Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see§ Sequence analysis for further information.Map of the human X chromosome (from theNational Center for Biotechnology Information (NCBI) website)
Computational, statistical, and computer programming techniques have been used forcomputer simulation analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field ofgenomics, such as by the identification ofgenes and singlenucleotide polymorphisms (SNPs). These pipelines are used to better understand the genetic basis of disease, unique adaptations, desirable properties (especially in agricultural species), or differences between populations. Bioinformatics also includesproteomics, which tries to understand the organizational principles withinnucleic acid andprotein sequences.[1]
Image andsignal 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 observedmutations. Bioinformatics includestext mining of biological literature and the development of biological and geneontologies 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 ofsystems biology. Instructural biology, it aids in the simulation and modeling of DNA,[2] RNA,[2][3] proteins[4] as well as biomolecular interactions.[5][6][7][8]
The first definition of the termbioinformatics was coined byPaulien Hogeweg andBen Hesper in 1970, to refer to the study of information processes in biotic systems.[9][10][11][12][13] This definition placed bioinformatics as a field parallel tobiochemistry (the study of chemical processes in biological systems).[10]
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 theHuman Genome Project and by rapid advances in DNA sequencing technology.[citation needed]
Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually.These are sequences being compared in a MUSCLE multiple sequence alignment (MSA). Each sequence name (leftmost column) is from various louse species, while the sequences themselves are in the second column.
There has been a tremendous advance in speed and cost reduction since the completion of the Human Genome Project, with some labs able tosequence over 100,000 billion bases each year, and a full genome can be sequenced for $1,000 or less.[14]
Computers became essential in molecular biology whenprotein sequences became available afterFrederick Sanger determined the sequence ofinsulin in the early 1950s.[15][16] Comparing multiple sequences manually turned out to be impractical.Margaret Oakley Dayhoff, a pioneer in the field,[17] compiled one of the first protein sequence databases, initially published as books[18] as well as methods of sequence alignment andmolecular evolution.[19] Another early contributor to bioinformatics wasElvin 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.[20]
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.[21][22]
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[when?], 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 andamino acid sequences,protein domains, andprotein structures.[23]
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 agene within a sequence, to predict protein structure and/or function, and tocluster protein sequences into families of related sequences.
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 ininformation 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 analyzingDNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.
Since the bacteriophagePhage Φ-X174 wassequenced in 1977,[24] theDNA sequences of thousands of organisms have been decoded and stored in databases. This sequence information is analyzed to determine genes that encodeproteins, RNA genes, regulatory sequences, structural motifs, and repetitive sequences. A comparison of genes within aspecies or between different species can show similarities between protein functions, or relations between species (the use ofmolecular systematics to constructphylogenetic trees). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually.Computer programs such asBLAST are used routinely to search sequences—as of 2008, from more than 260,000 organisms, containing over 190 billionnucleotides.[25]
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 forbase calling for the various experimental approaches to DNA sequencing.
Most DNA sequencing techniques produce short fragments of sequence that need to be assembled to obtain complete gene or genome sequences. Theshotgun sequencing technique (used byThe Institute for Genomic Research (TIGR) to sequence the first bacterial genome,Haemophilus influenzae)[26] 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 thehuman 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.
Ingenomics,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 ofsequencing exceeds the rate of genome annotation, genome annotation has become the new bottleneck in bioinformatics.[when?]
Genome annotation can be classified into three levels: thenucleotide, protein, and process levels.
Gene finding is a chief aspect of nucleotide-level annotation. For complex genomes, a combination ofab 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 theprotein 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.[27]
The first description of a comprehensive annotation system was published in 1995[26] byThe Institute for Genomic Research, which performed the first complete sequencing and analysis of the genome of a free-living (non-symbiotic) organism, the bacteriumHaemophilus influenzae.[26] The system identifies the genes encoding all proteins, transfer RNAs, ribosomal RNAs, in order to make initial functional assignments. TheGeneMark program trained to find protein-coding genes inHaemophilus influenzae is constantly changing and improving.
Following the goals that the Human Genome Project left to achieve after its closure in 2003, theENCODE project was developed by theNational 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).
While genome annotation is primarily based on sequence similarity (and thushomology), other properties of sequences can be used to predict the function of genes. In fact, mostgene function prediction methods focus onprotein sequences as they are more informative and more feature-rich. For instance, the distribution of hydrophobicamino acids predictstransmembrane segments in proteins. However, protein function prediction can also use external information such as gene (or protein)expression data,protein structure, orprotein-protein interactions.[28]
Evolutionary biology is the study of the origin and descent ofspecies, as well as their change over time.Informatics has assisted evolutionary biologists by enabling researchers to:
trace the evolution of a large number of organisms by measuring changes in theirDNA, rather than through physical taxonomy or physiological observations alone,
The core of comparative genome analysis is the establishment of the correspondence betweengenes (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.[30] Entire genomes are involved in processes of hybridization, polyploidization andendosymbiosis 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 andapproximation algorithms for problems based on parsimony models toMarkov chain Monte Carlo algorithms forBayesian analysis of problems based on probabilistic models.
Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particularmonophyletic 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.[32]
As of 2013, the existence of efficient high-throughput next-generation sequencing technology allows for the identification of cause many different human disorders. SimpleMendelian inheritance has been observed for over 3,000 disorders that have been identified at theOnline 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 asinfertility,[33]breast cancer[34] andAlzheimer's disease[35]), rather than a single cause.[36][37] 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.[38]
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.[39]Rare variants may account for some of themissing heritability.[40] Large-scalewhole genome sequencing studies have rapidly sequenced millions of whole genomes, and such studies have identified hundreds of millions ofrare variants.[41]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.[42] 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.[43][44] 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.[45]
Two important principles can be used to identify cancer by mutations in theexome. First, cancer is a disease of accumulated somatic mutations in genes. Second, cancer contains driver mutations which need to be distinguished from passengers.[46]
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 oflesions found to be recurrent among many tumors.[47]
Theexpression of many genes can be determined by measuringmRNA levels with multiple techniques includingmicroarrays,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 separatesignal fromnoise in high-throughput gene expression studies.[48] Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerousepithelial 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.
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 affinityproteomics displayed as spatial data based onimmunohistochemistry andtissue microarrays.[49]
Gene regulation is a complex process where a signal, such as an extracellular signal such as ahormone, eventually leads to an increase or decrease in the activity of one or moreproteins. 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 ofsequence 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 ofchromosome conformation capture experiments.
Expression data can be used to infer gene regulation: one might comparemicroarray 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 thecell 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-representedregulatory elements. Examples of clustering algorithms applied in gene clustering arek-means clustering,self-organizing maps (SOMs),hierarchical clustering, andconsensus clustering methods.
Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells. Agene ontology category,cellular component, has been devised to capture subcellular localization in manybiological databases.
3-dimensional protein structures such as this one are common subjects in bioinformatic analyses.
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.[53][54]
The linearamino acid sequence of a protein is called theprimary structure. The primary structure can be easily determined from the sequence ofcodons 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 themisfolded protein involved inbovine spongiform encephalopathy. This structure is linked to the function of the protein. Additional structural information includes thesecondary,tertiary andquaternary 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.[citation needed]
In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of geneA, whose function is known, is homologous to the sequence of geneB, 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 sameprotein 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.[55]
Other techniques for predicting protein structure include protein threading andde novo (from scratch) physics-based modeling.
Another aspect of structural bioinformatics include the use of protein structures forVirtual Screening models such asQuantitative 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 andin silico mutagenesis studies.
A 2021deep-learning algorithms-based software calledAlphaFold, developed by Google'sDeepMind, greatly outperforms all other prediction software methods,[56][how?] and has released predicted structures for hundreds of millions of proteins in the AlphaFold protein structure database.[57]
Network analysis seeks to understand the relationships withinbiological networks such asmetabolic orprotein–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.
Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions fromTreponema pallidum, the causative agent ofsyphilis and other diseases.[58]
Tens of thousands of three-dimensional protein structures have been determined byX-ray crystallography andprotein 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 performingprotein–protein interaction experiments. A variety of methods have been developed to tackle theprotein–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) andprotein–peptide. Molecular dynamic simulation of movement of atoms about rotatable bonds is the fundamental principle behind computationalalgorithms, termed docking algorithms, for studyingmolecular interactions.
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
Computational technologies are used to automate the processing, quantification and analysis of large amounts of high-information-contentbiomedical imagery. Modernimage analysis systems can improve an observer'saccuracy,objectivity, or speed. Image analysis is important for bothdiagnostics and research. Some examples are:
Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained fromflow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.
Biological ontologies aredirected acyclic graphs ofcontrolled 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.[citation needed]
TheOBO Foundry was an effort to standardise certain ontologies. One of the most widespread is theGene ontology which describes gene function. There are also ontologies which describe phenotypes.
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:
Manyfree and open-source software tools have existed and continued to grow since the 1980s.[59] The combination of a continued need for newalgorithms for the analysis of emerging types of biological readouts, the potential for innovativein silico experiments, and freely availableopen code bases have created opportunities for research groups to contribute to both bioinformatics regardless offunding. The open source tools often act as incubators of ideas, or community-supportedplug-ins in commercial applications. They may also providede facto standards and shared object models for assisting with the challenge of bioinformation integration.
SOAP- andREST-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 theEBI into three categories:SSS (Sequence Search Services),MSA (Multiple Sequence Alignment), andBSA (Biological Sequence Analysis).[61] The availability of theseservice-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 extensiblebioinformatics workflow management systems.
Abioinformatics workflow management system is a specialized form of aworkflow 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 theprovenance of the workflow execution results and the workflow creation steps.
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.[64]
In 2016, the group reconvened at the NIH in Bethesda and discussed the potential for aBioCompute 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.[65][66]
Bioinformatics is not only taught as in-personmaster's degree at many universities. The computational nature of bioinformatics lends it tocomputer-aided and online learning.[67][68] Software platforms designed to teach bioinformatics concepts and methods includeRosalind and online courses offered through theSwiss Institute of Bioinformatics Training Portal. TheCanadian Bioinformatics Workshops provides videos and slides from training workshops on their website under aCreative Commons license. The 4273π project or 4273pi project[69] also offers open source educational materials for free. The course runs on low costRaspberry Pi computers and has been used to teach adults and school pupils.[70][71] 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.[72][73]
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