Просмотр кода страницы Английская Википедия:Bioinformatics

{{Английская Википедия/Панель перехода}}
{{Short description|Computational analysis of large, complex sets of biological data}}
{{For|the journal|Bioinformatics (journal)}}
{{Use dmy dates|date=September 2020}}
[[File:WPP domain alignment.PNG|500px|thumbnail|right|Early bioinformatics—computational alignment of experimentally determined sequences of a class of related proteins; see {{Section link||Sequence analysis}} for further information.]]
[[Image:Genome viewer screenshot small.png|thumbnail|220px|Map of the human X chromosome (from the [[National Center for Biotechnology Information]] (NCBI) website)]]{{Not to be confused with|Biological computation|Genetic algorithm}}
'''Bioinformatics''' ({{IPAc-en|audio=en-us-bioinformatics.ogg|ˌ|b|aɪ|.|oʊ|ˌ|ɪ|n|f|ɚ|ˈ|m|æ|t|ɪ|k|s}}) is an [[interdisciplinary]] field of [[science]] that develops methods and [[Bioinformatics software|software tool]]s for understanding [[biology|biological]] data, especially when the data sets are large and complex. Bioinformatics uses [[biology]], [[chemistry]], [[physics]], [[computer science]], [[computer programming]], [[Information engineering (field)|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 [[In silico|computer simulation]] analyses of biological queries. They include reused specific analysis "pipelines", particularly in the field of [[genomics]], such as by the identification of [[gene]]s and single [[nucleotide]] polymorphisms ([[Single-nucleotide polymorphism|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.<ref>{{cite web |vauthors=Lesk AM |date=26 July 2013 |title=Bioinformatics |url=https://www.britannica.com/science/bioinformatics |website=Encyclopaedia Britannica |access-date=17 April 2017 |archive-date=14 April 2021 |archive-url=https://web.archive.org/web/20210414103621/https://www.britannica.com/science/bioinformatics |url-status=live }}</ref>

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 [[mutation]]s. Bioinformatics includes [[text mining]] of biological literature and the development of biological and gene [[Ontology (information science)|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,<ref name=":0">{{cite journal | vauthors = Sim AY, Minary P, Levitt M | title = Modeling nucleic acids | journal = Current Opinion in Structural Biology | volume = 22 | issue = 3 | pages = 273–8 | date = June 2012 | pmid = 22538125 | pmc = 4028509 | doi = 10.1016/j.sbi.2012.03.012 }}</ref> RNA,<ref name=":0" /><ref>{{cite journal | vauthors = Dawson WK, Maciejczyk M, Jankowska EJ, Bujnicki JM | title = Coarse-grained modeling of RNA 3D structure | journal = Methods | volume = 103 | pages = 138–56 | date = July 2016 | pmid = 27125734 | doi = 10.1016/j.ymeth.2016.04.026 | doi-access = free }}</ref> proteins<ref>{{cite journal | vauthors = Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A | title = Coarse-Grained Protein Models and Their Applications | journal = Chemical Reviews | volume = 116 | issue = 14 | pages = 7898–936 | date = July 2016 | pmid = 27333362 | doi = 10.1021/acs.chemrev.6b00163 | doi-access = free }}</ref> as well as biomolecular interactions.<ref>{{cite book | vauthors = Wong KC |year=2016 |title=Computational Biology and Bioinformatics: Gene Regulation |publisher=CRC Press/Taylor & Francis Group |isbn=9781498724975 }}</ref><ref>{{cite journal | vauthors = Joyce AP, Zhang C, Bradley P, Havranek JJ | title = Structure-based modeling of protein: DNA specificity | journal = Briefings in Functional Genomics | volume = 14 | issue = 1 | pages = 39–49 | date = January 2015 | pmid = 25414269 | pmc = 4366589 | doi = 10.1093/bfgp/elu044 | doi-access = free }}</ref><ref>{{Cite book | vauthors = Spiga E, Degiacomi MT, Dal Peraro M |date=2014 |chapter=New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly | veditors = Karabencheva-Christova T |title=Biomolecular Modelling and Simulations |series=Advances in Protein Chemistry and Structural Biology |volume=96 |pages=77–111 |publisher=Academic Press |doi=10.1016/bs.apcsb.2014.06.008 |pmid=25443955 |isbn=9780128000137 }}</ref><ref>{{cite journal | vauthors = Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S | title = Protein-peptide docking: opportunities and challenges | journal = Drug Discovery Today | volume = 23 | issue = 8 | pages = 1530–1537 | date = August 2018 | pmid = 29733895 | doi = 10.1016/j.drudis.2018.05.006 | doi-access = free }}</ref>

== 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.<ref>{{cite journal |last1=Ouzounis |first1=C. A. |last2=Valencia |first2=A. |date=2003 |title=Early bioinformatics: the birth of a discipline—a personal view |journal=Bioinformatics |volume=19 |issue=17 |pages=2176–2190 | pmid=14630646 | doi=10.1093/bioinformatics/btg309| doi-access=free}}</ref><ref name="Hogeweg2011">{{cite journal | vauthors = Hogeweg P | title = The roots of bioinformatics in theoretical biology | journal = PLOS Computational Biology | volume = 7 | issue = 3 | pages = e1002021 | date = March 2011 | pmid = 21483479 | pmc = 3068925 | doi = 10.1371/journal.pcbi.1002021 | veditors = Searls DB | bibcode = 2011PLSCB...7E2021H | doi-access = free }}</ref><ref>{{Cite journal| vauthors = Hesper B, Hogeweg P |year=1970|title=Bio-informatica: een werkconcept|journal=Kameleon|volume=1 |issue=6|pages=28–29}}</ref><ref>{{cite arXiv | vauthors = Hesper B, Hogeweg P |eprint=2111.11832v1 |title=Bio-informatics: a working concept. A translation of "Bio-informatica: een werkconcept" by B. Hesper and P. Hogeweg |date=2021 |class=q-bio.OT}}</ref><ref>{{cite journal | vauthors = Hogeweg P |title=Simulating the growth of cellular forms |journal=Simulation |volume=31 |issue=3 |pages=90–96 |year=1978 |doi=10.1177/003754977803100305 |s2cid=61206099 }}</ref> This definition placed bioinformatics as a field parallel to [[biochemistry]] (the study of chemical processes in biological systems).<ref name="Hogeweg2011" />

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 [[algorithm]]s 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 ===
[[File: Example DNA sequence.png|thumbnail|right|Sequences of genetic material are frequently used in bioinformatics and are easier to manage using computers than manually.]]

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.<ref>{{cite web | vauthors = Colby B | date = 2022 | work = Sequencing.com | title = Whole Genome Sequencing Cost | url = https://sequencing.com/education-center/whole-genome-sequencing/whole-genome-sequencing-cost | access-date = 8 April 2022 | archive-date = 15 March 2022 | archive-url = https://web.archive.org/web/20220315025036/https://sequencing.com/education-center/whole-genome-sequencing/whole-genome-sequencing-cost | url-status = live }}</ref> 

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,<ref>{{cite book | vauthors = Moody G |year=2004 |title=Digital Code of Life: How Bioinformatics is Revolutionizing Science, Medicine, and Business |publisher=John Wiley & Sons |isbn=978-0-471-32788-2 |url-access=registration |url=https://archive.org/details/digitalcodeoflif0000mood }}</ref> compiled one of the first protein sequence databases, initially published as books<ref>Dayhoff, M.O. (1966) Atlas of protein sequence and structure. National Biomedical Research Foundation, 215 pp.</ref> as well as methods of sequence alignment and [[molecular evolution]].<ref name="pmid17775169">{{cite journal | vauthors = Eck RV, Dayhoff MO | title = Evolution of the structure of ferredoxin based on living relics of primitive amino Acid sequences | journal = Science | volume = 152 | issue = 3720 | pages = 363–6 | date = April 1966 | pmid = 17775169 | doi = 10.1126/science.152.3720.363 | s2cid = 23208558 | bibcode = 1966Sci...152..363E }}</ref> 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.<ref>{{cite journal | vauthors = Johnson G, Wu TT | title = Kabat database and its applications: 30 years after the first variability plot | journal = Nucleic Acids Research | volume = 28 | issue = 1 | pages = 214–8 | date = January 2000 | pmid = 10592229 | pmc = 102431 | doi = 10.1093/nar/28.1.214 }}</ref>

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.<ref>{{cite journal | vauthors = Erickson JW, Altman GG |title=A Search for Patterns in the Nucleotide Sequence of the MS2 Genome |journal=Journal of Mathematical Biology |date=1979 |volume=7 |issue=3 |pages=219–230 |doi=10.1007/BF00275725 |s2cid=85199492 }}</ref><ref>{{cite journal | vauthors = Shulman MJ, Steinberg CM, Westmoreland N | title = The coding function of nucleotide sequences can be discerned by statistical analysis | journal = Journal of Theoretical Biology | volume = 88 | issue = 3 | pages = 409–20 | date = February 1981 | pmid = 6456380 | doi = 10.1016/0022-5193(81)90274-5 | bibcode = 1981JThBi..88..409S }}</ref>

[[File:Muscle alignment view.png|thumb|369x369px|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.]]

== 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{{When|date=June 2023}}, 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 sequence]]s, [[protein domain]]s, and [[protein structure]]s.<ref>{{Cite book|title=Essential Bioinformatics|url=https://archive.org/details/essentialbioinfo00xion|url-access=limited| vauthors = Xiong J |publisher=Cambridge University Press|year=2006|isbn=978-0-511-16815-4|location=Cambridge, United Kingdom|pages=[https://archive.org/details/essentialbioinfo00xion/page/n13 4]|via=Internet Archive}}</ref> 

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 analysis|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 [[Biological Data Visualization|visualization]]. Major research efforts in the field include [[sequence alignment]], [[gene finding]], [[genome assembly]], [[drug design]], [[drug discovery]], [[protein structural alignment|protein structure alignment]], [[protein structure prediction]], prediction of [[gene expression]] and [[protein–protein interaction]]s, [[genome-wide association studies]], the modeling of [[evolution]] and [[Cellular model|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==
{{main|Sequence alignment|Sequence database|Alignment-free sequence analysis}}
Since the bacteriophage [[Phi X 174|Phage Φ-X174]] was [[sequencing|sequenced]] in 1977,<ref name="pmid870828">{{cite journal | vauthors = Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes CA, Hutchison CA, Slocombe PM, Smith M | display-authors = 6 | title = Nucleotide sequence of bacteriophage phi X174 DNA | journal = Nature | volume = 265 | issue = 5596 | pages = 687–95 | date = February 1977 | pmid = 870828 | doi = 10.1038/265687a0 | s2cid = 4206886 | bibcode = 1977Natur.265..687S }}</ref> the [[DNA sequence]]s of thousands of organisms have been decoded and stored in databases. This sequence information is analyzed to determine genes that encode [[protein]]s, 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 tree]]s). With the growing amount of data, it long ago became impractical to analyze DNA sequences manually. [[Computer program]]s such as [[BLAST (biotechnology)|BLAST]] are used routinely to search sequences—as of 2008, from more than 260,000 organisms, containing over 190 billion [[nucleotide]]s.<ref name="pmid18073190">{{cite journal | vauthors = Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL | title = GenBank | journal = Nucleic Acids Research | volume = 36 | issue = Database issue | pages = D25-30 | date = January 2008 | pmid = 18073190 | pmc = 2238942 | doi = 10.1093/nar/gkm929 }}</ref>

[[File:Sequencing analysis steps.png|center|600px|Image: 450 pixels Sequencing analysis steps]]

===DNA sequencing===
{{main|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. [[Algorithm]]s have been developed for [[base calling]] for the various experimental approaches to DNA sequencing.

===Sequence assembly===
{{main|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]]'')<ref name="pmid7542800">{{cite journal | vauthors = Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM | display-authors = 6 | title = Whole-genome random sequencing and assembly of Haemophilus influenzae Rd | journal = Science | volume = 269 | issue = 5223 | pages = 496–512 | date = July 1995 | pmid = 7542800 | doi = 10.1126/science.7542800 | bibcode = 1995Sci...269..496F }}</ref> 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.

{{see also|sequence analysis|sequence mining|sequence profiling tool|sequence motif}}

===Genome annotation===
{{main|Gene prediction}}

In [[genomics]], [[Genome project#Genome annotation|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 [[DNA sequencing|sequencing]] exceeds the rate of genome annotation, genome annotation has become the new bottleneck in bioinformatics{{When|date=June 2023}}. 

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.<ref>{{cite journal |title=Genome annotation: from sequence to biology |journal=Nature |year=2001 |doi=10.1038/35080529|last1=Stein |first1=Lincoln |volume=2 |issue=7 |pages=493–503 |pmid=11433356 |s2cid=12044602 }}</ref>

The first description of a comprehensive annotation system was published in 1995<ref name="pmid7542800"/> by [[The Institute for Genomic Research]], which performed the first complete sequencing and analysis of the genome of a free-living (non-[[symbiosis|symbiotic]]) organism, the bacterium ''[[Haemophilus influenzae]]''.<ref name="pmid7542800"/> 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 (biology)|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 acid]]s predicts [[Transmembrane domain|transmembrane segments]] in proteins. However, protein function prediction can also use external information such as gene (or protein) [[Gene expression|expression]] data, [[protein structure]], or [[Protein–protein interaction|protein-protein interactions]].<ref>{{cite journal | vauthors = Erdin S, Lisewski AM, Lichtarge O | title = Protein function prediction: towards integration of similarity metrics | journal = Current Opinion in Structural Biology | volume = 21 | issue = 2 | pages = 180–8 | date = April 2011 | pmid = 21353529 | pmc = 3120633 | doi = 10.1016/j.sbi.2011.02.001 }}</ref>

===Computational evolutionary biology===
{{further|Computational phylogenetics}}

[[Evolutionary biology]] is the study of the origin and descent of [[species]], as well as their change over time. [[Informatics (academic field)|Informatics]] has assisted evolutionary biologists by enabling researchers to:
* 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<ref>{{cite journal | vauthors = Carvajal-Rodríguez A | title = Simulation of genes and genomes forward in time | journal = Current Genomics | volume = 11 | issue = 1 | pages = 58–61 | date = March 2010 | pmid = 20808525 | pmc = 2851118 | doi = 10.2174/138920210790218007 }}</ref>
* track and share information on an increasingly large number of species and organisms
Future work endeavours to reconstruct the now more complex [[Evolutionary tree|tree of life]].{{according to whom|date=June 2020}}

===Comparative genomics===
{{main|Comparative genomics}}

The core of comparative genome analysis is the establishment of the correspondence between [[genes]] ([[Homology (biology)#Orthology|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.<ref>{{cite book | vauthors = Brown TA |title=Genomes |date=2002 |publisher=Oxford |location=Manchester (UK) |edition=2nd |chapter=Mutation, Repair and Recombination}}</ref> 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 family|protein families]].<ref>{{cite journal | vauthors = Carter NP, Fiegler H, Piper J | title = Comparative analysis of comparative genomic hybridization microarray technologies: report of a workshop sponsored by the Wellcome Trust | journal = Cytometry | volume = 49 | issue = 2 | pages = 43–8 | date = October 2002 | pmid = 12357458 | doi = 10.1002/cyto.10153 }}</ref>

===Pan genomics===
{{main|Pan-genome}}

Pan genomics is a concept introduced in 2005 by Tettelin and Medini. Pan genome is the complete gene repertoire of a particular [[Monophyly|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.<ref>{{cite journal | vauthors = Chaudhari NM, Gupta VK, Dutta C | title = BPGA- an ultra-fast pan-genome analysis pipeline | journal = Scientific Reports | volume = 6 | pages = 24373 | date = April 2016 | pmid = 27071527 | pmc = 4829868 | doi = 10.1038/srep24373 | bibcode = 2016NatSR...624373C }}</ref>

===Genetics of disease===
{{main|Genome-wide association studies}}

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]],<ref name="Demerec1945">{{cite journal | vauthors = Aston KI | title = Genetic susceptibility to male infertility: news from genome-wide association studies | journal = Andrology | volume = 2 | issue = 3 | pages = 315–21 | date = May 2014 | pmid = 24574159 | doi = 10.1111/j.2047-2927.2014.00188.x | s2cid = 206007180 | doi-access = free }}</ref> [[breast cancer]]<ref name="Véron2013">{{cite journal | vauthors = Véron A, Blein S, Cox DG | title = Genome-wide association studies and the clinic: a focus on breast cancer | journal = Biomarkers in Medicine | volume = 8 | issue = 2 | pages = 287–96 | year = 2014 | pmid = 24521025 | doi = 10.2217/bmm.13.121 }}</ref> and [[Alzheimer's disease]]<ref name="Tosto2013">{{cite journal | vauthors = Tosto G, Reitz C | title = Genome-wide association studies in Alzheimer's disease: a review | journal = Current Neurology and Neuroscience Reports | volume = 13 | issue = 10 | pages = 381 | date = October 2013 | pmid = 23954969 | pmc = 3809844 | doi = 10.1007/s11910-013-0381-0 }}</ref>), rather than a single cause.<ref name="Londin2013">{{Cite book |vauthors=Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P |chapter=Use of linkage analysis, genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations |title=Pharmacogenomics |volume=1015 |pages=127–46 |year=2013 |pmid=23824853 |doi=10.1007/978-1-62703-435-7_8 |isbn=978-1-62703-434-0 |series=Methods in Molecular Biology }}</ref><ref>{{cite journal | vauthors = Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA | title = Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 23 | pages = 9362–7 | date = June 2009 | pmid = 19474294 | pmc = 2687147 | doi = 10.1073/pnas.0903103106 | doi-access = free | bibcode = 2009PNAS..106.9362H }}</ref> 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. <ref>{{Cite book | vauthors = Hall LO |title=2010 International Conference on System Science and Engineering |chapter=Finding the right genes for disease and prognosis prediction |date=2010 |pages=1–2 |doi=10.1109/ICSSE.2010.5551766|isbn=978-1-4244-6472-2 |s2cid=21622726 }}</ref>

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.<ref>{{cite journal |last1=Manolio |first1=Teri A. |last2=Collins |first2=Francis S. |last3=Cox |first3=Nancy J. |last4=Goldstein |first4=David B. |last5=Hindorff |first5=Lucia A. |last6=Hunter |first6=David J. |last7=McCarthy |first7=Mark I. |last8=Ramos |first8=Erin M. |last9=Cardon |first9=Lon R. |last10=Chakravarti |first10=Aravinda |last11=Cho |first11=Judy H. |last12=Guttmacher |first12=Alan E. |last13=Kong |first13=Augustine |last14=Kruglyak |first14=Leonid |last15=Mardis |first15=Elaine |last16=Rotimi |first16=Charles N. |last17=Slatkin |first17=Montgomery |last18=Valle |first18=David |last19=Whittemore |first19=Alice S. |last20=Boehnke |first20=Michael |last21=Clark |first21=Andrew G. |last22=Eichler |first22=Evan E. |last23=Gibson |first23=Greg |last24=Haines |first24=Jonathan L. |last25=Mackay |first25=Trudy F. C. |last26=McCarroll |first26=Steven A. |last27=Visscher |first27=Peter M. |display-authors=3|title=Finding the missing heritability of complex diseases |journal=Nature |date=October 2009 |volume=461 |issue=7265 |pages=747–753 |doi=10.1038/nature08494|pmid=19812666 |pmc=2831613 |bibcode=2009Natur.461..747M }}</ref> [[rare functional variant|Rare variants]] may account for some of the [[Missing heritability problem|missing heritability]].<ref>{{cite journal |last1=Wainschtein |first1=Pierrick |last2=Jain |first2=Deepti |last3=Zheng |first3=Zhili |last4=Aslibekyan |first4=Stella |last5=Becker |first5=Diane |last6=Bi |first6=Wenjian |last7=Brody |first7=Jennifer |last8=Carlson |first8=Jenna C. |last9=Correa |first9=Adolfo |last10=Du |first10=Margaret Mengmeng |last11=Fernandez-Rhodes |first11=Lindsay |last12=Ferrier |first12=Kendra R. |last13=Graff |first13=Misa |last14=Guo |first14=Xiuqing |last15=He |first15=Jiang |last16=Heard-Costa |first16=Nancy L. |last17=Highland |first17=Heather M. |last18=Hirschhorn |first18=Joel N. |last19=Howard-Claudio |first19=Candace M. |last20=Isasi |first20=Carmen R. |last21=Jackson |first21=Rebecca |last22=Jiang |first22=Jicai |last23=Joehanes |first23=Roby |last24=Justice |first24=Anne E. |last25=Kalyani |first25=Rita R. |last26=Kardia |first26=Sharon |last27=Lange |first27=Ethan |last28=LeBoff |first28=Meryl |last29=Lee |first29=Seunggeun |last30=Li |first30=Xihao |last31=Li |first31=Zilin |last32=Lim |first32=Elise |last33=Lin |first33=Danyu |last34=Lin |first34=Xihong |last35=Liu |first35=Simin |last36=Lu |first36=Yingchang |last37=Manson |first37=JoAnn |last38=Martin |first38=Lisa |last39=McHugh |first39=Caitlin |last40=Mikulla |first40=Julie |last41=Musani |first41=Solomon K. |last42=Ng |first42=Maggie |last43=Nickerson |first43=Deborah |last44=Palmer |first44=Nicholette |last45=Perry |first45=James |last46=Peters |first46=Ulrike |last47=Preuss |first47=Michael |last48=Qi |first48=Qibin |last49=Raffield |first49=Laura |last50=Rasmussen-Torvik |first50=Laura |last51=Reiner |first51=Alex |last52=Russell |first52=Emily M. |last53=Sitlani |first53=Colleen |last54=Smith |first54=Jennifer |last55=Spracklen |first55=Cassandra N. |last56=Wang |first56=Tao |last57=Wang |first57=Zhe |last58=Wessel |first58=Jennifer |last59=Xu |first59=Hanfei |last60=Yaser |first60=Mohammad |last61=Yoneyama |first61=Sachiko |last62=Young |first62=Kendra A. |last63=Zhang |first63=Jingwen |last64=Zhang |first64=Xinruo |last65=Zhou |first65=Hufeng |last66=Zhu |first66=Xiaofeng |last67=Zoellner |first67=Sebastian |last68=Abe |first68=Namiko |last69=Abecasis |first69=Gonçalo |last70=Aguet |first70=Francois |last71=Almasy |first71=Laura |last72=Alonso |first72=Alvaro |last73=Ament |first73=Seth |last74=Anderson |first74=Peter |last75=Anugu |first75=Pramod |last76=Applebaum-Bowden |first76=Deborah |last77=Ardlie |first77=Kristin |last78=Arking |first78=Dan |last79=Ashley-Koch |first79=Allison |last80=Assimes |first80=Tim |last81=Auer |first81=Paul |last82=Avramopoulos |first82=Dimitrios |last83=Ayas |first83=Najib |last84=Balasubramanian |first84=Adithya |last85=Barnard |first85=John |last86=Barnes |first86=Kathleen |last87=Barr |first87=R. 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|last58=Rotter |first58=Jerome I. |last59=Natarajan |first59=Pradeep |last60=Peloso |first60=Gina M. |last61=Li |first61=Zilin |last62=Lin |first62=Xihong |title=Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies |journal=Nature Genetics |date=January 2023 |volume=55 |issue=1 |pages=154–164 |doi=10.1038/s41588-022-01225-6|pmid=36564505 |pmc=10084891 |s2cid=255084231 }}</ref>

===Analysis of mutations in cancer===
{{main|Oncogenomics}}

In [[cancer]], the genomes of affected cells are rearranged in complex or unpredictable ways. In addition to [[single-nucleotide polymorphism]] arrays identifying [[point mutation]]s that cause cancer, [[oligonucleotide]] microarrays can be used to identify chromosomal gains and losses (called [[comparative genomic hybridization]]). These detection methods generate [[terabyte]]s 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 variation|copy number]] changes.{{Citation needed|date=June 2023}}

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.<ref>{{cite journal | vauthors = Vazquez M, de la Torre V, Valencia A | title = Chapter 14: Cancer genome analysis | journal = PLOS Computational Biology | volume = 8 | issue = 12 | pages = e1002824 | date = 2012-12-27 | pmid = 23300415 | pmc = 3531315 | doi = 10.1371/journal.pcbi.1002824 | bibcode = 2012PLSCB...8E2824V | doi-access = free }}</ref>

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 [[lesion]]s found to be recurrent among many tumors.<ref>{{cite book | vauthors = Hye-Jung EC, Jaswinder K, Martin K, Samuel AA, Marco AM | veditors = Dellaire G, Berman JN, Arceci RJ | title= Cancer Genomics |date=2014 |publisher=Academic Press |location=Boston (US) |isbn=9780123969675 |pages=13–30 |chapter=Second-Generation Sequencing for Cancer Genome Analysis |doi=10.1016/B978-0-12-396967-5.00002-5}}</ref>

==Gene and protein expression==

===Analysis of gene expression===
The [[gene expression|expression]] of many genes can be determined by measuring [[Messenger RNA|mRNA]] levels with multiple techniques including [[DNA microarray|microarrays]], [[expressed sequence tag|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 (information theory)|signal]] from [[noise]] in high-throughput gene expression studies.<ref>{{cite journal | vauthors = Grau J, Ben-Gal I, Posch S, Grosse I | title = VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees | journal = Nucleic Acids Research | volume = 34 | issue = Web Server issue | pages = W529-33 | date = July 2006 | pmid = 16845064 | pmc = 1538886 | doi = 10.1093/nar/gkl212 }}</ref> 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.

[[File:MIcroarray vs RNA-Seq.png|thumb|center|400px|MIcroarray vs RNA-Seq]]

===Analysis of protein expression===
[[Protein microarray]]s 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 microarray]]s.<ref>{{Cite web |url=https://www.proteinatlas.org/ |title=The Human Protein Atlas |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=4 March 2020 |archive-url=https://web.archive.org/web/20200304041657/http://www.proteinatlas.org/ |url-status=live }}</ref>

===Analysis of regulation===
[[Regulation of gene expression|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 [[protein]]s. 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 motif]]s 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 (genetics)|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.). [[cluster analysis|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 map]]s (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 database]]s.

===Microscopy and image analysis===
Microscopic pictures allow for the location of [[organelle]]s 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 [[Cell nucleus|nucleus]] it may be involved in [[Regulation of gene expression|gene regulation]] or [[RNA splicing|splicing]]. By contrast, if a protein is found in [[Mitochondrion|mitochondria]], it may be involved in [[Cellular respiration|respiration]] or other [[Metabolism|metabolic processes]]. There are well developed [[protein subcellular localization prediction]] resources available, including protein subcellular location databases, and prediction tools.<ref>{{Cite web |url=https://www.proteinatlas.org/humancell |title=The human cell |website=www.proteinatlas.org |access-date=2017-10-02 |archive-date=2 October 2017 |archive-url=https://web.archive.org/web/20171002215345/https://www.proteinatlas.org/humancell |url-status=live }}</ref><ref>{{cite journal | vauthors = Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E | display-authors = 6 | title = A subcellular map of the human proteome | journal = Science | volume = 356 | issue = 6340 | pages = eaal3321 | date = May 2017 | pmid = 28495876 | doi = 10.1126/science.aal3321 | s2cid = 10744558 }}</ref>

===Nuclear organization of chromatin===
{{main|Nuclear organization}}
Data from high-throughput [[chromosome conformation capture]] experiments, such as [[Hi-C (genomic analysis technique)|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 Domain]]s (TADs), that are organised together in three-dimensional space.<ref>{{cite journal | vauthors = Ay F, Noble WS | title = Analysis methods for studying the 3D architecture of the genome | journal = Genome Biology | volume = 16 | issue = 1 | pages = 183 | date = September 2015 | pmid = 26328929 | pmc = 4556012 | doi = 10.1186/s13059-015-0745-7 | doi-access = free }}</ref>

==Structural bioinformatics==<!-- this is specific enough, compared to general knowledge in previous paragraph, that it would be nice to have a source or example -->
{{main|Structural bioinformatics|Protein structure prediction}}
{{See also|Structural motif|Structural domain}}
[[File:1kqf opm.png|thumbnail|left|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.<ref>{{Cite journal|title=Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII|year=2019 |pmc=6927249 |last1=Kryshtafovych |first1=A. |last2=Schwede |first2=T. |last3=Topf |first3=M. |last4=Fidelis |first4=K. |last5=Moult |first5=J. |journal=Proteins |volume=87 |issue=12 |pages=1011–1020 |doi=10.1002/prot.25823 |pmid=31589781 }}</ref><ref>{{Cite web |title=Home - CASP14 |url=https://predictioncenter.org/casp14/ |access-date=2023-06-12 |website=predictioncenter.org |archive-date=30 January 2023 |archive-url=https://web.archive.org/web/20230130200222/https://predictioncenter.org/casp14/ |url-status=live }}</ref>

=== 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 [[Prion|misfolded protein]] involved in [[bovine spongiform encephalopathy]]. This structure is linked to the function of the protein. Additional structural information includes the ''[[secondary structure|secondary]]'', ''[[tertiary structure|tertiary]]'' and ''[[quaternary structure|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.{{citation needed|date=July 2015}}

=== 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.<ref>{{cite journal | vauthors = Hoy JA, Robinson H, Trent JT, Kakar S, Smagghe BJ, Hargrove MS | title = Plant hemoglobins: a molecular fossil record for the evolution of oxygen transport | journal = Journal of Molecular Biology | volume = 371 | issue = 1 | pages = 168–79 | date = August 2007 | pmid = 17560601 | doi = 10.1016/j.jmb.2007.05.029 }}</ref>

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|Virtual Screening]] models such as [[QSAR|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<ref>{{Cite journal |last1=Jumper |first1=John |last2=Evans |first2=Richard |last3=Pritzel |first3=Alexander |last4=Green |first4=Tim |last5=Figurnov |first5=Michael |last6=Ronneberger |first6=Olaf |last7=Tunyasuvunakool |first7=Kathryn |last8=Bates |first8=Russ |last9=Žídek |first9=Augustin |last10=Potapenko |first10=Anna |last11=Bridgland |first11=Alex |last12=Meyer |first12=Clemens |last13=Kohl |first13=Simon A. A. |last14=Ballard |first14=Andrew J. |last15=Cowie |first15=Andrew |date=August 2021 |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |language=en |volume=596 |issue=7873 |pages=583–589 |bibcode=2021Natur.596..583J |doi=10.1038/s41586-021-03819-2 |issn=1476-4687 |pmc=8371605 |pmid=34265844}}</ref>{{How|date=June 2023}}, and has released predicted structures for hundreds of millions of proteins in the AlphaFold protein structure database.<ref>{{Cite web |title=AlphaFold Protein Structure Database |url=https://alphafold.ebi.ac.uk/ |access-date=2022-10-10 |website=alphafold.ebi.ac.uk |archive-date=24 July 2021 |archive-url=https://web.archive.org/web/20210724013505/https://alphafold.ebi.ac.uk/ |url-status=live }}</ref>

==Network and systems biology==
{{main|Computational systems biology|Biological network|Interactome}}

''Network analysis'' seeks to understand the relationships within [[biological network]]s such as [[Metabolic network|metabolic]] or [[Interactome|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 simulation]]s of [[cell (biology)|cellular]] subsystems (such as the [[metabolic network|networks of metabolites]] and [[enzyme]]s that comprise [[metabolism]], [[signal transduction]] pathways and [[gene regulatory network]]s) 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===
[[File:The protein interaction network of Treponema pallidum.png|200px|thumbnail|right|Interactions between proteins are frequently visualized and analyzed using networks. This network is made up of protein–protein interactions from ''[[Treponema pallidum]]'', the causative agent of [[syphilis]] and other diseases.<ref>{{cite journal | vauthors = Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P | title = The binary protein interactome of Treponema pallidum--the syphilis spirochete | journal = PLOS ONE | volume = 3 | issue = 5 | pages = e2292 | date = May 2008 | pmid = 18509523 | pmc = 2386257 | doi = 10.1371/journal.pone.0002292 | bibcode = 2008PLoSO...3.2292T | veditors = Hall N | doi-access = free }}</ref>]]
{{main|Protein–protein interaction prediction|interactome}}

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 [[algorithm]]s, termed docking algorithms, for studying [[interactome|molecular interactions]].
==Biodiversity informatics==
{{main|Biodiversity informatics}}
Biodiversity informatics deals with the collection and analysis of [[biodiversity]] data, such as [[taxonomic database]]s, or [[microbiome]] data. Examples of such analyses include [[phylogenetics]], [[niche modelling]], [[species richness]] mapping, [[DNA barcoding]], or [[Speciesism|species]] identification tools. A growing area is also [[Macroecology|macro-ecology]], i.e. the study of how biodiversity is connected to [[ecology]] and human impact, such as [[climate change]].
==Others==


===Literature analysis===
{{main|Text mining|Biomedical text mining}}

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 [[protein]]s 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 [[medical imaging|biomedical imagery]]. Modern [[image analysis]] systems can improve an observer's [[accuracy]], [[Objectivity (science)|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 [[Gene mapping|DNA mapping]], e.g. the [[Sulston score]]

===High-throughput single cell data analysis===
{{main|Flow cytometry bioinformatics}}
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 graph]]s of [[controlled vocabulary|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.{{Citation needed|date=June 2023}}

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==
{{main|List of biological databases|Biological database}}
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 [[Sequence motif|Motif]] Finding: [[InterPro]], [[Pfam]]
* Used for Next Generation Sequencing: [[Sequence Read Archive]]
* Used in Network Analysis: Metabolic Pathway Databases ([[KEGG]], [[BioCyc database collection|BioCyc]]), Interaction Analysis Databases, Functional Networks
* Used in design of synthetic genetic circuits: [[GenoCAD]]
{{Citation needed|date=June 2023}}

==Software and tools==
[[List of bioinformatics software|Software tools for bioinformatics]] include simple command-line tools, more complex graphical programs, and standalone web-services. They are made by [[List of bioinformatics companies|bioinformatics companies]] or by public institutions.

===Open-source bioinformatics software===
{{Main articles|List of bioinformatics software}}
Many [[free and open-source software]] tools have existed and continued to grow since the 1980s.<ref name="obf-main">{{cite web |title=Open Bioinformatics Foundation: About us |url=http://www.open-bio.org/wiki/Main_Page |website=Official website |publisher=[[Open Bioinformatics Foundation]] |access-date=10 May 2011 |archive-date=12 May 2011 |archive-url=https://web.archive.org/web/20110512022059/http://open-bio.org/wiki/Main_Page |url-status=live }}</ref> The combination of a continued need for new [[algorithm]]s 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 of science|funding]]. The open source tools often act as incubators of ideas, or community-supported [[Plug-in (computing)|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 (software)|Orange]] with its bioinformatics add-on, [[Apache Taverna]], [[UGENE]] and [[GenoCAD]]. 

The non-profit [[Open Bioinformatics Foundation]]<ref name="obf-main" /> and the annual [[Bioinformatics Open Source Conference]] promote open-source bioinformatics software.<ref name="obf-bosc">{{cite web |title=Open Bioinformatics Foundation: BOSC |url=http://www.open-bio.org/wiki/BOSC |website=Official website |publisher=[[Open Bioinformatics Foundation]] |access-date=10 May 2011 |archive-date=18 July 2011 |archive-url=https://web.archive.org/web/20110718175922/http://www.open-bio.org/wiki/BOSC |url-status=live }}</ref>

===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 [[European Bioinformatics Institute|EBI]] into three categories: [[Sequence alignment software|SSS]] (Sequence Search Services), [[Multiple sequence alignment|MSA]] (Multiple Sequence Alignment), and [[#Sequence analysis|BSA]] (Biological Sequence Analysis).<ref>{{Cite book | vauthors = Nisbet R, Elder IV J, Miner G |title=Handbook of Statistical Analysis and Data Mining Applications |chapter-url=https://books.google.com/books?id=U5np34a5fmQC&q=bioinformatics%20service%20categories%20EBI&pg=PA328|publisher=Academic Press |year=2009 |page=328 |chapter=Bioinformatics |isbn=978-0080912035 }}</ref> The availability of these [[Service-orientation|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 ====
{{main|Bioinformatics workflow management systems}}

A [[Bioinformatics workflow management systems|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 (computational biology)|Galaxy]], [[Kepler scientific workflow system|Kepler]], [[Apache Taverna|Taverna]], [[UGENE]], [[Anduril (workflow engine)|Anduril]], [[High-performance Integrated Virtual Environment|HIVE]].
<!-- This is like saying there are different kinds of websites, some of which are convert-PNG-to-PDF.com, and others which are Wikipedia. Plus a lot of jargon. Consider cutting down further.  -->

=== BioCompute and BioCompute Objects ===
In 2014, the [[Food and Drug Administration|US Food and Drug Administration]] sponsored a conference held at the [[National Institutes of Health]] Bethesda Campus to discuss reproducibility in bioinformatics.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|title=Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation Sequencing Standards|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=14 November 2017|archive-url=https://web.archive.org/web/20171114200347/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm389561.htm|url-status=live}}</ref> Over the next three years, a consortium of stakeholders met regularly to discuss what would become BioCompute paradigm.<ref>{{cite journal | vauthors = Simonyan V, Goecks J, Mazumder R | title = Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations | journal = PDA Journal of Pharmaceutical Science and Technology | volume = 71 | issue = 2 | pages = 136–146 | date = 2017 | pmid = 27974626 | pmc = 5510742 | doi = 10.5731/pdajpst.2016.006734 }}</ref> 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 University|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.<ref>{{Cite web|url=https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|title=Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and encouraging interoperability|author=Office of the Commissioner|website=www.fda.gov|language=en|access-date=2017-11-30|archive-date=26 January 2018|archive-url=https://web.archive.org/web/20180126133504/https://www.fda.gov/ScienceResearch/SpecialTopics/RegulatoryScience/ucm491893.htm|url-status=live}}</ref>

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.<ref>{{cite journal | vauthors = Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, Hayes A, Suresh A, Purkayastha A, King CH, Taylor D, Johanson E, Thompson EE, Donaldson E, Morizono H, Tsang H, Vora JK, Goecks J, Yao J, Almeida JS, Keeney J, Addepalli K, Krampis K, Smith KM, Guo L, Walderhaug M, Schito M, Ezewudo M, Guimera N, Walsh P, Kahsay R, Gottipati S, Rodwell TC, Bloom T, Lai Y, Simonyan V, Mazumder R | display-authors = 6 | title = Enabling precision medicine via standard communication of HTS provenance, analysis, and results | journal = PLOS Biology | volume = 16 | issue = 12 | pages = e3000099 | date = December 2018 | pmid = 30596645 | doi = 10.1371/journal.pbio.3000099 | pmc = 6338479 | doi-access = free }}</ref><ref>{{Citation|title=BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification Document: user manual for understanding and creating B.|date=2017-09-03|url=https://github.com/biocompute-objects/HTS-CSRS|publisher=biocompute-objects|access-date=30 November 2017|archive-date=27 June 2018|archive-url=https://web.archive.org/web/20180627081221/https://github.com/biocompute-objects/HTS-CSRS|url-status=live}}</ref>

==Education platforms==
Bioinformatics is not only taught as in-person [[Master's degree|masters degree]] at many universities. The computational nature of bioinformatics lends it to [[Educational technology|computer-aided and online learning]].<ref>{{Cite journal |last=Campbell |first=A. Malcolm |date=2003-06-01 |title=Public Access for Teaching Genomics, Proteomics, and Bioinformatics |journal=Cell Biology Education |volume=2 |issue=2 |pages=98–111 |doi=10.1187/cbe.03-02-0007 |pmc=162192 |pmid=12888845}}</ref><ref>{{Cite journal |last=Arenas |first=Miguel |date=September 2021 |title=General considerations for online teaching practices in bioinformatics in the time of COVID -19 |journal=Biochemistry and Molecular Biology Education |language=en |volume=49 |issue=5 |pages=683–684 |doi=10.1002/bmb.21558 |issn=1470-8175 |pmc=8426940 |pmid=34231941}}</ref> Software platforms designed to teach bioinformatics concepts and methods include [[Rosalind (education platform)|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<ref>{{cite journal | vauthors = Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, Smart SD | title = 4273π: bioinformatics education on low cost ARM hardware | journal = BMC Bioinformatics | volume = 13 | pages = 522 | date = August 2013 | pmid = 23937194 | pmc = 3751261 | doi = 10.1186/1471-2105-14-243 | doi-access = free }}</ref> 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.<ref>{{cite journal | vauthors = Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, Muirhead GT, Sweeney SD | display-authors = 6 |title=University-level practical activities in bioinformatics benefit voluntary groups of pupils in the last 2 years of school |journal=International Journal of STEM Education |date=2015 |volume=2 |issue=17 |doi=10.1186/s40594-015-0030-z | s2cid = 256396656 |hdl=10023/7704 |hdl-access=free | doi-access = free }}</ref><ref>{{cite journal | vauthors = McDonagh JL, Barker D, Alderson RG | title = Bringing computational science to the public | journal = SpringerPlus | volume = 5 | issue = 259 | pages = 259 | date = 2016 | pmid = 27006868 | pmc = 4775721 | doi = 10.1186/s40064-016-1856-7 | doi-access = free }}</ref> 4283 is actively developed by a consortium of academics and research staff who have run research level bioinformatics using Raspberry Pi computers and the 4283π operating system.<ref>{{cite journal | vauthors = Robson JF, Barker D | title = Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer | journal = BMC Research Notes | volume = 8 | issue = 561 | pages = 561 | date = October 2015 | pmid = 26462790 | pmc = 4604092 | doi = 10.1186/s13104-015-1476-2 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Wreggelsworth KM, Barker D | title = A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1 | journal = BMC Research Notes | volume = 8 | issue = 565 | pages = 565 | date = October 2015 | pmid = 26467441 | pmc = 4606965 | doi = 10.1186/s13104-015-1535-8 | doi-access = free }}</ref>

[[Massive open online course|MOOC]] platforms also provide online certifications in bioinformatics and related disciplines, including [[Coursera]]'s Bioinformatics Specialization ([[University of California, San Diego|UC San Diego]]) and Genomic Data Science Specialization ([[Johns Hopkins University|Johns Hopkins]]) as well as [[EdX]]'s Data Analysis for Life Sciences XSeries ([[Harvard University|Harvard]]).

==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).

== See also ==
{{Columns-list|colwidth=30em|
* [[Biodiversity informatics]]
* [[Bioinformatics companies]]
* [[Computational biology]]
* [[Computational biomodeling]]
* [[Computational genomics]]
* [[Cyberbiosecurity]]
* [[Functional genomics]]
* [[Health informatics]]
* [[International Society for Computational Biology]]
* [[Jumping library]]
* [[List of bioinformatics institutions]]
* [[List of open-source bioinformatics software]]
* [[List of bioinformatics journals]]
* [[Metabolomics]]
* [[Nucleic acid sequence]]
* [[Phylogenetics]]
* [[Proteomics]]
* [[Gene Disease Database]]
}}

== References ==
{{Reflist}}

== Further reading ==
<!-- It's possible that some of these were used as the original sources for the article. -->
{{refbegin|35em}}
* Sehgal et al. : Structural, phylogenetic and docking studies of D-amino acid oxidase activator(DAOA ), a candidate schizophrenia gene. Theoretical Biology and Medical Modelling 2013 10 :3.
* Achuthsankar S Nair [http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf Computational Biology & Bioinformatics – A gentle Overview] {{Webarchive|url=https://web.archive.org/web/20081216212125/http://print.achuth.googlepages.com/BINFTutorialV5.0CSI07.pdf |date=16 December 2008 }}, Communications of Computer Society of India, January 2007
* [[Srinivas Aluru|Aluru, Srinivas]], ed. ''Handbook of Computational Molecular Biology''. Chapman & Hall/Crc, 2006. {{ISBN|1-58488-406-1}} (Chapman & Hall/Crc Computer and Information Science Series)
* Baldi, P and Brunak, S, ''Bioinformatics: The Machine Learning Approach'', 2nd edition. MIT Press, 2001. {{ISBN|0-262-02506-X}}
* Barnes, M.R. and Gray, I.C., eds., ''Bioinformatics for Geneticists'', first edition. Wiley, 2003. {{ISBN|0-470-84394-2}}
* Baxevanis, A.D. and Ouellette, B.F.F., eds., ''Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins'', third edition. Wiley, 2005. {{ISBN|0-471-47878-4}}
* Baxevanis, A.D., Petsko, G.A., Stein, L.D., and Stormo, G.D., eds., ''[[Current Protocols]] in Bioinformatics''. Wiley, 2007. {{ISBN|0-471-25093-7}}
* Cristianini, N. and Hahn, M. [http://www.computational-genomics.net/ ''Introduction to Computational Genomics''] {{Webarchive|url=https://web.archive.org/web/20090104042023/http://www.computational-genomics.net/ |date=4 January 2009 }}, Cambridge University Press, 2006. ({{ISBN|9780521671910}} |{{ISBN|0-521-67191-4}})
* Durbin, R., S. Eddy, A. Krogh and G. Mitchison, ''Biological sequence analysis''. Cambridge University Press, 1998. {{ISBN|0-521-62971-3}}
* {{cite journal | vauthors = Gilbert D | title = Bioinformatics software resources | journal = Briefings in Bioinformatics | volume = 5 | issue = 3 | pages = 300–4 | date = September 2004 | pmid = 15383216 | doi = 10.1093/bib/5.3.300 | doi-access = free }}
* Keedwell, E., ''Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems''. Wiley, 2005. {{ISBN|0-470-02175-6}}
* Kohane, et al. ''Microarrays for an Integrative Genomics.'' The MIT Press, 2002. {{ISBN|0-262-11271-X}}
* Lund, O. et al. ''Immunological Bioinformatics.'' The MIT Press, 2005. {{ISBN|0-262-12280-4}}
* [[Lior Pachter|Pachter, Lior]] and [[Bernd Sturmfels|Sturmfels, Bernd]]. "Algebraic Statistics for Computational Biology" Cambridge University Press, 2005. {{ISBN|0-521-85700-7}}
* Pevzner, Pavel A. ''Computational Molecular Biology: An Algorithmic Approach'' The MIT Press, 2000. {{ISBN|0-262-16197-4}}
* Soinov, L. [http://jprr.org/index.php/jprr/article/view/8/5 Bioinformatics and Pattern Recognition Come Together] {{Webarchive|url=https://web.archive.org/web/20130510213503/http://jprr.org/index.php/jprr/article/view/8/5 |date=10 May 2013 }} Journal of Pattern Recognition Research ([http://www.jprr.org JPRR] {{Webarchive|url=https://web.archive.org/web/20080908110041/http://www.jprr.org/ |date=8 September 2008 }}), Vol 1 (1) 2006 p.&nbsp;37–41
* Stevens, Hallam, ''Life Out of Sequence: A Data-Driven History of Bioinformatics'', Chicago: The University of Chicago Press, 2013, {{ISBN|9780226080208}}
* Tisdall, James. "Beginning Perl for Bioinformatics" O'Reilly, 2001. {{ISBN|0-596-00080-4}}
* [http://www.nap.edu/catalog/11480.html Catalyzing Inquiry at the Interface of Computing and Biology (2005) CSTB report] {{Webarchive|url=https://web.archive.org/web/20070128222920/http://www.nap.edu/catalog/11480.html |date=28 January 2007 }}
* [http://www.nap.edu/catalog/2121.html Calculating the Secrets of Life: Contributions of the Mathematical Sciences and computing to Molecular Biology (1995)] {{Webarchive|url=https://web.archive.org/web/20080706035211/http://www.nap.edu/catalog/2121.html |date=6 July 2008 }}
* [https://web.archive.org/web/20071222091912/http://ocw.mit.edu/OcwWeb/Biology/7-91JSpring2004/LectureNotes/index.htm Foundations of Computational and Systems Biology MIT Course]
* [http://compbio.mit.edu/6.047/ Computational Biology: Genomes, Networks, Evolution Free MIT Course] {{Webarchive|url=https://web.archive.org/web/20130408034631/http://compbio.mit.edu/6.047/ |date=8 April 2013 }}
{{Refend}}

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