders included representatives from government, industry, and academic entities. Session leaders represented numerous branches of the FDA and NIH Institutes and Centers, non-profit entities including the Human Variome Project and the European Federation for Medical Informatics, and research institutions including Stanford, the New York Genome Center, and the George Washington University.

It was decided that the BioCompute paradigm would be in the form of digital 'lab notebooks' which allow for the reproducibility, replication, review, and reuse, of bioinformatics protocols. This was proposed to enable greater continuity within a research group over the course of normal personnel flux while furthering the exchange of ideas between groups. The US FDA funded this work so that information on pipelines would be more transparent and accessible to their regulatory staff.

In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for a BioCompute Object, an instance of the BioCompute paradigm. This work was copied as both a "standard trial use" document and a preprint paper uploaded to bioRxiv. The BioCompute object allows for the JSON-ized record to be shared among employees, collaborators, and regulators.

Education platforms
Bioinformatics is not only taught as in-person masters degree at many universities. The computational nature of bioinformatics lends it to computer-aided and online learning. Software platforms designed to teach bioinformatics concepts and methods include Rosalind and online courses offered through the Swiss Institute of Bioinformatics Training Portal. The Canadian Bioinformatics Workshops provides videos and slides from training workshops on their website under a Creative Commons license. The 4273π project or 4273pi project also offers open source educational materials for free. The course runs on low cost Raspberry Pi computers and has been used to teach adults and school pupils. 4273 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4273π operating system.

MOOC platforms also provide online certifications in bioinformatics and related disciplines, including Coursera's Bioinformatics Specialization at the University of California, San Diego, Genomic Data Science Specialization at Johns Hopkins University, and EdX's Data Analysis for Life Sciences XSeries at Harvard University.

Conferences
There are several large conferences that are concerned with bioinformatics. Some of the most notable examples are Intelligent Systems for Molecular Biology (ISMB), European Conference on Computational Biology (ECCB), and Research in Computational Molecular Biology (RECOMB).

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Brian De Palma

Brian Russell De Palma (born September 11, 1940) is an American film director and screenwriter. With a career spanning over 50 years, he is best known for work in the suspense, crime and psychological thriller genres. His films include mainstream box office hits such as Carrie (1976), Dressed to Kill (1980), Scarface (1983), The Untouchables (1987), and Mission: Impossible (1996), as well as cult favorites such as Sisters (1972), Phantom of the Paradise (1974), Blow Out (1981), Body Double (1984), Casualties of War (1989), and Carlito's Way (1993).

De Palma was a leading member of the New Hollywood generation of film directors. His direction often makes use of quotations from other films or cinematic styles, and bears the influence of filmmakers such as Alfred Hitchcock, Jean-Luc Godard, and Michelangelo Antonioni. His work has been criticized for its violence and sexual content but has also been championed by American critics such as Roger Ebert and Pauline Kael.

Early life and education
De Palma was born on September 11, 1940, in Newark, New Jersey, the youngest of three boys. His Italian-American parents were Vivienne DePalma (née Muti), and Anthony F. DePalma, an orthopedic surgeon who was the son of immigrants from Alberona, Province of Foggia. He was raised in Philadelphia, Pennsylvania, and New Hampshire, and attended various Protestant and Quaker schools, eventually graduating from Friends' Central School. He had a poor relationship with his father, and would secretly follow him to record his adulterous behavior; this would eventually inspire the teenage character played by Keith Gordon in De Palma's 1980 film Dressed to Kill. When he was in high school, he built computers. He won a regional science-fair prize for a project titled "An Analog Computer to Solve Differential Equations".

Enrolled at Columbia University as a physics student, De Palma became enraptured with the filmmaking process after viewing Citizen Kane and Vertigo. After receiving his undergraduate degree in 1962, De Palma enrolled at the newly coed Sarah Lawrence College as a graduate student in their theater department, earning an M.A. in the discipline in 1964 and becoming one of the first male students among a female population. Once there, influences as various as drama teacher Wilford Leach, the Maysles brothers, Michelangelo Antonioni, Jean-Luc Godard, Andy Warhol, and Alfred Hitchcock impressed upon De Palma the many styles and themes that would shape his own cinema in the coming decades.

1963–1976: Rise to prominence
An early association with a young Robert De Niro resulted in The Wedding Party. The film, which was co-directed with Leach and producer Cynthia Munroe, had been shot in 1963 but remained unreleased until 1969, when De Palma's star had risen sufficiently within the Greenwich Village filmmaking scene. De Niro was unknown at the time; the credits mistakenly display his name as "Robert Denero [sic]". The film is noteworthy for its invocation of silent film techniques and an insistence on the jump-cut for effect. De Palma followed this style with various small films for the NAACP and the Treasury Department.

During the 1960s, De Palma began making a living producing documentary films, notably The Responsive Eye, a 1966 movie about The Responsive Eye op-art exhibit curated by William Seitz for MOMA in 1965. In an interview with Joseph Gelmis from 1969, De Palma described the film as "very good and very successful. It's distributed by Pathe Contemporary and makes lots of money. I shot it in four hours, with synched sound. I had two other guys shooting people's reactions to the paintings, and the paintings themselves."

Dionysus in '69 (1969) was De Palma's other major documentary from this period. The film records the Performance Group's performance of Euripides' The Bacchae, starring, amongst others, De Palma regular William Finley. The play is noted for breaking traditional barriers between performers and audience. The film's most striking quality is its extensive use of the split-screen. De Palma recalls that he was "floored" by this performance upon first sight, and in 1973 recounts how he "began to try and figure out a way to capture it on film. I came up with the idea of split-screen, to be able to show the actual audience involvement, to trace the life of the audience and that of the play as they merge in and out of each other."

De Palma's most significant features from this decade are Greetings (1968) and Hi, Mom! (1970). Both films star Robert De Niro and espouse a leftist revolutionary viewpoint common to the era in which they were released. Greetings was entered into the 19th Berlin International Film Festival, where it won a Silver Bear award. His other major film from this period is the slasher comedy Murder a la Mod. Each of these films experiments with narrative and intertextuality, reflecting De Palma's stated intention to become the "American Godard" while integrating several of the themes which permeated Hitchcock's work.

In 1970, De Palma left New York for Hollywood at age thirty to make Get to Know Your Rabbit (1972), starring Orson Welles and Tommy Smothers. Making the film was a crushing experience for De Palma, as Smothers did not like many of De Palma's ideas. Here he made several small, studio and independently-released films that included stand-outs Sisters (1972), Phantom of the Paradise (1974), and Obsession (1976).

1976–1979: Breakthrough
In November 1976, De Palma released a film adaptation of the 1974 novel Carrie by Stephen King. Though some see the psychic thriller as De Palma's bid for a blockbuster, the project was in fact small, underfunded by United Artists, and well under the cultural radar during the early months of production, as the source novel had yet to climb the bestseller list. De Palma gravitated toward the project and changed crucial plot elements based upon his own predilections, not the saleability of the novel. The cast was mostly young and relatively new, though Sissy Spacek and John Travolta had gained attention for previous work in, respectively, film and episodic sitcoms. Carrie became De Palma's first genuine box-office success, garnering Spacek and Piper Laurie Oscar nominations for their performances. Pre-production for the film had coincided with the casting process for George Lucas's Star Wars, and many of the actors cast in De Palma's film had been earmarked as contenders for Lucas's movie, and vice versa. The "shock ending" finale is effective even while it upholds horror-film convention, its suspense sequences are buttressed by teen comedy tropes, and its use of split-screen, split-diopter and slow motion shots tell the story visually rather than through dialogue. As for Lucas's project, De Palma complained in an early viewing of Star Wars that the opening text crawl was poorly written and volunteered to help edit the text to a more concise and engaging form.

The financial and critical success of Carrie allowed De Palma to pursue more personal material. The Demolished Man was a novel that had fascinated De Palma since the late 1950s and appealed to his background in mathematics and avant-garde storytelling. Its unconventional unfolding of plot (exemplified in its mathematical layout of dialogue) and its stress on perception have analogs in De Palma's filmmaking. He sought to adapt it numerous times, though the project would carry a substantial price tag, and has yet to appear on-screen (Steven Spielberg's 2002 adaptation of Philip K. Dick's Minority Report bears striking similarities to De Palma's visual style and some of the themes of The Demolished Man). The result of his experience with adapting The Demolished Man was the 1978 science fiction psychic thriller film The Fury, starring Kirk Douglas, Carrie Snodgress, John Cassavetes and Amy Irving. The film was admired by Jean-Luc Godard, who featured a clip in his mammoth Histoire(s) du cinéma, and Pauline Kael, who championed both The Fury and De Palma. The film boasted a larger budget than Carrie, though the consensus view at the time was that De Palma was repeating himself, with diminishing returns. As a film, it retains De Palma's considerable visual flair, but points more toward his work in mainstream entertainments such as Mission: Impossible, the thematic complex thriller for which he is now better known.

1980–1996: Established career
The 1980s were marked by some of De Palma's best known films including the erotic psychological thriller Dressed to Kill (1980) starring Michael Caine, and Angie Dickinson. Although the film received critical admiration, it also received criticism and controversy for its negative depiction of the transgender community. The following year he directed the neo-noir mystery thriller Blow Out (1981) starring John Travolta, Nancy Allen, and John Lithgow. The film received critical acclaim. The New Yorker film critic Pauline Kael praised the director writing, "De Palma has sprung to the place that Robert Altman achieved with films such as McCabe & Mrs. Miller and Nashville and that Francis Ford Coppola reached with The Godfather films—that is, to the place where genre is transcended and what we're moved by is an artist's vision...it's a great movie. Travolta and Allen are radiant performers". De Palma then directed crime film Scarface (1983) starring Al Pacino and Michelle Pfeiffer with a screenplay by Oliver Stone. The film received mixed reviews with its negative depictions of ethnic stereotypes, as well as its violence and profanity. It has since been re-evaluated and became a cult classic. The following year he made another neo-noir erotic thriller, Body Double (1984), starring Craig Wasson and Melanie Griffith. The film also received mixed reviews but has since had a reassessment and found acclaim. De Palma directed the music video for Bruce Springsteen's single "Dancing in the Dark" the same year. In 1987, De Palma directed the crime film The Untouchables loosely based on the book of the same name, adapted by David Mamet. The film stars Kevin Costner, Andy Garcia, Robert De Niro, and Sean Connery, the latter of whom won the Academy Award for Best Supporting Actor for the film. It received critical acclaim and box-office success. De Palma's Vietnam War film Casualties of War (1989) won critical praise but performed poorly in theatres and The Bonfire of the Vanities (1990) was a notorious failure with both critics and audiences. De Palma then had subsequent successes with Raising Cain (1992) and Carlito's Way (1993) with Mission: Impossible (1996) becoming his highest grossing film and starting a successful franchise.

1998–present: Career slump
De Palma's work after Mission: Impossible has been less well received. His ensuing films Snake Eyes (1998), Mission to Mars (2000), and Femme Fatale (2002) all failed at the box office and received generally poor reviews, though Femme Fatale has since been revived in the eyes of many film critics and became a cult classic. His 2006 adaptation of The Black Dahlia was also unsuccessful and is currently the last movie De Palma has directed with backing from Hollywood.

A political controversy erupted over the portrayal of US soldiers in De Palma's 2007 film Redacted. Loosely based on the 2006 Mahmudiyah killings by American soldiers in Iraq, the film echoes themes that appeared in Casualties of War. Redacted received a limited release in the United States and grossed less than $1 million against a $5 million budget.

De Palma's output has slowed since the release of Redacted, with subsequent projects often falling into development hell, due mostly to creative differences. In 2012, his film Passion starring Rachel McAdams and Noomi Rapace was selected to compete for the Golden Lion at the 69th Venice International Film Festival but received mixed reviews and was financially unsuccessful.

De Palma's next project was the thriller Domino (2019), released two years after the film began production. It received generally negative reviews and was released direct-to-VOD in the United States, grossing less than half a million dollars internationally. De Palma has also expressed dissatisfaction with both the production of the film and the final result; "I never experienced such a horrible movie set."

In 2018, De Palma published his debut novel in France, Les serpents sont-ils nécessaires? (English translation: Are Snakes Necessary?), co-written with Susan Lehman. It was published in the U.S. in 2020. De Palma and Lehman also wrote a second book, currently unpublished, called Terry, based on one of De Palma's passion projects about a French film production making an adaptation of Thérèse Raquin.

Trademarks and style
De Palma's films can fall into two categories, his thriller films (Sisters, Body Double, Obsession, Dressed to Kill, Blow Out, Raising Cain) and his mainly commercial films (Scarface, The Untouchables, Carlito's Way, and Mission: Impossible). He has often produced "De Palma" films one after the other before going on to direct a different genre, but would always return to his familiar territory. Because of the subject matter and graphic violence of some of De Palma's films, such as Dressed to Kill, Scarface and Body Double, they are often at the center of controversy with the Motion Picture Association of America, film critics and the viewing public.

Inspirations
De Palma frequently quotes and references other directors' work. His early work was inspired by the films of Jean-Luc Godard. Michelangelo Antonioni's Blowup and Francis Ford Coppola's The Conversation plots were used for the basis of Blow Out. The Untouchables' finale shoot out in the train station is a clear borrowing from the Odessa Steps sequence in Sergei Eisenstein's The Battleship Potemkin. The main plot from Rear Window was used for Body Double, while it also used elements of Vertigo. Vertigo was also the basis for Obsession. Dressed to Kill was a note-for-note homage to Hitchcock's Psycho, including such moments as the surprise death of the lead actress and the exposition scene by the psychiatrist at the end.

Camera shots
Film critics have often noted De Palma's penchant for unusual camera angles and compositions. He often frames characters against the background using a canted angle shot. Split-screen techniques have been used to show two separate events happening simultaneously. To emphasize the dramatic impact of a certain scene De Palma has employed a 360-degree camera pan. Slow sweeping, panning and tracking shots are often used throughout his films, often through precisely-choreographed long takes lasting for minutes without cutting. Split focus shots, often referred to as "di-opt", are used by De Palma to emphasize the foreground person/object while simultaneously keeping a background person/object in focus. Slow-motion is frequently used in his films to increase suspense.

Personal life
De Palma has been married and divorced three times, to actress Nancy Allen (1979–1983), producer Gale Anne Hurd (1991–1993), and Darnell Gregorio (1995–1997). He has one daughter from his marriage to Hurd, and one daughter from his marriage to Gregorio. He resides in Manhattan, New York.

Reception and legacy
De Palma is often cited as a leading member of the New Hollywood generation of film directors, a distinct pedigree who either emerged from film schools or are overtly cine-literate. His contemporaries include Martin Scorsese, Paul Schrader, John Milius, George Lucas, Francis Ford Coppola, Steven Spielberg, John Carpenter, and Ridley Scott. His artistry in directing and use of cinematography and suspense in several of his films has often been compared to the work of Alfred Hitchcock. Psychologists have been intrigued by De Palma's fascination with pathology, by the aberrant behavior aroused in characters who find themselves manipulated by others.

De Palma has encouraged and fostered the filmmaking careers of directors such as Mark Romanek and Keith Gordon, the latter of whom collaborated with him twice as an actor, both in 1979's Home Movies and 1980's Dressed to Kill. Filmmakers influenced by De Palma include Terrence Malick, Quentin Tarantino, Ronny Yu, Don Mancini, Nacho Vigalondo, and Jack Thomas Smith. During an interview with De Palma, Quentin Tarantino said that Blow Out is one of his all-time favorite films, and that after watching Scarface he knew how to make his own film. John Travolta's performance as Jack Terry in Blow Out even resulted in Tarantino casting him as Vincent Vega in his 1994 film Pulp Fiction, which would go on to reinvigorate Travolta's then-declining career. Tarantino also placed Carrie at number eight in a list of his favorite films.

Critics who frequently admire De Palma's work include Pauline Kael and Roger Ebert. Kael wrote in her review of Blow Out, "At forty, Brian De Palma has more than twenty years of moviemaking behind him, and he has been growing better and better. Each time a new film of his opens, everything he has done before seems to have been preparation for it." In his review of Femme Fatale, Roger Ebert wrote about the director: "De Palma deserves more honor as a director. Consider also these titles: Sisters, Blow Out, The Fury, Dressed to Kill, Carrie, Scarface, Wise Guys, Casualties of War, Carlito's Way, Mission: Impossible. Yes, there are a few failures along the way (Snake Eyes, Mission to Mars, The Bonfire of the Vanities), but look at the range here, and reflect that these movies contain treasure for those who admire the craft as well as the story, who sense the glee with which De Palma manipulates images and characters for the simple joy of being good at it. It's not just that he sometimes works in the style of Hitchcock, but that he has the nerve to."

The influential French film magazine Cahiers du Cinéma has placed five of De Palma's films (Carlito's Way, Mission: Impossible, Snake Eyes, Mission to Mars, and Redacted) on their annual top ten list, with Redacted placing first on the 2008 list. The magazine also listed Carlito's Way as the greatest film of the 1990s.

Julie Salamon has written that critics have accused De Palma of being "a perverse misogynist", to which De Palma has responded with, "I'm always attacked for having an erotic, sexist approach – chopping up women, putting women in peril. I'm making suspense movies! What else is going to happen to them?"

His films have also been interpreted as feminist and examined for their perceived queer affinities. In Film Comment "Queer and Now and Then" column on Femme Fatale, film critic Michael Koresky writes that "De Palma's films radiate an undeniable queer energy" and notes the "intense appeal" De Palma's films have for gay critics. In her book The Erotic Thriller in Contemporary Cinema, Linda Ruth Williams writes that "De Palma understood the cinematic potency of dangerous fucking, perhaps earlier than his feminist detractors".

Robin Wood considered Sisters an overtly feminist film, writing that "one can define the monster of Sisters as women's liberation; adding only that the film follows the time-honored horror film tradition of making the monster emerge as the most sympathetic character and its emotional center." Pauline Kael's review of Casualties of War, "A Wounded Apparition", describes the film as "feminist" and notes that "De Palma was always involved in examining (and sometimes satirizing) victimization, but he was often accused of being a victimizer". Helen Grace, in a piece for Lola, writes that upon seeing Dressed to Kill amidst calls for a boycott from feminist groups Women Against Violence Against Women and Women Against Pornography, that the film "seemed to say more about masculine anxiety than about the fears that women were expressing in relation to the film".

David Thomson wrote in his entry for De Palma, "There is a self-conscious cunning in De Palma's work, ready to control everything except his own cruelty and indifference." Matt Zoller Seitz objected to this characterisation, writing that there are films from the director which can be seen as "straightforwardly empathetic and/or moralistic".

His life and career in his own words was the subject of the 2015 documentary De Palma, directed by Noah Baumbach and Jake Paltrow.

title: Brian De Palma